Download pdf - Managing Forward Curves in a Complex Market

Managing Forward Curves in a Complex Market

WHITE PAPER

Sponsored by

© Commodity Technology Advisory LLC, 2014

Introduction

Commodity prices are constantly changing and are driven by market forces that are virtually impossible to predict with any degree of certainty. As such, accurately forecasting costs and price exposures is difficult at best, and particularly so now, given the rapidly changing supply and demand patterns that define the global commodity complex. Huge growth in demand for all commodities in Asia, the rapid rise of agricultural exports from developing countries in the Asia-Pac region, and the shale revolution that is driving unprecedented growth in US oil produc-tion, are all examples of the new dynamics that have fundamentally altered price formation in markets around the world. In this globalized and increasingly interconnected market-place, which is being con-stantly buffeted by economic uncertainty, predicting future prices is more difficult, but perhaps more im-portant, than ever. Along with ever shifting supply and demand patterns, new markets, trading hubs, and storage facilities have opened, creating new trading loca-tions where none existed just a few short years ago. Though many have already become recognized pric-ing centers, others are, and continue to be, rather illiquid, with few transactions and little knowledge in the broader market as how to price those locations on a future basis. Even in areas and markets that have had a long and sustained history of prices, new productive re-gions (such the massive growth in natural gas pro-duction from the Marcellus Shale in the Northeast US, for example) can create a lasting and dramatic

change in futures prices. Future price prediction then becomes difficult as the sudden change in fundamentals produces prices that are uncor-related from historical activity. With these market changes, the ability to interpret market activity and measure the future impact of anticipated developments becomes more imperative. Defaulting to a common exchange price curve or at-tempting to simply project historical prices forward is insufficient in this dynamic environment as it ignores the both the global impact of chang-ing supply and demand patterns and the growing inter-relationships amongst commodities and markets. While some wholesale spot markets that trade on exchanges, such as Henry Hub’s natural gas contract, are well established, highly liquid and somewhat seasonally predictable, the majority of commodity trading locations and markets around the globe are not, and exchange data is either not directly reflective or is unreliable. It’s these imperfect, inefficient and sometimes insufficiently liquid wholesale spot markets where the need for careful and thoughtful modeling of future prices, or the “forward curve”, becomes an essential exercise in risk management and financial reporting for commodity trading companies. In this paper, we’ll examine the complexities associated with the development, and the specific uses, of forward price curves. In addi-tion, we’ll review a sophisticated technology available from DataGenic – the Genic CurveBuilder - that can automate and reduce the complexity associated with the development of forward price curves.

Any company that owns commodities, either through production or merchant activities, needs to know not only the current value of those commodities based on market prices, but also needs to develop a view of the future value of those commodities during the time that they are projected to be held in inventory. Additionally, agreements to purchase commodities in the future must be accounted for, not only at their agreed or projected purchase price, but also during their anticipated holding period.

© Commodity Technology Advisory LLC, 2015, All Rights Reserved.

A ComTech Advisory WhitepaperManaging Forward Curves in a Complex Market

FORWARD CURVES DEFINED

USES OF FORWARD CURVES

The term “forward curve” refers to a series of sequential prices either for future delivery of an asset or expected future settlements of an index. Established futures markets, such as the NYMEX Henry Hub natural gas contract, provide a series of future month contracts which are traded for fixed prices. These published future month prices take on a curve shape when graphed, and are thus referred to as the “forward curve”.

A forward curve can be derived for any commodity with a forward delivery market; however, the accu-racy and completeness of that curve is going to de-pend on a number of factors, and primarily on the liquidity of each forward period. Unfortunately, given

The predominant use of forward curves is in the preparation of corporate financial statements. Com-panies will use forward curves as key inputs to deriv-ative valuation models in order to calculate the fair value of commodity inventories or financial instru-ments that are carried on the balance sheet. For US-based, public companies that operate under the oversight of the Securities and Exchange Commission (SEC), this valuation activity is gov-erned by GAAP, and specifically ASC Topic 820 (formerly, SFAS-57). Amongst its requirements, Topic 820 states that companies should use mar-ket-based price inputs and should disclose the re-liability of those inputs. Input reliability is classified as either “level 1” (unadjusted quotes from active markets), “level 2” (quotes from inactive markets or markets for similar instruments), or “level 3” (price inputs based on management assumptions). These reliability level requirements often mean that compa-nies must use the most active market quotes, even

The second common use of forward curves is in asset valuation for ei-ther planning purposes or dynamic hedging. As these valuations are not part of, or included, in the preparation of financial statement, com-panies may use something other than exchange-based curves. This is especially helpful in cases where the operating characteristics of a par-ticular asset are more granular than available market quotes; that is, they operate in a market or region not directly traded or otherwise well reflected by an exchange instrument. In this case, using derived curves would provide the asset holder with a better estimate of the asset’s cur-rent and future value. With an improved estimate of the asset’s value, the asset holder would be in a better position to manage the asset’s net risk via pro-duction or fuels hedging, or operational adjustments to maximize val-ue. However, again, it’s important to remember that such price curves would not meet GAAP definitions for input price reliability.

that most markets and/exchanges do not exhibit high liquidity in all fu-ture periods, it is generally best practice to derive the curve from many sources of market data – including exchanges, broker marks, trader indications and independent data publishers.

FINANCIAL STATEMENTS AND FORWARD CURVES

ASSET VALUATION

in instances where those markets are quoted as strips as opposed to individual months. Accidentally using lower-level price inputs or misrepresenting the reliability of price inputs may put the company at risk of re-statement in future periods; and in the process, bring increased scrutiny of their accounting and management practices by regulators and shareholders.



RISK MANAGEMENT AND REPORTING

In addition, settlement data from an exchange will be limited to transactions that have been executed across that platform and the “accuracy” of that data may be constrained by the liquidity in those markets – the fewer the deals consummated at any particular mar-ket point, the greater the impact an anomalous trade will have in establishing the published price. Further, should a trade not be consummated for a particular market point in any given period, the exchange will still “settle” its open interest using a formula-based ap-proach in order to keep margin accounts in balance. Clearly, simply relying on exchange data to es-tablish forward prices may be insufficient, particularly for illiquid points or markets. So, in order to meet a company’s requirement for forward curves reflective of their markets and curve usage, more robust curves can be internally developed against independent mar-ket data aggregated from multiple sources.

A third common use for forward curves is in risk man-agement and reporting; and for these purposes, prac-tices can vary widely amongst market participants. Some companies may wish to have Value-at-Risk measurements and limits-monitoring processes match observable market data regardless of granu-larity. In this case, an exchange-based curve source will likely be the best option for forward curve devel-opment. Other companies may wish to apply liquidity and seasonality adjustments if they believe those practic-

SOURCES OF FORWARD CURVE DATA

There are many choices available to market participants seeking forward curve data sources. The most common sources are exchanges, brokers, data publishers, data distributors, ETRM system vendors, and in-ternally-developed models. It is important to understand your company’s intended use of any particular forward curve in order to select the appro-priate sources and methodologies for deriving those curves. It is also key to understand the limitations and methodologies inherent in each of the selected data sources. Internally modeled curves may be the only option where reliable market data does not exist (e.g. illiquid points and tenors). In these cir-cumstances, the forward curve’s quality is highly dependent on the qual-ity of the market data inputs, modeling assumptions and methodology. Whenever internally modeled curves are used, calibration and back-test-ing should be done regularly to validate the quality of the curve and its assumptions. Additionally, when possible, internally developed curves should be compared to independently modeled curves for further valida-tion.

es provide a more nuanced view of firm risk. For these companies, the use of non-exchange sources, in addition to exchange data, may provide them with the better fit curves that reflect their operations and risk port-folio. Regardless of which situation a company finds themselves, best risk practices dictate that a curve validation process is used in which independent forward curve data is compared to the forward curves that they use for financial reporting, risk measurement, and risk reporting. Companies that utilize forward curves derived from multiple sources, or with internally-developed adjustments, should enshrine a regular testing of those curves and adjustments as part of their risk management poli-cies. And most critically, all forward curve information should be archived indefinitely for audit and compliance purposes.

As previously noted, the data used for the construction of forward curves will likely differ, sometimes dramatically, between different sources. Market activity, knowledge, and insights available to those different sources will impact their views of value of the commodity in the future. Different sources may, and usually will, provide a different set of periods over different time horizons. For example, an exchange may have monthly contracts that will extend for ten years, while an over-the-counter broker may quote future prices as multi-month strips that extend over a period of 5 years.



UNLIMITED CURVE BUILDINGFLEXIBILITY

DATAGENIC FORWARD CURVE SOLUTION - GENIC CURVEBUILDERGiven that OTC and spot markets can be volatile, illiquid and inefficient, the need for careful and detailed modelling of forward prices is an essential aspect of risk management in the industry. Even when curves are available from exchanges, brokers, or published, often they will not match your company’s needs for achieving accurate mark-to-market, value at risk and portfolio optimisation calculations. Genic CurveBuilder provides automated generation of fully customized forward curves based on your choice of source data coupled with rules that you define.

Genic CurveBuilder is an intelligent, fully automat-ed, powerful and flexible forward curve builder and price data management application. Utilizing built-in artificial intelligence, this SMART application offers complete flexibility that goes well beyond standard curve configuration. Modelling definitions include basis and arbitrage-free calculations, interpolation

and extrapolation, shaping and smoothing, flexible tenor specification, prioritization and weighting. For the simple to the most complex curves, a rules-based frame-work offers unlimited flexibility in the creation of the curves. Using a de-finable English language-based logic, all rules are then interpreted auto-matically using an expert system. Rules can be expanded and re-applied to other curves. The process for curve building can be data event driven or scheduled, allowing for end-of-day and real-time creation. Contract rollover calendars along with holiday calendars are utilized to ensure accurate market condition modelling.



IN-MEMORY CURVE BUILDING

FORWARD CURVE REAL-TIME MONITOR

FORWARD CURVE SECURITY

Performance can mean the difference between suc-cess and failure in building forward curves. Genic CurveBuilder uses ‘in-memory’ processing to speed up calculations and processing time, thereby reduc-ing data access delays. Curves are built in rapid time ensuring the end-users and systems have immediate and correct access to information required for rapid decision making using real-time curve building.

The Genic CurveBuilder provides interactive real-time monitoring and visualisation with pro-active alerts for monitoring the curve build pro-cesses. Users can quickly assess the business impact and take imme-diate corrective action.

Security should be robust not complex. With Genic CurveBuilder you get a role based security access control segregated into resource lev-el and workgroup level coupled with a data encryption security layer, using single ‘sign-on’ authentication (Active Directory). You can be as-sured on the protection of your data assets.

SUMMARYDeveloping and managing forward curves is a chal-lenge in any environment – selecting the appropri-ate data sources that meet your particular curve use case or application; developing appropriate curve adjustments to meet your particular market, location or asset; and ensuring appropriate controls and con-stant testing can be a complex exercise. However, in a market that is constantly hammered by economic uncertainty, rapidly changing supply and demand patterns, and intense regulatory scrutiny, managing the complexities associated with curve development and maintenance becomes even more difficult, and even more critical. In this environment, spreadsheets or simplistic models are insufficient as they are prone to errors and lack the necessary sophistication to perform the

complex multi-commodity, multi-dimensional analysis that is required in a globally integrated marketplace. Without a dedicated curve develop-ment and management solution, like the Genic CurveBuilder from Dat-aGenic, gaining accurate insights and ensuring proper financial report-ing and risk management of trading and production assets becomes a tenuous proposition at best; and at worst, may actually increase the risk of financial loss, shareholder dissatisfaction and regulatory scrutiny of your operations.

CURVE VISUALISATION & ANALYSISWorking with forward curves can require being in-teractive. Being able to visualise market dynamics, can instantly help the business or manager to rapidly process multiple complex data configurations. Genic CurveBuilder provides visualisation and analysis of curves within different curve structure views and sources and with pre-formed reports and options.

DataGenic is the leading global provider of on-premise and in-cloud Smart Commodity Data Management software, delivering intelligent analytics, real-time data content and proven business value.

The innovative solutions include a data-agnostic multi-commodity data management platform, visual mapping and management of business processes, extensive and extensible data quality management, unlimited forward curves construction and an intelligent decision framework. DataGenic customers include participants in the energy, metals, minerals, chemicals, agriculture, shipping and food and beverage industries.

DataGenic operates in Europe, Asia and the Americas.

For more information, please contact DataGenic at:

Tel: +44 203 651 5560 or +1 281 810 8290

[email protected]

ABOUT DATAGENIC LTD

ABOUT

Commodity Technology Advisory LLCCommodity Technology Advisory is the leading analyst organization covering the ETRM and CTRM markets. We provide the invaluable insights into the issues and trends affecting the users and providers of the technologies that are crucial for success in the constantly evolving global commodities markets.

Patrick Reames and Gary Vasey head our team, whose combined 60-plus years in the energy and commodities markets, provides depth of understanding of the market and its issues that is unmatched and unrivaled by any analyst group.

For more information, please visit:

ComTech Advisory also hosts the CTRMCenter, your online portal with news and views about commodity markets and technology as well as a comprehensive online directory of software and services providers. Please visit the CTRMCenter at:

19901 Southwest FreewaySugar Land TX 77479+1 281 207 5412

Prague, Czech Republic+420 775 718 112

ComTechAdvisory.comEmail : [email protected]

www.comtechadvisory.com

www.ctrmcenter.com