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Economic Forecasting: Learning from the Future
Dr. Olga Loiseau-Aslanidi
Asst. Director-Economist
Economic & Consumer Credit Analytics – EMEA
Moody’s Analytics
UPCES lecture , Prague, October 1, 2013
2 2
Lecture Outline
• Overview of Moody’s Corporation
• Basics of economic forecasting
• Macroeconomic forecasting: baseline and alternative scenarios
• Market risk forecasting (if time permits)
3 3
• Moody's Corporation provides credit ratings, research, tools and analysis that contribute to
transparent and integrated financial markets.
• Moody’s was founded more than 100 years ago by John Moody & Company.
• In 1900, Moody created the manual based on his assessment of the market’s needs at the
time.
• Moody's Corporation maintains a presence in 29 countries.
• Americas: Boston, Buenos Aires, Chicago, Dallas, Mexico City, New York, São Paulo, San
Francisco, Toronto, Alpharetta, Montreal, West Chester
• EMEA: Dubai, Frankfurt, Johannesburg, Limassol, London, Madrid, Milan, Moscow, Paris,
Grenoble, Saint Cloud, Port Louis, Prague, Warsaw
• Asia: Beijing, Shenzhen, Mumbai, Hong Kong, Seoul, Singapore, Sydney, Melbourne,
Tokyo
4 4
Moody's Analytics operates independently of the credit ratings activities of Moody's Investors
Service. We do not comment on credit ratings or potential rating changes, and no opinion or
analysis you hear during this presentation can be assumed to reflect those of the ratings agency.
5 5
Economics Group at Moody’s Analytics
Who we are
» 70+ economists, more than 40% of which
have PhD’s
» 20+ data specialists
» Located around the globe in London,
Prague, Sydney and West Chester
We focus on being THE LEADER at understanding local economies using the most
recent data from the best sources, analyzed by dedicated economists
What we do
» Maintain extensive database of economic,
financial and demographic data down to the
regional and city level with over 260 million time
series covering 200+ countries and 600+ cities
» Provide the highest frequency and most up-to-
date outlook with monthly updated forecasts of
national and regional economies worldwide
» Forecast alternative macroeconomic
scenarios globally for stress testing and risk
management
» Forecast and stress test clients’ consumer credit
portfolios with customized models
6 6
Why Markets Need Economic Forecasts?
• Banks, asset managers, corporates, insures, consumers,
governments need to measure, monitor, and manage risk.
• They need to adapt to changing financial markets, meet regulatory
requirements, and make informed decisions for allocating capital
and maximizing opportunities.
• Economic forecasts provide with greater ability to make the right
choices.
• Forecasts can incorporate baseline (most likely) and alternative (can
be regulatory-driven) scenarios.
• Market participants need forecasting at the global, macro, and
regional levels to evaluate impact on their businesses.
7 7
What Forecasting Involves?
• The forecast object: event outcome, event timing, time series
forecasts.
• The statement of time series forecasting: a point forecast, an interval
forecast or the probability density forecast.
• The forecast horizon: short, medium or long-term.
• Types of forecasts: qualitative or quantitative.
• Quantitative forecasting: model-building and forecasting stages.
• Important: the assumed data-generating process is never entirely
correct!
• Determine how well model describes the underlying relationships we
are trying to model, that are hidden in the data.
8 8
Forecasting Process
Drivers Identification and In-Sample Validation
Forecasting and Out-of-Sample
Validation
Phase 1: Model Building
Use of Model for Forecasting and Stress Testing
Phase 2: Implementation
• Identify both internal
and external drivers of
main macro variables
• Involves data
preparation and
modeling
• Ensure model forecasts
observed performance
accurately by running
several out-of sample
validation exercises
• Involves modeling and
fine-tuning to improve
forecast capabilities
• Models used to generate projections
• “What if “ analysis conducted under
various policy scenarios
• Economic data and alternative forecast
scenarios leveraged for further analysis
• Results refreshed as forecasts are
updated
9 9
Macroeconomic Forecasting Model
• Forecasting external forces that affect a particular agent: forecasting
economic environments.
• Examples of macro time series: GDP and its components, labor
force and unemployment rate, prices, interest rates, etc.
• Construct a macroeconomic model that that most accurately “fits”
the given historical data and which provides reasonable forecasts of
the future based on patterns “hidden” in the data.
• Systems of equations that define the relationships between different
sectors of the economy.
• Models need to be founded on the analysis of the statistical
properties of the data under study.
10 10
Forecasting Model Design: Theory at Work…
Exchange
rates
Investment
Wages and
salaries
Popula
tion
Prices
GDP
Monetary
policy rate
Imports
Government
Exports
Global GDP
Unemployment rate
Consumption
Labor force
Employment
Potential
GDP
Other
deflators
Import
prices
10-yr
yield
Global
prices
11 11
Global Macro Forecast Coverage
Americas Asia/Pacific EMEA
• Argentina
• Brazil
• Canada
• Chile
• Colombia
• Mexico
• Peru
• United States
• Uruguay
• Venezuela
• Australia
• China
• Hong Kong
• India
• Indonesia
• Japan
• Malaysia
• New Zealand
• Philippines
• Singapore
• South Korea
• Taiwan
• Thailand
• Austria
• Belgium
• Czech Republic
• Denmark
• Finland
• France
• Germany
• Greece
• Hungary
• Ireland
• Israel
• Italy
• Luxembourg
• Netherlands
• Norway
• Poland
• Portugal
• Russia
• Slovak Republic
• Slovenia
• South Africa
• Spain
• Sweden
• Switzerland
• Turkey
• United Kingdom
Forecasting national economies that account for more than 93% of
world output
12
Source: Moody’s Analytics
Expansion
In recession
At risk
Recovery
September 2013
Global Business Cycle Status
13
Source: Moody’s Analytics
Expansion
In recession
At risk
Recovery
September 2013
Europe Business Cycle Status
14 14
Stress Testing
• Outputs of forecasting: a detailed description of the current
economic situation; a fairly detailed forecast for the near future; an
outlook beyond that with a discussion of alternative scenarios.
• Helps market participants to answer "What if?" questions enabling
improved portfolio management.
• How will I meet regulatory requirements for stress testing?
• How should I adjust lending standards if the economy goes into a
double-dip recession?
• Could my portfolio withstand a sovereign default event in Europe?
• What impact would a prolonged deflation cycle have on current and
future loan performance?
15
Alternative Macroeconomic Scenarios
Stronger Near-Term Rebound S1
S2 Mild Second Recession
S3 Deeper Second Recession
Protracted Slump S4
Baseline / Most Likely BL
Standard
Below Trend Long Term Growth S5
Oil Price Shock S6
Fed Baseline FB
Fed Adverse Scenario FA
EBA Baseline EB
EBA Adverse Scenario ES
Regulatory Driven
FSA Anchor FSA
Fed Severely Adverse FS
16 16
-5 -4 -3 -2 -1 0 1 2 3 4 5
Global
North America
South America
Asia
Mid. East & Africa
Eastern Europe
Other Europe
Euro zone
S4: Protracted slump S3: Deeper Rec. S2: Moderate Rec. Baseline
Europe Suffers Across Downside Scenarios
Real GDP, 2013, % change yr ago
Sources: National statistical offices, Moody’s Analytics
17 17
Developed Markets: Unemployment Rate
Sources: National statistical offices, Moody’s Analytics
0
5
10
15
20
25
30
35
Euro Zone Japan Germany Spain UK US
2013 baseline 2013 S4 2013 S6
% of labour force
18 18
Italy Scenario Analysis: Standard Forecasts
Sources: National statistical offices, Moody’s Analytics
19
• Swaps and Sovereign Curves (term structure)
• Stock Market Returns, Historical and Implied Volatilities
• CDS Spreads by Sector and Rating category
• Credit Migration
•
From Macro Scenarios to Market Risk
Internal Actions
Consumer Loan Data
…
Past & Future
Business Decisions
Forecasts +
Alternative Scenarios
Economic Data
…
+ External Environment
Modeling Market Risk Variables
(Interest rates)
20
Interest Rates Modeling
Past & Future
Business Decisions
Forecasts +
Alternative Scenarios
+
21
EURO Swap Rate Curves
02
46
Rat
es,%
2000m1
2001m1
2002m1
2003m1
2004m1
2005m1
2006m1
2007m1
2008m1
2009m1
2010m1
2011m1
2012m1
2013m1
Maturities: 1M-360M
Euro Swap Rates
IRS- derivative contracts, which typically exchange (swap) fixed-rate interest
payments for floating-rate interest payments. Interest Rate Swap contracts may
have a duration from 2 years to 30 years and more.
22
Stress Testing of Swap Rate Curves
-10
12
3
Term
Pre
miu
m
02
46
Rate
s (
%)
2000
m1
2002
m1
2004
m1
2006
m1
2008
m1
2012
m1
2014
m1
2016
m1
2018
m1
2010
m1
EURO Swap Curve: Baseline
-10
12
3
Te
rm P
rem
ium
02
46
Rate
s (
%)
2000
m1
2002
m1
2004
m1
2006
m1
2008
m1
2010
m1
2012
m1
2014
m1
2016
m1
2018
m1
Euro Zone Crisis
EUR Swap Curve vs Term Premium
23
Financial Models: Equity
• Stylized fact: stock index returns tend to co-move across country
• Multivariate exploratory analysis: how much of the variability of international co-
movements in global stock markets can we explain with a single component?
• Principal Component Analysis addresses this kind of question in a simple
descriptive statistics framework
• Let’s investigate stock index returns over the period 2000–2013:
Correlation Matrixukxindex sx5eindex spxindex omxindex smiindex nkyindex top40index mxwoindex obxindex
ukxindex 1
sx5eindex 0.7099 1
spxindex 0.9259 0.6663 1
omxindex 0.9381 0.5264 0.8458 1
smiindex 0.8781 0.8975 0.811 0.7789 1
nkyindex 0.666 0.9007 0.674 0.5228 0.8974 1
top40index 0.7326 0.14 0.6308 0.8337 0.4873 0.1637 1
mxwoindex 0.9415 0.7129 0.9525 0.8824 0.8812 0.7462 0.7066 1
obxindex 0.8723 0.3732 0.8011 0.9294 0.6823 0.4234 0.9474 0.8757 1
24
Financial Models: Equity
-80
-60
-40
-20
0
20
40
60
80
2002M8
2003M3
2003M10
2004M5
2004M12
2005M7
2006M2
2006M9
2007M4
2007M11
2008M6
2009M1
2009M8
2010M3
2010M10
2011M5
2011M12
2012M7
ukxindexsx5eindexspxindexomxindexsmiindexnkyindextop40indexobxindex
Because of the high cross-
correlation, we can reduce the
modelling space from the 8
original variables to 1
(unobserved) principal
component…
One single principal
component explains 91% of
the original variability!
Variability Explained, %
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8
% E
xpai
ned
by e
ach
com
pone
nt
Principal Component #
25
Financial Models: Equity
-8
-6
-4
-2
0
2
4
6
2002M8
2003M3
2003M10
2004M5
2004M12
2005M7
2006M2
2006M9
2007M4
2007M11
2008M6
2009M1
2009M8
2010M3
2010M10
2011M5
2011M12
2012M7
2013M2
2013M9
2014M4
2014M11
2015M6
2016M1
2016M8
2017M3
2017M10
2018M5
Global Equity Factor
Global_Equity_Factor_bl
Global_Equity_Factor_fsa
Global_Equity_Factor_s4
26
Financial Models: Equity
-60
-40
-20
0
20
40
60
2001M9
2002M4
2002M11
2003M6
2004M1
2004M8
2005M3
2005M10
2006M5
2006M12
2007M7
2008M2
2008M9
2009M4
2009M11
2010M6
2011M1
2011M8
2012M3
2012M10
2013M5
2013M12
2014M7
2015M2
2015M9
2016M4
Equity Forecasts, Annual Returns
tyy_ukxindex_bl
tyy_ukxindex_fsa
tyy_ukxindex_s4
-60
-40
-20
0
20
40
60
2001M9
2002M4
2002M11
2003M6
2004M1
2004M8
2005M3
2005M10
2006M5
2006M12
2007M7
2008M2
2008M9
2009M4
2009M11
2010M6
2011M1
2011M8
2012M3
2012M10
2013M5
2013M12
2014M7
2015M2
2015M9
2016M4
Equity Forecasts, Annual Returns
tyy_spxindex_bl
tyy_spxindex_fsa
tyy_spxindex_s4-60
-40
-20
0
20
40
60
2001M8
2002M3
2002M10
2003M5
2003M12
2004M7
2005M2
2005M9
2006M4
2006M11
2007M6
2008M1
2008M8
2009M3
2009M10
2010M5
2010M12
2011M7
2012M2
2012M9
2013M4
2013M11
2014M6
2015M1
2015M8
2016M3
Equity Forecasts, Annual Returns
tyy_mxwoindex_bl
tyy_mxwoindex_fsa
tyy_mxwoindex_s4
Equity Indexes, annual growth rate
History and forecasts
27 27
Summary and Conclusions
Historical data provides comprehensive understanding of macroeconomic trends, risks
and opportunities worldwide
Market participants use alternative scenarios to meet regulatory requirements, evaluate
the impact of shocks, expose vulnerabilities, and develop strategic business plans
Economic historical data, forecasts, and alternative scenarios drive key business
processes
o Stress testing asset portfolios for effective risk management and regulatory compliance
o Managing exposure to potential risks in regions with weak economic fundamentals
o Researching and analyzing investment opportunities worldwide
o Developing strategic plans, determining demand, and forecasting business lines
o Measuring the impact of shocks on businesses
Importantly one should use sensible models and the most current data from the best
national, local, and multinational sources for the foundation for forecasts and alternative
scenarios.
www.economy.com
Moody’s Analytics
Olga Loiseau-Aslanidi
Asst. Director - Economist
Email: [email protected]
THANK YOU!
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