25 February 2020 - Sheraton Doha Hotel · we expect to have result oriented discussions for serving the goals of the conference ... and organized by Bait Al-Mashura finance consultations

6th Doha Islamic Finance Conference

25 February 2020 - Sheraton Doha Hotel

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Academic Partner

In the Name of Allah, the Most Gracious, the Most Merciful

Foreword

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In the Name of Allah the Most gracious the Most Merciful

Praise be to Allah the Lord of the worlds and may the blessings and peace of Allah be upon the most honored of messengers our prophet Muhammad, master of goodness and guide for the straight path and upon all his family and companions.

Human being, undoubtedly and without exception, undergoes the Universal laws of variation and changing by Allah, the Almighty, as the signs of this variation are mirrored and reflected on the objects around us. This variation, which comes to the human beings and inanimate objects, necessarily affects the life cycle, so it is touched by variation or changing, or even establishes a promising and new future that can withstand these big changes and variations that have touched and interfered in people’s lives in their finest detail. Thus, it affected their economic life and their wide and complex relations, and this transformation has become an inherent characteristic of their world.

The Islamic finance in this transforming world, which gravitates by the laws of change without prejudice to its origin and reality, makes it a continuous movement to maintain its path of deviation and transformation, in addition to a movement towards its goal to develop and invest wealth as the wealth’s owner wishes. The 6th Doha Islamic Financial Conference, scheduled on February 25, 2020, discusses an important topic, entitled “Future of Islamic Finance in this Changing and Transforming World”, after having discussed, at its 5th edition, an important aspect thereof, namely “The Digital Aspect and its Impact on Islamic Finance”. We also find that the world today is like moving sand driven by many factors, one of which is digitization. There are other reasons worthy of highlighting and drawing attention, thus they will predict new financial and economic crises that experts and economists have been warning of and calling for avoiding their causes and trying to minimize their expected effects.

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In this conference and through its four themes, we are attempting to highlight the indicators of potential global crisis and the extent of its impact on Islamic finance, as we also discuss the possibilities of the sports sectors out-reach to Islamic finance and the investment opportunities therein; we shall also review trading platforms in terms of their mechanism and relevant Shariah rules, in addition to foresee the future of artificial intelligence in this transforming world and the relevant investment opportunities and Shari’ah maxims.

We are honored in this conference to find the elite group of scholars, thinkers and field related professionals to enrich it by their scientific papers and proposals, and we expect to have result oriented discussions for serving the goals of the conference as well to entail recommendations augmenting the role of Islamic finance and its institution in the context of transforming world.

The conference is being held under the generous patronage of HE Sheikh Khalid Bin Khalifa Bin Abdul Aziz Al-Thani, prime minister and minister of interior, and organized by Bait Al-Mashura finance consultations along with the strategic partner “Barwa Bank” while the diamond sponsor is “Qatar Financial Centre” and silver sponsor is “Qatar Development Bank” & “Qatar Finance House”. The event is held with due academic partnership of College of Islamic Studies at Hamad Bin Khalifa University, College of Shari’ah and Islamic Studies at Qatar University and International Shari’ah Research Academy (ISRA) of Malaysia.

Hoping the accordance and success from Allah

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Conference Objectives

• To foresee financial and economic crises and their impact on Islamic finance.

• To direct the Islamic finance institutions towards the new sectors of finance and investment.

• To present the experiences of Islamic trading platforms and their impact on global economy.

• To showcase the importance of innovation and artificial intelligence in Islamic finance and investment opportunities in the light of maxims and standards of Shari’ah.

GLOBAL ECONOMIC OUTLOOKKey risks to the global economy and their potential

implications for economic growth, inflation, monetary and fiscal policy, and financial markets

Adrian CooperCEO and Chief Economist, Oxford Economics

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Outline of presentation

• Reasons behind global growth slowdown

• Estimating the probability of recession

• Potential triggers of a global recession

• Quantifying the impact

• Key results of alternative scenarios

Investment intentions have fallen sharply…

G-3: Investment indicators

Investment intentions have fallen sharply…

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2001 2004 2007 2010 2013 2016 2019

G-3* investment goods orders indicator (LHS)Survey-based indicator, advanced 3 months (RHS)

G-3: Investment indicators% year, 3mma

Source : Oxford Economics/Haver Analytics * US, Germany, Japan

Standardised indicator

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…but we are still a long way from a global recession

World: GDP & PMI

Impact of US China tariffs modest so far

China’s domestic slowdown has played a key role

…but we are still a long way from a global recession

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2001 2004 2007 2010 2013 2016 2019

Global composite PMI (Adv two months, LHS)Global GDP (RHS)

Index

Source : Oxford Economics/Haver Analytics/Markit

World: GDP & PMI% y/y

3Impact of US-China tariffs modest so far

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The “Trump boost” has faded


5The “Trump boost” has faded

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2016 2017 2018 2019 2020

Impact of BBA, 2018Impact of TCJA, 2017

Source: Oxford Economics

US: Policy tailwinds diminish over timeReal GDP growth, Q4/Q4

BBA, 2018 = Bipartisan Budget Act of 2018TCJA, 2017 = Tax Cuts and Jobs Act of 2017

+1.0ppt

+0.3ppt

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Germany hit hard by automotive weakness…

…and by Brexit

Germany hit hard by automotive weakness…

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2012 2014 2016 2018

Automotive Services

Goods ex. Auto. Goods & services

Germany: Exports

Source: Oxford Economics/Haver Analytics

Nominal, % & ppts contribution, y/y, 3M avg.

7…and by Brexit

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China EurozoneUK TurkeyUS

Germany: Goods exports

Source : Oxford Economics/Haver Analytics

Nominal, % y/y, 3M avg.

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Employment continues to rise despite uncertainties…

…making consumers remain resilient for now

Employment continues to rise despite uncertainties…

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2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Surveys of employment intentions* (LHS, adv. 6 months)Employment (RHS)

Advanced economies: Employment


Index % year

* Weighted average of US, Eurozone and UK surveys

9…making consumers remain resilient for now

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IndexConsumer confidence (Adv 3 months, LHS)Retail sales (RHS)

Adv econ: Retail sales & consumer sentiment


3m % year

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Low leverage & high savings represent reassuring buffers

Fed policy shift, accompanied by loosening elsewhere…

Low leverage & high savings represent reassuring buffers

11Fed policy shift, accompanied by loosening elsewhere…

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Dec. 2016 Dec. 2017 Dec. 2018 Dec. 2019 Dec. 2020 Dec. 2021

Fed's "dot plot" (Dec 2019)Oxford Economics (Jan 2020)Market Implied (Jan-7-2020)

%

Source: CME/Federal Reserve/Oxford Economics

US: Federal funds rate expectations

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…and some modest fiscal support, with room for more

Stagnation in prospect, rather than recessionStagnation in prospect, rather than recession

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2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021

World Advanced economies Emerging markets

Global: Real GDP forecast


% year

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…and some modest fiscal support, with room for more

Fiscal policy boosts growth

Fiscal policy detracts from growth

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An early warning framework

• A strong framework encompasses a number of different approaches and acknowledges the limitations in each.

• We approach tracking the economy and turning points in the economy in several ways:

• Real activity indicators

• Compiling timely indicators of activity into a single measure for each economy

• Monitoring leading indicators of recessions

• Following key measures that have a historical track record of predicting recessions

• Calculating recession probabilities

• Modelling the chances of recession over different time periods

Real Activity Indicators (RAI)

• To create our Real Activity Indicators, we apply principal component analysis (PCA) to obtain the first factor which captures the degree of common movement to give a single time series measure of activity.

• Our indicators contain 8-13 timely indicators (which are subject to no or small historical revisions) including a mix of:

• Primary and secondary sector indicators

• Manufacturing, metals production, cement production, core

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industries production

• Tertiary sector indicators

• Services and trade indicators

• Other activity indicators

• Electricity consumption, railway freight, car registrations

Real Activity Indicators can illustrate common trends

• Our RAIs are standardised indicators (i.e. mean zero, measures in standard deviations)

• The colour formatting in the tables provides a guide to the strength of the readings over the sample period in the table, as opposed to the full sample period.

Real activity indicators can illustrate common trends

• Our RAIs are standardised indicators (i.e. mean zero, measures in standard deviations)

• The colour formatting in the tables provides a guide to the strength of the readings over the sample period in the table, as opposed to the full sample period.

Dec-17 Mar-18 Jun-18 Sep-18 Dec-18 Mar-19 Jun-19 Sep-19 Oct-19 Nov-19China -0.14 0.06 0.28 -0.09 0.00 -0.06 -0.26 -0.21 -0.13 0.10India 0.97 0.14 1.51 0.15 -0.09 0.91 -0.66 -2.54 -2.48 -2.39Brazil 0.99 0.62 0.28 -0.09 0.01 -0.51 -0.19 0.27 0.04 -0.04Russia 0.00 0.93 0.12 -0.20 -0.64 -0.41 -0.54 -0.57 -0.45 -0.38Mexico 0.09 0.51 0.07 0.37 -0.64 -0.68 -0.47 -0.89 -0.93 -0.73Indonesia -0.46 -0.48 -1.28 -0.45 -0.14 0.34 -0.44 -0.48 -0.33 -0.44Turkey 1.11 0.73 0.33 -0.37 -1.27 -0.70 -1.10 -0.59 -0.22 -0.26Argentina -0.27 0.28 -0.81 -1.37 -1.31 -1.55 -0.88 -0.37 -0.35 -0.50Poland 0.37 0.64 1.18 0.72 0.66 0.40 -0.34 0.05 0.27 0.21Thailand 0.21 0.12 0.37 0.17 0.01 0.02 -0.34 -0.75 -0.69 -0.91Philippines 1.33 -0.99 -0.56 -0.56 -1.78 -0.45 -0.48 -0.86 -0.93 -0.93Malaysia 0.42 -0.65 1.29 0.05 0.13 -0.11 -0.90 0.12 -0.73 -0.73South Africa 0.50 1.31 0.60 0.31 -0.06 -1.27 -0.27 -1.44 -1.37 -1.37Colombia -0.91 -0.40 -0.08 -0.19 -0.16 -0.14 -0.71 -0.74 -0.61 -0.61Chile 0.20 0.70 1.00 0.02 0.12 -0.54 -1.04 -0.39 -1.29 -1.34Peru -0.82 0.28 0.26 -0.31 0.32 -0.11 -0.46 -0.13 -0.25 -0.29Hungary 0.91 0.57 1.31 0.97 0.88 1.23 0.36 0.90 0.73 0.82Source: Oxford Economics/Haver Analytics

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RAIs can also help clarify official data

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RAIs can also help clarify official data

There are a wide range of recession indicators

• Understanding recession signs requires examining a wide range of indicators and understanding their differing properties e.g. timeliness and proportion of false positives.

There are a wide range of recession indicators

• Understanding recession signs requires examining a wide range of indicators and understanding their differing properties e.g. timeliness and proportion of false positives

Quarters before recessiont-8 YCt-7t-6 ER ERt-5 YC YC YC, CM CSt-4 YC ER YC CSt-3 ST CM ST IP ST SPt-2 ER,IP SP CS ST IP STt-1 ER ST SP IPt CM SP CSt+1 IP IP CMt+2 SP CM, IP ER CM,SPRecession 1974 1980 1981-2 1990-91 1992 2001-02 2008-2009Key: YC=yield curve, ST=stocks, CM=commodities, ER=earnings, SP=credit spreads,CS=bank credit standards, IP=G7 industrial outputIndicators positioned in the quarter when they started sending recession signals

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Estimating the probability of recession

• Difficult to be precise, no one methodology is infallible. Trusted indicators may become less useful over time (e.g. US yield curve due to QE) Goodhart’s law.

• Therefore we employ three methodologies and average between them in order to give a more robust overall answer

• Probit models of recessions

• Binary models (1 recession, 0 not), estimated using key short termindicators global model includes: global IP, commodity prices,house price and the slope of the US yield curve.

• Fan chart approach

• Using past forecast errors to construct a fan chart around thebaseline, augmented with a view on risks from our proprietary survey.

• Monte Carlo simulation model

• Distribution around central model projection constructed by applyingrandom shocks from model’s standard errors.

Estimating short term probabilities

• Estimate the following binary equation as a probit model:

• Coefficients are significant to the 1% level except house prices. The model is also robust to removing the US yield curve (below) and differing sample periods.


Recession = 𝛼𝛼 + 𝛽𝛽1 ∗ 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑡𝑡−4 − 𝑈𝑈𝑈𝑈𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑈𝑈𝑡𝑡−4 + 𝛽𝛽2 ∗ (Log(𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃𝑡𝑡−2) −log(𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃𝑡𝑡−6)) + 𝛽𝛽3 ∗ (Log(𝐺𝐺𝑜𝑜𝐺𝐺𝑡𝑡−4) − log(𝐺𝐺𝑜𝑜𝐺𝐺𝑡𝑡−8) + 𝛽𝛽4 ∗ (Log(𝐴𝐴𝐴𝐴_𝑝𝑝𝑝𝑡𝑡−2) −Log(𝐴𝐴𝐴𝐴_𝑝𝑝𝑝𝑡𝑡−6) + 𝜀𝜀𝑡𝑡



Sample 1977 -2019 Constant

US yield curve slope

Global industrial

productionOil prices

Advanced Economy house

pricesAdj. R2

Coefficient -0.49** -0.34*** -19.3*** 2.55*** -7.18* 0.32

Std error 0.25 0.09 6.53 0.70 4.98

Coefficient -1.19*** -18.8*** 2.50*** -24.9* 0.26

Std error 0.19 11.0 1.02 9.67

*** – Significant at 1% level; ** – Significant at 5% level; * – Significant at 15% level.22

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We place a 25% probability on global recession


Recession = 𝛼𝛼 + 𝛽𝛽1 ∗ 𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑡𝑡−4 − 𝑈𝑈𝑈𝑈𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑈𝑈𝑡𝑡−4 + 𝛽𝛽2 ∗ (Log(𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃𝑡𝑡−2) −log(𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺_𝑃𝑃𝑃𝑃𝑡𝑡−6)) + 𝛽𝛽3 ∗ (Log(𝐺𝐺𝑜𝑜𝐺𝐺𝑡𝑡−4) − log(𝐺𝐺𝑜𝑜𝐺𝐺𝑡𝑡−8) + 𝛽𝛽4 ∗ (Log(𝐴𝐴𝐴𝐴_𝑝𝑝𝑝𝑡𝑡−2) −Log(𝐴𝐴𝐴𝐴_𝑝𝑝𝑝𝑡𝑡−6) + 𝜀𝜀𝑡𝑡



Sample 1977 -2019 Constant

US yield curve slope

Global industrial

productionOil prices

Advanced Economy house

pricesAdj. R2

Coefficient -0.49** -0.34*** -19.3*** 2.55*** -7.18* 0.32

Std error 0.25 0.09 6.53 0.70 4.98

Coefficient -1.19*** -18.8*** 2.50*** -24.9* 0.26

Std error 0.19 11.0 1.02 9.67

*** – Significant at 1% level; ** – Significant at 5% level; * – Significant at 15% level.22

We place a 25% probability on global recession

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1979 1984 1989 1994 1999 2004 2009 2014 2019

Global: Recession probability%


Recessions

Recession probability

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*** Significant at 1% level; ** Significant at 5% level; * Significant at 15% level.

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• Difficult to be precise, no one methodology is infallible. Trusted indicators may become less useful over time (e.g. US yield curve due to QE) – Goodhart’s law.

• Therefore we employ three methodologies and average between them in order to give a more robust overall answer:


• Binary models (1 recession, 0 not), estimated using key short term indicators – global model includes: global IP, commodity prices, house price and the slope of the US yield curve.


• Using past forecast errors to construct a fan chart around the baseline, augmented with a view on risks from our proprietary survey


• Distribution around central model projection constructed by applying random shocks from model’s standard errors.

Constructing fan charts

• Oxford economics has around 20 years of monthly base case forecasts over a horizon of five to ten years. We measure the deviation of forecasts against the actual GDP data (adjusting for revisions to history) so that we are able to collect information on the characteristics of the error distribution at each point (t+1, t+2…t+40).

• Using this information we are able to construct bands of uncertainty around each point on the forecast distribution. This is the same methodology as used by major central banks.

• A two piece normal distribution is used to incorporate skew into the distribution. A normal distribution seems to fit the uncertainty around a base case and therefore a two piece normal is used when skew is found in the distribution. We use responses from our proprietary quarterly risk survey of around 200 clients to inform the skew.

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Businesses remain pessimistic…

…which skews our probability distribution

Businesses remain pessimistic…

26…which skews our probability distribution

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• Difficult to be precise, no one methodology is infallible. Trusted indicators may become less useful over time (e.g. US yield curve due to QE) – Goodhart’s law.

• Therefore we employ three methodologies and average between them in order to give a more robust overall answer:


• Binary models (1 recession, 0 not), estimated using key short term indicators – global model includes: global IP, commodity prices, house price and the slope of the US yield curve.


• Using past forecast errors to construct a fan chart around the baseline, augmented with a view on risks from our proprietary survey


• Distribution around central model projection constructed by applying random shocks from model’s standard errors.

Using Monte Carlo simulations to infer risks

• Estimate a simple IS curve where global GDP growth is a function of interest rates, exogenous fiscal policy, and supply side factors in a error correction form:

• Run Monte Carlo simulations on the forecast using the model’s own forecast errors as the starting point of the distribution around the baseline forecast. Adjust distribution according to a view on the balance of risks if required.

• Assess the thousands of GDP profiles to see which ones feature 2 consecutive quarters of negative growth and how deep those recessions are.

Using Monte Carlo simulations to infer risks

• Estimate a simple IS curve where global GDP growth is a function of interest rates, exogenous fiscal policy, and supply side factors in a error correction form:

• Run Monte Carlo simulations on the forecast using the model’s own forecast errors as the starting point of the distribution around the baseline forecast. Adjust distribution according to a view on the balance of risks if required.

• Assess the thousands of GDP profiles to see which ones feature 2 consecutive quarters of negative growth and how deep those recessions are.

Log(𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡) − log(𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡−4) = 𝛼𝛼 + 𝛽𝛽1 ∗ Log(𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡−1) − log(𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡−5) + 𝛽𝛽2 ∗ 𝑅𝑅𝑅𝑅𝐺𝐺𝑅𝑅𝑅𝑅𝑡𝑡−4 +𝛽𝛽3 ∗ 𝜟𝜟 Structuralbalanc𝑒𝑒𝑡𝑡−6 − 𝛽𝛽4 ∗ Log(𝐺𝐺𝐺𝐺𝐺𝐺𝑡𝑡−4 − 𝛼𝛼 − Log(𝑅𝑅𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡−4) + 𝜀𝜀𝑡𝑡

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Probability of a deep recession is below 5%







Probability of a deep recession is below 5%

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Protectionism dominates our clients’ risk perceptions







Protectionism dominates our clients’ risk perceptions

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Oxford Economics Global Economic Model• Fully integrated global economic

model. Individual country models are fully linked through global assumptions about trade volume and prices, competitiveness, capital flows, interest and exchange rates, and commodity prices.

• Comprehensive country coverage. 80 countries are examined in detail, plus the Eurozone. The rest of the world economy is covered in six trading blocs.

• User-friendly software. Quickly build scenarios, export data, load pre-defined scenarios, and present data as charts, heat maps, and dashboards.

The Most Comprehensive Model of it Kind

Oxford Economics Global Economic Model

• Fully integrated global economic model. Individual country models are fully linked through global assumptions about trade volume and prices, competitiveness, capital flows, interest and exchange rates, and commodity prices.

• Comprehensive country coverage. 80 countries are examined in detail, plus the Eurozone. The rest of the world economy is covered in six trading blocs.

• User-friendly software. Quickly build scenarios, export data, load pre-defined scenarios, and present data as charts, heat maps, and dashboards.

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How our country models work

The broad structure of our models is similar across countries, with amendments to reflect country specific factors such as dependence on commodities, exchange rate regime, and flexibility of the labour market. The key relationships in a typical model include:

• Consumer spending is driven by real income, wealth and interest rates.

• Investment is driven by the return on investment and changes in capacity utilisation.

• Exports/Imports depend on world/domestic demand and competitiveness.

• Wages move with inflation, productivity and unemployment relative to the natural rate.

• Prices are a mark-up on unit costs, are profit margins are a function of the output gap.

• Monetary policy is modelled to reflect central bank behaviour.

• Exchange rates are determined by relative productivity and net external assets in the long run, and by movements in relative interest rates in the short run.

Integrated global model with multiple linkages

• Trade volumes: World trade for each country is a weighted average of the growth in total goods imports (excluding oil) of all other countries.

• Competitiveness: IMF relative unit labour costs where possible

• Trade prices: A country’s exports are another’s imports.

• Interest rates and exchange rates

• Commodity prices: Oil depends on supply/demand balance and metals on industrial growth.

• Capital flows: Including the impact of FDI, credit ratings and bond spreads.

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Quantitative approach to modelling trigger events

• We model scenarios using variables in the Global Economic Model (GEM) known as scenario levers.

• These include:

• Policy variables (central bank interest rates, government investment/consumption)

• Confidence shocks (general or equities only)

• Tariff shocks between given countries

• Shocks to risk premia on sovereign and corporate bonds

• Shocks to financial market volatility

• Shocks to credit availability

• Supply side shocks (potential output)

• The choice of variables differs between scenarios and is chiefly informed by the narrative of a given shock.

Quantitative approach to modelling trigger events• Once the narrative of the chosen

scenario has determined which levers to use, the severity of the shocks to these variables are determined in one of two ways:• Calibration against historic

episodes (e.g. a shock to US GDP growth of 50% of the GFC impact)

• Probabilistic generation using historic forecast errors (e.g. a 5% probability downside path based on error bands around our central forecast)

• These approaches then provide a set of well-founded assumptions from which the GEM can generate scenario responses.

Quantitative approach to modelling trigger events

• Once the narrative of the chosen scenario has determined which levers to use, the severity of the shocks to these variables are determined in one of two ways:

• Calibration against historic episodes (e.g. a shock to US GDP growth of 50% of the GFC impact)

• Probabilistic generation using historic forecast errors (e.g. a 5% probability downside path based on error bands around our central forecast)

• These approaches then provide a set of well-founded assumptions from which the GEM can generate scenario responses.

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2009 2011 2013 2015 2017 2019 2021 2023

US: GDP% year

Baseline

Forecast

US recession


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Global trade war – scenario outline

• Trade tensions escalate dramatically on all fronts.

• The US imposes tariffs of:

• 45% on goods imports from China.

• 35% on Mexican goods imports.

• 25% on all goods imported from the EU

• and all other major trading partners.

• All targeted countries respond in kind.

• Financial markets react immediately:equities fall back sharply; advanced economy bond yields are compressed but EM risk premia rise; and the US dollar appreciates across most currencies.

• Policies offset some of the tariff impacts -the Fed cuts rates aggressively.

Global trade war – scenario outline

• Trade tensions escalate dramatically on all fronts.

• The US imposes tariffs of:

• 45% on goods imports from China.

• 35% on Mexican goods imports.

• 25% on all goods imported from the EU and all other major trading partners.

• All targeted countries respond in kind.

• Financial markets react immediately: equities fall back sharply; advanced economy bond yields are compressed but EM risk premia rise; and the US dollar appreciates across most currencies.

• Policies offset some of the tariff impacts -the Fed cuts rates aggressively.

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Global growth slows sharply…

…as tariffs result in a deterioration in sentiment…

Global growth slows sharply…

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…as tariffs result in a deterioration in sentiment…

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…despite the Fed’s aggressive reaction…

…which drives a dramatic fall in Treasury yields

…despite the Fed’s aggressive reaction…

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…which drives a dramatic fall in Treasury yields

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Global trade war – scenario results

Global trade war – key impacts

• Global GDP growth slows to 1.6% in 2020 and 1.5% in 2021

• Most affected:

• (i) Mexico;

• (ii) China;

• (iii) Open Asian economies;

• (iv) US.

• Commodity prices are depressed by weaker global demand.

• The US dollar strengthens (apart from against the yen).

• Advanced economy yields are compressed, with US Treasury yieldsmore than 100bps below baseline.

• But Emerging Market risk premia rise, particularly for the morevulnerable.

• US and global equity prices fall sharply.

Global trade war – scenario results

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Global Recession – scenario outline

• A more severe downturn in the manufacturing sector spills over to the broader economy as wage and jobs growth slow.

• Consumer and business confidence deteriorate across large manufacturing economies.

• As asset markets react, a fall in equity prices deepens the slowdown.

• A rise in risk premia results in a spike in short-term spreads and higher bond yields.

• The yen appreciates against the dollar, whereas the euro depreciates.

• The Fed responds by cutting the interest rate on excess reserves.

• Fiscal policy action is initially limited.

Global industrial production contracts as often occurs in global recessions

Global Recession – scenario outline

• A more severe downturn in the manufacturing sector spills over to the broader economy as wage and jobs growth slow.

• Consumer and business confidence deteriorate across large manufacturing economies.

• As asset markets react, a fall in equity prices deepens the slowdown.

• A rise in risk premia results in a spike in short-term spreads and higher bond yields.

• The yen appreciates against the dollar, whereas the euro depreciates.

• The Fed responds by cutting the interest rateon excess reserves.

• Fiscal policy action is initially limited.

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Global industrial production contracts as often occurs in global recessions

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Weakness spreads as both goods and services exports falter

Asset price falls exacerbate the slowdown

Weakness spreads as both goods and services exports falter

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Asset price falls exacerbate the slowdown

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GDP expands more slowly than population in the first half of 2020

Global Recession – scenario results

GDP expands more slowly than population in the first half of 2020

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Global Recession – scenario results

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Global Recession – key impacts

• Global GDP growth to 1.2% below baseline in 2020 and 1.1% in 2021.

• Most affected:

• (i) Advanced economies

• (ii)Vulnerable emerging markets

• Equity prices in the US and Europe are the worst hit globally.

• The euro depreciates against the US dollar in the first year of the scenario, by around 5%.

• The Fed cuts the interest rate on reserves to 0.25% by Q3 2020, more than 100bps below our baseline.

• Oil prices drop around 31% below our baseline.

Conclusions

• Don’t get overly gloomy about the global economy – we don’t yet see the trigger for a global recession.

• But we expect an extended period of uncertainty, depressing growth, interest rates and investment.

• We estimate using a variety of techniques that there is between 25-30% likelihood of a global recession over the next year.

• A global trade war is considered the key risk, although there are concerns about the US economy and geopolitical risks.

• Modelling these scenarios using the Oxford Global Economic Model suggests that both a trade war and a deterioration of the US economy could tip the world into recession.

Global Economic ProspectsSlow Growth, Policy Challenges

Dr. M. Ayhan KoseEFI - Prospects Group - The World Bank

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Global Economic Prospects – January 2020

• Global Outlook: Fragile, Handle with Care

• Regional Outlooks

• Fading Promise: How to Rekindle Productivity Growth

• The Fourth Wave: Recent Debt Buildup in EMDEs

• Price Controls: Good Intentions, Bad Outcomes

• Low for How Much Longer? Inflation in Low-Income Countries

* EMDEs = Emerging Market and Developing Economies

Three Questions

1 How is the health of the global economy? Global growth remains weak with tentative signs of stabilization.

2 What are the major risks? Risks are to the downside: trade tensions, sharper-than-expected slowdown in major economies, financial market stress, lower-than-expected potential growth, geopolitical risks, and natural disasters. Two important upside risks: sustained decline in policy uncertainty and additional policy stimulus.

3 What policies could help? Need for comprehensive, country-specific policies. Facilitate investment in physical, intangible, and human capital; encourage resource reallocation; foster technology adoption and innovation. Rebuild buffers and improve debt management and transparency.


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Global Activity - 1

Weak Trade; Lower Commodity Prices; Easing Financing Conditions

Source: Bloomberg; CPB Bureau for Economic Policy Analysis; World Bank.

Left Panel. Trade is the average of export and import volumes. 3-month moving averages. Other EAP = East Asia and Pacific excl. China, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Last observation is October 2019. Right Panel. Based on Goldman Sachs Financial Conditions Index for the United States, United Kingdom, Japan, Euro Area, India, Indonesia, Brazil, Mexico, Russia, and Turkey. Aggregates calculated using GDP weights at 2010 prices and market exchange rates for 2018. Last observation is January 8, 2020.

Global Activity - 2

Stabilizing at Weak Levels

Sources: Haver Analytics, Institute of Shipping Economics and Logistics, World Bank.

Left Panel. Manufacturing and services are measured by Purchasing Managers’ Index (PMI). PMI readings above 50 indicate expansion in economic activity; readings below 50 indicate contraction. Last observation is December 2019. Center Panel. Figure shows 3-month moving averages. New export orders are for manufacturing and measured by PMI. Last observation is November 2019 for container shipping and December 2019 for new export orders. Right Panel. Last observation is December 2019.

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Growth Forecasts

Widespread Downgrades for 2019 and 2020

Source: World Bank.

Note: e and f refer to estimates and forecasts, respectively.

Per Capita Growth

Significant Loss of Momentum

Source: United Nations; World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Left Panel. Data for 2019 are estimates. Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates. EMDE sample includes 144 countries, with 83 commodity exporters. Right Panel. Long-term average is 2000-19. Poverty rates represent latest available data.

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Three Questions2 -What are the major risks? Risks are to the downside: trade tensions, sharper-than-expected slowdown in major economies, financial market stress, lower-than-expected potential growth, geopolitical risks, and natural disasters. Two important upside risks: sustained decline in policy uncertainty and additional policy stimulus.

RisksTilted to the Downside Despite Some Upside Risks.

Risks

Upside• Sustained decline in policy uncertainty• Additional policy support

Downside• Re-escalation of trade tensions• Sharper-than-expected slowdown

in United States, Euro Area, or China

• Financial market stress amid rising debt

• Lower-than-expected potential growth

• Rising geopolitical tensions• Increasing frequency of natural

disasters

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Upside RisksSustained Decline in Policy Uncertainty; Further Monetary and Fiscal Stimulus

Source: Baker, Bloom, and Davis (2016); Bank for International Settlements; Bloomberg; Haver Analytics; World Bank.

Left Panel. Figure shows median growth impact of 10 percent fall in U.S. economic policy uncertainty (EPU). See Annex SF.1B of World Bank (2017) for details on the methodology. Right Panel. Calculations based on change in year-on-year global inflation and nominal interest rate between November 2018 and November 2019. Aggregate nominal interest rate calculated using GDP weights at 2010 prices and market exchange rates. Unbalanced samples include 35 advanced economies and 77 EMDEs, including 39 low-income countries, for nominal interest rates and include 36 advanced economies and 112 EMDEs for inflation. Last observation is November 2019.

Trade Policy UncertaintyRemain Elevated

Source: Ahir, Bloom, and Furceri (2018); International Monetary Fund; Organisation for Economic Co-operation and Development; World Bank.

Left Panel. Shaded area indicates forecasts. Trade measured as the average of import and export volumes. Right Panel. Trade policy-related uncertainty is an index presented in Ahir, Bloom, and Furceri (2018) for 143 countries on a quarterly basis. Business confidence data are end of period and include 7 advanced economies and 5 EMDEs. Aggregate business confidence calculated using GDP weights at 2010 prices and market exchange rates. Last observation is 2019Q4 for trade policy uncertainty. Business confidence data for 2019Q4 use October 2019.

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Waves of Debt in EMDEsLargest and Fastest Debt Increase in the Current (Fourth) Wave

Source: International Monetary Fund; World Bank.

Note. Gross debt for general government and nonfinancial private sector. Averages computed with current U.S. dollar GDP as weight and shown as a 3-year moving average. Vertical lines in gray, designating the start of debt waves, are for years 1970, 1990, 2002, and 2010. Wave I (1970-1989); Wave II (1990-2001); Wave III (2002-2009); Wave IV (2010-now). Right Panel. Rate of change calculated as total increase in debt-to-GDP ratios over the duration of a wave, divided by the number of years in a wave. Wave I (1970-1989); Wave II (1990-2001); Wave III (2002-2009); Wave IV (2010-now).

Current Debt Wave - 1Different: Slowing Growth; Rising Debt

Source: International Monetary Fund; World Bank.

Note: Total debt (in percent of GDP) and real GDP growth (GDP-weighted at 2010 prices and exchange rates). Wave I (1970-1989); Wave II (1990-2001); Wave III (2002-2009); Wave IV (2010-now).

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Current Debt Wave - 2Record Low Nominal Interest Rates; Rising Debt Trajectories

Source: Bloomberg; Haver; Kose et al. (2017); World Bank.

Left Panel. Weighted average of the U.S., Euro Area and Japan using GDP-weights at 2010 prices and exchange rates. History reflects short-term interest rates (3 months or less). Expectations reflect overnight index swap (OIS) forward rates. Last updated 8 January 2019. Right Panel. Share of countries where long-term nominal interest rates (represented by 10-year local currency government bond yields or with close maturity) are below nominal GDP growth. Sample includes 84 EMDEs, though the sample size varies by year. Share of countries in which sustainability gaps are negative (i.e., debt is on a rising trajectory, or debt-increasing fiscal positions). Sample includes 83 EMDEs.

Productivity GrowthSteepest, Longest, Broadest Slowdown in 40 years

Source: International Monetary Fund; Penn World Table; World Bank.

Note: Productivity = output per worker. Data from 1981-2018 and includes 29 advanced economies and 74 EMDEs. GDP-weighted (at constant 2010 prices and exchange rates) aggregates. Shaded area shows EMDE slowdown episodes. Right Panel. “Slowdown” is the cumulative decline in EMDE productivity growth from the peak of the episode to the trough. “Rebound” is the cumulative increase in EMDE productivity growth from the trough (end) of the episode to three years later. “Affected EMDEs” is the share of EMDEs that experienced a slowdown.

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Productivity Convergence in EMDEsWeak Productivity Growth; Slow Convergence

Source: Penn World Table; World Bank.

Note. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa. Left Panel. GDP-weighted productivity growth for 8 EMDEs in EAP, 10 in ECA, 18 in LAC, 10 in MNA, 2 in SAR, and 26 in SSA. Right Panel. The proportion of EMDEs in each region that will close half of the productivity gap with the average advanced economy in each bracket of years based on average growth during 2013-18 relative to average advanced economy growth and the outstanding productivity gap over the same period.

Other Downside RisksSharp Movements in Oil Prices; Increasing Frequency of Weather Events

Source: International Monetary Fund; Munich Reinsurance Company; World Bank.

Left Panel. Change in overall fiscal balance is measured from 2014-16. Above average and below average oil revenue groups are defined by countries above or below the sample average of oil revenues as a share of GDP based on 2014 data. Right Panel. Global natural disasters and economic losses statistics from Munich Reinsurance Company including loss estimation based on Property Claim Services (PCS). The 30-year average represents 1988-2017. 5-year average represents 2014-2018. Losses adjusted to inflation based on local CPI.

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Three Questions3 - What policies could help? Need for comprehensive, country-specific policies. Facilitate investment in physical, intangible, and human capital; encourage resource reallocation; foster technology adoption and innovation. Rebuild buffers and improve debt management and transparency.

PoliciesComprehensive, Country-Specific Policies Needed

• Broaden tax base

• Improve debt management and transparency

• Replace energy subsidies with targeted social safety net

• Facilitate investment in physical, intangible, and human capital

• Foster technology adoption and innovation

• Improve governance and business climates

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• Strengthen regulatory and supervisory regimes

• Improve financial stability

• Build credibility and transparency of monetary policy

• Reinforce multilateral trading system

• Coordinate home-host bank supervision

• Prevent tax avoidance

Policies for Managing Debt and Promoting ProductivityBroad Guidelines; Depends on Country Context

Dealing with debt-related vulnerabilities

• Improve debt management and debt transparency to contain fiscal risks• Strengthen monetary, exchange rate, and fiscal policy frameworks to

safeguard resilience• Design strong financial sector regulation and supervision systems to

identify and act on risks• Strengthen institutions and government efficiency to promote good

governance practices

Rekindling productivity growth

• Raise labor productivity by stimulating investment and improving human capital

• Foster firm productivity by exposing firms to competition and strengthening human capital

• Support sectoral reallocation and diversification• Strengthen institutions and government efficiency

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Three Questions1- How is the health of the global economy? Global growth remains weak with tentative signs of stabilization.

2- What are the major risks? Risks are to the downside: trade tensions, sharper-than-expected slowdown in major economies, financial market stress, lower-than-expected potential growth, geopolitical risks, and natural disasters. Two important upside risks: sustained decline in policy uncertainty and additional policy stimulus.

3- What policies could help? Need for comprehensive, country-specific policies. Facilitate investment in physical, intangible, and human capital; encourage resource reallocation; foster technology adoption and innovation. Rebuild buffers and improve debt management and transparency.


Questions & Comments

Thanks!

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Select Publications by Prospects Group

• Global Economic Prospects – January 2020

(January and June)

• Commodity Markets Outlook – October 2019

(April and October)

• A Decade After the Global Recession– November 2019

• Global Waves of Debt– December 2019

• Global Monthly

Investment in Sports Sector: Opportunities and Challenges

Dr. Mahfoud AmaraDirector of Sport Science Program - College of Arts and Sciences

Qatar University

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Sport is shaped by the nature of the political system, by the business, economy, and demography. According to recent report by Josoor Institute based on research undertaken in 17 countries in the MENA region (including Turkey) the value of the MENA sports and events industries stand at USD 15.8 billion and USD 8.7 billion, respectively.

Countries in the GCC such as Qatar, the UAE and Bahrain (and more recently KSA), have used sport for urban development around airlines, tourism and retails industry and a means for place branding (Doha and Dubai as sport capitals in the region). Qatar will host the first mega sport event in the region which is the FIFA 2022 World Cup. This would be considered as the pinnacle in the region with regards to sport development (or sport and development). A region which is undergoing a number of transitions in economic, political, social, and cultural domains. Global sport, and particularly internet and Satellite Sport TV broadcasting, is also shaping different forms of sport consumptions and access to major sport brands and professional sport leagues in the region: English premiership in football, and NBA in basketball, Rolland Garros in Tennis, and Formula 1 for car racing, to name but a few. Alongside, more efforts are put into maintaining, under the patronage of royal families, a tradition of ‘authentic’ sport culture – for example, horse and camel racings or falconry, which symbolize the royal families’ affection for the ‘authentic’ Arab identity.

Qatar since the 2006 Asian Games held in Doha, which is considered as a millstone for its international sport strategy, is investing in sport using both direct and indirect investment. The strategy is to establish an international network and alliances and bring more international visibility to local companies/sectors involved in the economic diversification of Qatar, associated with the development of retail, tourism and hospitality and other service economy based on banking, education and technology (Amara 2013; Chavannat 2017). Moreover, to develop a legacy for Qatar and in the region for sport participation and inclusion through sport.

Opportunities and challenges:

Sport is becoming a multifaceted industry that mobilizes a number of sectors such as tourism, hospitality, retail, media, manufacturing, and construction, to name but a few. It offers a number of opportunities in terms of jobs, diversifying state’s revenues and investment locally and internationally. Investment in sport offers an opening to an international network of business and political elites, and international exposure

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needed for country’s branding, as a tourist destination, or simply the place to be to make business.

Because it attracts and generate a large sum of money, sport is also vulnerable. There are different stakeholders involved (and compete): states with their own national interests; sponsors representing different products, including products that are deemed non-Islamic such as alcohol and gambling; national and international sport governing bodies, which have been recently associated with a number of issues with regards to their governance. TV networks benefiting from the popularity of sport product and its attraction to large audiences/consumers and investors to increase their revenue. Last and not least, private owners of professional sport clubs, who want return on investment, which impact negatively on access to sport events and matches for fans. Fans are asking to pay high cost for tickets, merchandising and TV Subscriptions to cover the inflation of players’ salaries and colossal cost to stage sport events. Hence, the reason why International sport organizations are changing their bid model from single hosting to joint hosting, as many nations and cities cannot bear the financial burden of hosting international sport events.

Sport despite its popularity and inventiveness has been tarnished by exploitation, and violence, including athletes’ violence against their own bodies. They are constantly pushed to go beyond their (human) limits to contribute to profit making, commercial interests, and to national (and sponsors) prestige. They are becoming commodities that can be sold and their bodies can be exposed and sexualized to attract more sponsors and viewers, in the age of consumerism and society of the spectacle. Despite efforts of governmental and non-governmental organizations, on and off the pitch, Sport still suffers from different from of exclusion, racism and inequality.

As most the funding is going toward sports that attract more media coverage, there are many (so-called minority) sports that are left behind. Professional or commercial sport are getting the biggest share of funding and media coverage at the detriment of community sport and sport for all. Many products that are deemed unhealthy (containing fats, sugar, and salt) are using sport and the positive values held around sport (togetherness, sportsmanship, performance, energy, resilience …etc.) to promote their brands among viewers who are perceived by these companies primarily as potential customers.

In applying principles of applied Islamic ethics, these are Amanah and trust ; the duty to contribute to change the world for the better, and comprehensive understanding of

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Islam particularly its paradigm of the middle way/ median course (Ummat al-wasata); the questions to ask are as follow:

1. What contribution Islamic ethics can bring/ can make to promote greater good (Maslaha) and to prevent harm (Mafsada) in sport and in society?

2. What convincing project can Islamic ethics provide to prevent deviance practices in sport?

3. How Islamic ethics can reconcile between performance in sport (being higher, faster, and stronger), nationalist/ commercial interests on the one hand, with health, wellbeing and preservation of elite athletes’ dignity on the other?

4. What contribution Islamic ethics can make to the understanding of the complexity of sport including the complexity of sport industry with its increasing international stakeholders and interests groups?

To conclude one need to strike the right balance between investment in sport, as business and industry, and investment for sport , to promote the values of sport, including participation (for all) in sport.

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References:

• Amara, M. 2013. The Pillars of Qatar’s International Sport Strategy. Retrieved from http://www.e-ir.info/2013/11/29/the-pillars-of-qatars-international-sport-strategy/ ;

• Bodet, G, and Amara, M. (2015) Islam, Sport and Marketing or sport marketing in Muslim cultures, in Testa and Amara, Sport in Islam and in Muslim Communities, London: Routledge.

• Chanavat, N. (2017) “French football, foreign investors: global sports as country branding”, Journal of Business Strategy , Vol. 38 Issue: 6, pp.3-10

• Josoor Institute (2017). “Sports and Event Industries in MENA” in collaboration with Repucom (now Nielsen Sports).

Islamic Finance Outlook – One Industry, Three Accelerators

Dr. Mohamed DamakSenior Director - Global Head of Islamic Finance

S&P. Global Ratings

Copyright © 2019 by S&P Global.

All rights reserved.

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Agenda

1- Sukuk Rating Methodology

2- Slow Growth Is The New Norm

3- Three Accelerators

1- Sukuk Rating Methodology

What is a sukuk?And why is it issued?Sukuk (plural of Sak) are trust certificates usually issued by a specialpurpose vehicle, the proceeds of which are, generally, on-lent to a corporate, financial institution, insurance company, sovereign, or local or regional government (the sponsor), for the purpose of raising funding according to Islamic principles.

Main reasons for Sukuk issuance• Size: limited access to bank financing (regulation limiting concentration, loan

to deposit ratio limits).• Cost of funding: disintermediated financing.• Maturity: generally longer than bank loans.• Sharia compliance: Sharia-compliant financing (if the structure is approved by

Sharia board).

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Sukuk Vs. Bonds: Are they similar?

Sukuk Bonds

Nature certificates Pure debt

Underlyingassets

A minimum percentage of tangibleassets

Not required

Principaland return

Derived from the underlying assets

and/or from the contractualcommitments of the sponsor

Obligations of the issuer(depending

on the ranking)

Purpose Raise long term funding Raise long term funding

Risks Sukuk with sufficient contractual

obligations and no conditionality:

Exposure to the credit quality of the

sponsor.Sukuk with insufficient

contractualobligations or with

conditionality:Residual exposure to the

assets.

Exposure to the credit quality ofthe issuer.

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How does Sukuk work in practice?Sukuk usually involve the transfer of an asset or a pool of underlying assets or beneficial interests in these assets to an SPV, which issues the trust certificates. Generally, the return on these underlying assets serves as a basis for the payment of periodic profit distribution to sukuk holders.To delink the sukuk’s credit quality from the performance of the underlying assets, sukuk sponsors have so far typically undertaken contractual payment obligations to the sukuk issuer to provide it with the necessary funds to fulfill its financial obligations toward investors. While this is the most common approach, we have seen cases where the repayment of the sukuk is solely based on the underlying assets, therefore mimicking the characteristics of a structured or project finance transaction.

Under some sukuk structures (typically the Wakala structures), the sponsor’s contractual payment obligations cover only the principal and last accrued periodic distribution under an early dissolution event, but not the ongoing periodic distribution. Under this type of structure, the sukuk will pay periodic distribution amounts as long as the underlying assets generate sufficient revenues or the sponsor decides to make equivalent payments to the issuing SPV. In case of shortfall, early dissolution is typically triggered, and the sponsor is obliged to make payments to the issuing SPV that enables it to pay any accrued periodic distribution amount and repay the sukuk principal. An early dissolution could require the consent of a certain minimal share of investors, to prevent the sponsor from deliberately triggering dissolution in case it desires to refinance its sukuk.In case of default, there is no access to the underlying assets except for Asset Backed Sukuk (with a true sale of the assets – less than 1% of total sukuk issuance).

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What is a sukuk?

Our Sukuk Rating Criteria

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Our Sukuk Rating Criteria

Methodology For Rating Sukuk, Jan. 19, 2015 – Paragraph 9 We rate a long-term sukuk… and assign it an issue credit rating at the same level as the sponsor’s senior unsecured rating, if the next five conditions (A-E) are met:A.) The contractual payment obligations of the sponsor to the issuer are sufficient for full and timely periodic distributions and final payments of principal (on the scheduled dissolution date or in case of early dissolution);B.) The sponsor’s contractual payment obligations rank pari passu with the sponsor’s other senior unsecured financial obligations (if they do not, but the other four conditions are met,see paragraphs 10 and 25 – (Subordination and Hybrid Instruments)).C.) The sponsor’s contractual payment obligations are irrevocable;D.) The sponsor commits to fully and unconditionally pay all foreseeable costs of the issuer including taxes and costs related to the trustee, service agent, and investment manager through the life of the transaction, in a timely way, so as not to weaken the issuer’s ability to meet all payments due in a timely way;E.) We assess as remote the risks that conditions, such as those mentioned in paragraphs 16 to 20, jeopardize full and timely payments (as defined by our criteria, see paragraph 12). If we believe these risks are non-remote, we may assign an issue credit rating on the sukuk that is different from the equivalent sponsor issue credit rating.

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Analysis of common structures of sukuk through S&P Global Ratings lenses

Most Common Structures of Sukuk

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Legal recourse for investors…

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Example 2In this case, the indemnification is conditional upon showing that the TLE was not due to the negligence of the sponsor or its failure to comply with its contractual obligations regarding insurance.



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How to rate a sukuk?

The five steps to rate sukuk:1- Understand the structure of the transaction (Murabaha: sale and buy back, Ijara: leasing, Wakala: asset management).2- Where the cash flows for repayment are expected to come from? Is it an obligation of the sponsor? Is everything covered (principal/interests)?3- Are these obligations irrevocable / what is their ranking?4- Are the costs covered?5- Is there any event that could disrupt the payments?

Sukuk documentation (Ijara sukuk) typically include:- Base prospectus including a summary of major characteristics of the transaction- A purchase agreement between the sponsor and the SPV: the SPV is the buyer of the beneficial interests in the underlying assets:check that there is no true sale of the assets / check the value of the transfer of the assets and how does it compare with the principal.- A purchase undertaking: agreement between the sponsor and the SPV: the sponsor is the buyer of the beneficial interests in the underlying assets at the maturity of the transaction: check the price and how does it compare with the principal amount.- Lease agreement: for an Ijara sukuk: check the leasing charge and how does it compare with the periodic distribution amounts – Note that there are two possible scenarios:1- Leasing charge is calibrated to match periodic distribution amount and is an obligation of the sponsor;2- Leasing charge is not clearly defined but sukuk holders have the right to request early dissolution if the payment of one of the PD is missed.Both are acceptable from a rating perspective but under scenario 2, check that the accrued but unpaid PD is part of the dissolution amount.- Servicing Agency agreement: that includes the obligations of the sponsor in managing the underlying assets. Check if there are any scenarios where payments could be disrupted and what are the proposed resolutions of such scenarios (Total Loss Event).

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Examples of applying the criteria

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2. Islamic Finance Industry: Slow Growth Is The New Norm

Slow Growth Will Continue…

# Expect the industry to continue to grow slowly due to its concentrated nature and the mild economic performance of core countries.# The sukuk market grew strongly in 2019 and this performance is likely to continue.

# $162 billion in 2019 vs. $129 billion in 2018 (up by 25.6%)# FC issuance up by 20.7% or $39 billion in 2019 vs $33 billion in 2018.# The increase didn’t come from the usual suspects…

After A Stagnation in 2018, Sukuk Issuance Recovered…

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Who led this performance?

# Indonesia started to offer sukuk as liquidity management instruments (volume of issuance +37%)# Turkey, under pressure, opened all available sources of financings (volume of issuance +65%)# Qatar: back to the market after one year absence post boycott (volume of issuance +61%)# Malaysia (+25%) thanks primarily to government issuances.# Kuwait (Central Bank + 22%); Saudi (private sector issuers +31%) have also supported the growth.# Bahrain (+10%) as it started to receive support from neighbors /UAE (-3%) stable although 2018 was exceptional.

# Government issuances increased by 77.3% and were partly explained by the issuances related to the transfer of some Lembaga Tabung Haji (LTH) assets in the course of its restructuring to a wholly owned subsidiary of the Ministry of Finance.# The International Islamic Liquidity Management Corporation issuances increased by 37%.# Other private sector have also increased their issuances by 10.4% in 2019.

Who is issuing in Malaysia?

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Our base case for 2020…

Why?

# We expect the strong performance of 2019 to repeat in 2020 with volume of issuances increasing by around 5% (including 40-45 billion for foreign currency denominated sukuk).

#1 Global Liquidity remains abundant: more than $10 trillion of assets have negative yields. That means that EMs with good credit story will continue to tap the market with relative ease.#2 Innovation is coming: new Fintech could open the market to new issuers by making the process smoother and less expensive. The impact will depend on the adoption by issuers and investors.#3 Standard setters and regulators now understand the necessity of pushing for more standardization and more importantly are adopting a more inclusive approach.#4 The green sukuk market will continue to expand: as more investors commit to responsible investment and the structures and benefits become more apparent. Several core Islamic finance countries have committed to diversifying their energy mix with a significant contribution from green energy generation. Moreover, as GCC countries begin their transition toward less carbon-intensive economies, green projects are set to flourish. Some of these projects will likely be funded via the sukuk market.

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Downside risks?#1 If the cycle unexpectedly turns: A major shift in the global economy could affect some core Islamic finance markets. The most obvious channel would be through lower oil prices. A lower price would mean higher financing needs for GCC governments, which would then need to choose between conventional or sukuk instruments.# 2 Geopolitical risk is still an important factor: Event risk has rapidly escalated in the Gulf region. For now, these do not alter our base-case assumption that any military action by either side will not lead to a fully fledged direct military confrontation. We continue to believe that any escalation will remain contained given that a direct conflict would be economically, socially, and politically destabilizing for the entire region, including U.S.-Gulf allies. #3 Complexity related to sukuk issuance process and lack of standardization (as shown by the lower contribution of sukuk in the funding mix of GCC issuers in 2019)

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# We expect the strong performance of 2019 to repeat in 2020 with volume of issuances increasing by around 5% (including 40-45 billion for foreign currency denominated sukuk).

Inclusive Standardization and Reduction of Complexity

Fintech As A Disruptor/Accelerator…

# Ease and Speed of transactions: particularly for payment services and money transfer. Fintech could also reduce costs and allow the redeployment of staff to higher added value operations.# Traceability of transactions: use of blockchain to reduce risks related to security or identity theft.# Greater accessibility to financial services: particularly relevant for OIC countries with small banking penetration. Mobile banking provision in remote areas for example or crowdfunding solution for business or affordable housing financing.# Improved governance through Regtech: for compliance with regulation and Sharia.Prerequisites for success: operating environment with a Fintech friendly infrastructure, regulation (through incubators, sandboxes, etc.), financial and human capital.

3. Three Accelerators

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Some Of These Are Still Lacking in MEA…

Sukuk and Blockchain: A perfect match?

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Blockchain: Underlying assets tracking

Blockchain: Cash flows tracking

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Blockchain: Investors tracking / decision making

Islamic finance and ESG

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Islamic Finance and “E”: Green Sukuk

The GBP four core components:1. Use of Proceeds2. Process for Project Evaluation andSelection3. Management of Proceeds4. Reporting

Islamic Finance and “E”: Green Sukuk

Why?

1- For issuers: access a broader investor base: a growing number of investors is publicly committing to climate and responsible investment.2- For investors: buy products that are in line with their beliefs (Sharia compliance and green)3- Rise in energy demand and change in mix.

How?

Regulatory push: tax breaks or additional funded or unfunded enhancement mechanisms (for example, guarantees or offtake agreements).Inclusive standardization: of legal documentation and Sharia interpretation.

The Five Principles of Islamic Finance1- Prohibition of interest2- Prohibition of speculation3- Prohibition of the financing of illicit sectors (pork, weapons, alcohol,…)4- Profit & Loss sharing principle5- Asset backing principle

Core Islamic Finance Countries1- Association of Southeast Asian Nations (ASEAN) target to increase the relative weight of renewables in their energy mix to 23% by 2025.2- Dubai is targeting a renewables mix of 75% by 2050, while3- Saudi Arabia expressed its intentions to build a gigantic $200 billion solar project.

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Islamic Finance and the Missing “S”

# Islamic issuers have relayed the “S” factor to the back seat# Only few of them have established specific guidelines / objectives for Social issues.# There are few instruments in Islamic finance/economy: Waqf /Zakat / Qard Hassan# Their total size is reportedly huge and therefore, it is important to have proper governance mechanisms in place to avoid diverting these instruments from their original purpose.

# The end goal: Move from negative screening to positive screening: from ”what is compliant” to “what is compliant and impactful”!

Thank you

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Artificial Intelligence in Islamic Banking: Future Vision

Professor Azmi OmarInternational Center for Education in Islamic Finance (INCEIF), Malaysia

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1- IntroductionArtificial intelligence (AI) according to many experts is one of the most important transformations brought by the 4th Industrial Revolution. Currently it is considered to be at a nascent stage but rapidly evolving to meet the needs of businesses and governments. Many pundits have heralded the emergence of AI and postulate the positive impact to the economy. PwC in its AI Impact Index 2017 predicted that AI could potentially contribute US$15.7 trillion to the global economy by 2030, thereby raising GDP of countries by up to 26%(1). Accenture describes AI as “a computer system that can sense, comprehend, act and learn - a system that can perceive the world around it, analyse and understand the information it receives, take actions based on that understanding, and improve its own performance by learning from what happened(2)”. Although AI is not new, it has been around since the 50s, but the current and future AI applications have tremendous potentials to make great impacts to the way businesses and governments organised their activities. The recent advent of massive computing power, ongoing development of machine-learning algorithms and availability of large data make AI a reality today. AI requires large amount of data for it to sense, comprehend, act and learn thereby develop human-like cognitive functions and massive computing power to run the applications.Banks, which sit on a large amount of data see great potentials in AI as they face competition from fintech companies. Many banks’ CEOs opined AI’s role as a platform to grow their revenue, reduce costs, improve customer experience and strengthen risk management. On the other hand, banks staff and unions see AI with trepidation, asserting that job cuts will likely occur and workers will be made redundant and ultimately replaced by machines. The objectives of the paper are to describe the current and future use of AI in both conventional and Islamic banks, discuss issues and challenges in adopting AI and provide some perspectives on the future development of AI for Islamic banks.

2- AI Applications in Banking

FSB(2017) and Deutsche Bank classify current and potential use of AI applications in banking into four categories: (i) Customer-focused front office applications, (ii) operations-focused back office applications, (iii) trading and portfolio management, and (iv) regulatory compliance.(3) The breakdown of each category and AI applications is shown in Table 1 below.

(1) https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf(2) https://www.accenture.com/_acnmedia/PDF-68/Accenture-Redefine-Banking.pdf(3) https://www.dbresearch.com/PROD/RPS_EN PROD/PROD0000000000495172/Artificial_intelligence_in_banking%3A_A_lever_for_pr.PDF

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Table 1 AI Implementation in Banking

Categories of AI Applications AI Applications

Customer-focused front office

Credit scoring

Insurance policies

Client facing chatbots

Know your customer

Operations-focused back office

Capital optimisation

Model risk management

Stress testing

Fraud detection

Trading and portfoliomanagement

Trade execution

Portfolio management

Regulatory compliance

Regulatory technology

Macroprudential surveillance

Data quality assurance

Supervisory technology

Sources: FSB (2017), Deutsche Bank Research (2019)

Some of the AI applications listed above are already available whilst others are in development stages. And as mentioned in the above introduction, AI is still at a nascent stage and current applications in banking do not reflect AI potentials. Hong Kong Monetary Authority in its 2019 study reported 5 top current use of AI cases among Hong Kong banks(4). These are Cybersecurity applications, Client-facing chatbots, Remote client on-boarding, Biometric customer identification, and Personalised advertisements. HKMA also reported that Hong Kong banks in the immediate future plan to use AI applications for Anti-money laundering (AML), Fraud detection, and Financial advice. It also reported that AI applications for Know-Your-Customer (KYC), Operational automation and Credit scoring are currently being explored

(4) https://www.hkma.gov.hk/media/eng/doc/key-functions/finanical-infrastructure/Whitepaper_on_AI.pdf

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and will be used in the future. In another study done by SAS and GARP, which received 2,000 responses from financial services industry, the top AI application areas are automation of manual process (52%), credit scoring (45%), data cleansing and enhancement (43%) and risk grading (37%)(5). Examples of some of the current AI use by banks are given below.

2.1 Client-facing Chatbots

Several large banks use client-facing Chatbots and voice bots as a tool to interact with customers and manage their accounts. In the same report, Hong Kong Monetary Authority cited one Hong Kong bank used its Chatbots to handle more than four million customer enquiries from Mainland China on a daily basis. In USA, Bank of America launched a chatbot called Erica which claims to use predictive analytics, to forecast customer account balance based on spending patterns and to provide financial guidance to customers(6). Kuwait Finance House recently launched a robotic bot named as Baitak Assistant, which will handle customer credit applications and will autonomously create credit reports for applicants(7). As Chatbots become widespread and more intelligent, we expect more banks to embrace the technology. Figure below illustrates how banks use Chatbots and voice bots to interface with clients.

Client-facing Chatbots Used in Banking

Source: HKMA (2019)

(5) https://www.sas.com/content/dam/SAS/documents/marketing-whitepapers-ebooks/third-party-whitepapers/en/artificial-intelligence-banking-risk-management-110277.pdf(6) https://emerj.com/ai-sector-overviews/ai-in-banking-analysis/(7) https://www.arabianbusiness.com/banking-finance/414530-kuwait-finance-house-using-robotic-assistant-for-loan-applications

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2.2 Remote client on-boarding

The first interaction between a customer and a bank is opening of a bank account. Before the advent of AI, the process to open a bank account involves a lot of paper work and time consuming. Some banks have now used remote client on-boarding to allow a seamless way to open a bank account for customer. An example of remote client on-boarding AI used in a Hong Kong bank is illustrated below. A customer is required to upload softcopy of identification documents and personal details to the bank’s website or Apps. In addition, the AI will use Facial and voice recognition and also biometric information as part of the on-boarding process. The time taken to on-board a customer may take 15 minutes or less.

Source: HKMA (2019)

2.3 Financial advice with robo advisors

Robo advisory is used by banks to offer professional investment advice and financial planning to customers at a cost lower than those provided by human advisors. This service is available twenty-four hours via Apps or web. Robo advisor uses algorithms to create an optimised and diversified portfolio based on client’s risk preference and appetite. An example of robo advisor is shown below:

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Source: HKMA (2019)

Currently Wahed Investment is the only AI robo advisor that provides Shariah-compliant investment advisory services(8). Like conventional robo advisor, Islamic bank customers can create Shariah-compliant investment based on their risk appetite.

2.4 Risk management

One of the most popular use of AI in risk management is fraud detection. AI algorithms check customers’ credit card transactions real time to determine legitimate or fraudulent transactions. Another AI application is credit risk assessment for SMEs based on traditional and alternative data. AI credit risk will utilise traditional credit risk data and data from other sources such as social media and e-commerce platform to determine credit worthiness of clients.

(8) https://journal.wahedinvest.com/role-of-robo-advisors-in-islamic-financial-institutions/

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3- Issues and challenges in AI adoption

Although many banks are keen to adopt AI given its potential contributions to the bottom line, there are many challenges and issues that must be addressed. These include:

3.1 Lack of quality data

AI requires large amount of quality data for training and validation. Banks face challenges to get quality data as most data are not in the form meeting AI requirements. Significant investments are required to clean the data before it can be utilised.

3.2 Data privacy and security

Data security and privacy is another concern when come to data collection, sharing and use. In many cases banks ask for customer consent to use data collected but whether that consent covers facial, voice and biometric information?

3.3 Ethical AI regulations

Many regulators are concern about the haphazard way AI has developed and look to develop ethical guidelines and principles in implementing AI. For example as reported in Hong Kong Monetary Authority report, the governments of France and Canada announced a plan to jointly create an International Panel on Artificial Intelligence (IPAI) to “support and guide the responsible development of artificial intelligence that is grounded in human rights, inclusion, diversity, innovation, and economic growth(9)”. At the moment there is no regulator that has issued or plan to issue guidelines on AI that will incorporate Shariah principles.

3.4 Lack of employees with AI expertise

As AI is new, the number of AI experts is limited. Hong Kong reported that 80.7% of the bank respondents cited the inability to find qualified experts in AI and banking as a significant barrier to AI adoption. A similar problem occurs in Islamic banks as there are very limited number of individuals with expertise in AI, Islamic banking and Shariah.

(9) https://www.hkma.gov.hk/media/eng/doc/key-functions/finanical-infrastructure/Whitepaper_on_AI.pdf

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4- Future development of AI in Islamic banks

As described in the paper, there are very limited examples of AI applications used by Islamic banks. AI is new and may not come under the radar of many Islamic banks. Those that are implementing or are planning to implement AI are the big Islamic banks in GCC and South East Asia. However, the potential to develop and implement AI applications in Islamic banks are great.

One of the potential areas is Fatwa or Shariah ruling. A recent example is the initiative of Dubai government which launched AI Fatwa service on religious matters(10). Although it is now able to answer around 205 questions related to prayer, zakat, purity and worship, future plan include financial matters. Islamic banks either through independent or collective effort can also develop AI applications on Shariah resolutions and parameters related to financial matters. The benefits of such AI application are numerous. Islamic banks can use AI in product development, assist their Shariah department in conducting Shariah review and audit and also assist Shariah Supervisory Board in their deliberations.

Another AI application is credit risk. Islamic banks are reluctant to implement mudaraba and musharakah financing with customers due to high risk and capital ratios. The current risk assessment method relies on traditional credit risk data and does not capture non-traditional data such as behavioural and spending patterns. With AI, it is possible to capture those data and use algorithms to determine credit risk assessment of customers. This will be helpful for Islamic banks to meet Maqasid al Shariah objectives as well other objectives such as SDGs in the form of f

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25 February 2020 - Sheraton Doha Hotel · we expect to have result oriented discussions for serving the goals of the conference ... and organized by Bait Al-Mashura finance consultations