Managing and Measuring Risk

The following sections are included:

• Foreword

• ABOUT THE EDITORS

• CONTENTS

This chapter aims to provide a conceptual framework on risk and uncertainty, with the goal of helping the reader gain a better understanding of how risk is measured and managed. Beginning with Luca Pacioli until the recent development in the discipline of risk management, the theoretical and historical framework is built around the concept of risk and uncertainty. The major contribution of the Classical Economics School is analyzed in order to investigate the relationship between profit and uncertainty. Throughout the five centuries which separate Luca Pacioli from the present, scholars have shared some common thoughts and ideas on the subject of risk and uncertainty. However several notable judgments still remain. This chapter illustrates the beliefs of Marginalists in the insignificant role of risk management in the progress of the society. As a consequence, the Marginalists, aware of the potential negative effects of including risk and uncertainty in their models, chose to avoid the problem by relegating them to simple assumptions. Later in the chapter, it is shown that in the 19th century the study of risk re-emerged drawing the attention of several scholars to uncertainty in the production process. The subsequent part of the chapter is dedicated to the systematization of the concepts developed in the last century by the respected academic scholar, Frank Knight. Since 1921, both scholars and practitioners have widely investigated the relationship between risk and return in order to determine if investors are well rewarded for the risk they retain. The final part of the chapter includes the contribution of Markowitz to the subject of portfolio selection and diversification, and that of Sharpe and Lintner to the areas of capital asset pricing. It also includes contributions of other followers framed in the modern theory of Finance. This leads us to the conclusions where risk management is shown as an independent discipline in the field of finance.

We propose a totally new approach toward assessing sovereign risk by examining rigorously the health and aggregate default risk of a nation's private corporate sector. Models such as our new Z-Metrics^TMapproach can be utilized to measure the median probability of default of the non-financial sector cumulatively for five years, both as an absolute measure of corporate risk vulnerability and a relative measure compared to other sovereigns and to the market's assessment via the credit-default-swap market. Specifically, we measure the default probabilities of listed corporate entities in nine European countries, and the U.S.A., as of 2009 and 2010. These periods coincide with the significant rise in concern with sovereign default risk in the Euro country sphere. We conclude that our corporate health index of the private sector measured at periods prior to the explicit recognition by most credit professionals, not only gave an effective early warning indicator but provided a mostly appropriate hierarchy of relative sovereign risk. Policy officials should, we believe, nurture, not penalize, the tax revenue paying and jobs generating private sector when considering austerity measures of distressed sovereigns.

The following sections are included:

• Overview

• The Dodd-Frank Wall Street Reform and Consumer Protection Act

• Evaluation of the Dodd-Frank ACT
  • Market-Based Measures of Systemic Risk
  • Interconnectedness
  • Stress Tests
  • Transparency
  • NYU Stern Systemic Risk Rankings
  • Systemic Risk Methodology
  • Systemic Risk Analysis of the Financial Crisis of 2007 to 2009

• Appendix A

• UK Banks

• European Banks

• Japanese Banks

• Insurers

• Appendix B

• Appendix C: Marginal Expected Shortfall (MES) and Supervisory Stress Test (SCAP)

• References

The following sections are included:

• Systemic Risk and the Financial Crisis of 2007 to 2009

• Regulating Systemic Risk
  • Obstacle 1: Measuring Systemic Risk
  • Obstacle 2: Implementing the Tax on Systemic Risk
  • Obstacle 3: Is Moral Hazard Solved?

• The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010
  • Measuring Systemic Risk
  • Reducing Systemic Risk
  • Mitigating Moral Hazard

• A Tax on Systemic Risk

• Summary

• References

The foreign currency market in a small open economy, like Israel, plays a major role in fiscal and monetary policy decisions, through its effects on the financial markets and the real economy. This has become even more evident in the recent financial crisis. In this paper we explore the relationship among three related foreign exchange options markets. The unique data set on OTC trading, on central bank options' auctions, in addition to the exchange traded ones provide us with insights about: (a) the effect of different financial infra-structures on prices, on limits to arbitrage, on their information content (forecasting volatility) and on the interrelationship between illiquidity and volatility, (b) the micro- structure interrelatedness between the FX spot market and the FX options market.

We investigate whether liquidity is an important price factor in the US corporate bond market. In particular, we focus on whether liquidity effects are more pronounced in periods of financial crises, especially for bonds with high credit risk, using a unique data set covering more than 20,000 bonds, between October 2004 and December 2008. We employ a wide range of liquidity measures and find that liquidity effects account for approximately 14% of the explained market-wide corporate yield spread changes. We conclude that the economic impact of the liquidity measures is significantly larger in periods of crisis, and for speculative grade bonds.

One of the ironies of the current global financial crisis is that the very innovations designed to mitigate risks in the past few decades have been blamed for causing the huge losses in wealth. But it is not the innovations that caused the cascade of losses that began in 2008; it was violations of the principles of prudent wealth management. This chapter explores two of those principles:

Principle #1: Safety First. When faced with choices involving risk, start by indentifying the safest choice and use it as a reference point for evaluating more risky choices. A risk premium can only be earned by taking genuine risk. If you choose to take risk, be prepared for the possibility that a worse outcome might occur.

Principle #2: Mark to Market. Use market prices and fair value accounting for all assets and liabilities. If an investment opportunity seems too good to be true, it is probably not true. Beware of illusory arbitrage opportunities created by use of accounting rules that violate market realities.

In the past, the U.S. financial system has been vulnerable to shocks because its accounting rules, compensation systems, and government guarantee programs have unwittingly encouraged high leverage and underfunding in too-big-to-fail financial institutions. To make the system more robust and resilient, those rules, systems, and programs have to be changed.

In our turbulent times effective risk management capabilities differentiate firms and influence performance outcomes. There is mounting recognition that effective risk management is reflected in adaptive responses that enable the firm to cope with sometimes abrupt environmental changes and not just an aim to circumvent adverse economic effects from identified hazards and market disruptions. It captures an ability to exploit opportunities as well as avoiding adverse economic effects. Hence, we use real options logic to argue that effective risk management capabilities can improve performance and is enhanced by innovation and financial slack. This proposition is examined on a sample of 896 companies. The study finds that risk management effectiveness is positively related to performance and the relationship is enhanced by investment in innovation and lower financial leverage. These results underscore the importance of innovation policies and capital structure decisions as firms deal with change and uncertainty.

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behavior of financial measures for SMEs is analyzed and the most significant variables in predicting the entities' credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on a panel of over 2,000 US firms (with sales less than $65 million) over the period 1994–2002, we develop a one-year default prediction model. This model has an out of sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model's ability to lower bank capital requirements considering the new Basel Capital Accord's rules for SMEs.

The aim of this chapter is to investigate the superiority of local modeling in the SME default risk estimation.

Both “Regional” and “national” models are developed on a dataset of 4,134 enterprises allocated into three samples: a regional “in-sample” (3,137 companies), a regional “out-of-sample” (515 companies), and a national “out-of-sample” (482 companies). By comparing the models' accuracy (ROC), our findings demonstrate the superiority of regional models for SME default risk estimation on the similar national-based models.

When geographical sampling is applied the accuracy increases in all local industry-specific models as well as in the local “general” model. In addition to higher accuracy results, the regional sampling approach made considerable simplification to the rating calibration due to the capability of the regional models to immediately adjust to the observed default rates in the region.

This chapter presents a model of systematic LGD that is simple and effective. It is simple in that it uses only parameters appearing in standard models. It is effective in that it survives statistical testing against more complicated models.

The following sections are included:

• Equity Risk Premiums: Importance and Determinants
  • Why Does the Equity Risk Premium Matter?
    • A price for risk
    • Expected returns and discount rates
    • Investment and policy implications
  • What are the Determinants of Equity Risk Premiums?
    • Risk aversion and consumption preferences
    • Economic risk
    • Information
    • Liquidity
    • Catastrophic risk
    • Government policy
    • The behavioral/irrational component
  • The Equity Risk Premium Puzzle

• Estimation Approaches
  • Survey Premiums
    • Investors
    • Managers
    • Academics
  • Academics
    • Estimation questions and consequences

• Estimates for the United States
  • • Global estimates
    • The survivor bias
  • Historical Premium Plus
    • Small cap and other risk premiums
    • The CAPM and market capitalization
    • The Small Cap Premium
    • Perils of the approach
    • Country risk premiums
    • The arguments for no country risk premium
    • The arguments for a country risk premium
    • Estimating a Country Risk Premium
    • Measuring Country Risk
    • Choosing between the approaches
  • Implied Equity Premiums
    • A Stable Growth DDM Premium
    • A Generalized Model: Implied Equity Risk Premium
    • Implied Equity Risk Premium: S&P 500
    • Implied Equity Risk Premiums: Annual Estimates from 2008 to 2012
    • A Term Structure for Equity Risk Premiums?
    • Time Series Behavior for S&P 500 Implied Premium
    • Implied Equity Risk Premiums during a Market Crisis and Beyond
    • Determinants of Implied Premiums
    • Implied ERP and Interest rates
    • Implied ERP and Macroeconomic variables
    • Implied ERP, Earnings Yields and Dividend Yields
    • Implied ERP and Technical Indicators
    • Other Equity Markets
    • Sector premiums
    • Firm Characteristics

• Choosing an Equity Risk Premium
  • Why Do the Approaches Yield Different Values?
  • Which Approach is the “Best” Approach?
  • Five Myths About Equity Risk Premiums

• Summary

• References

We investigate the stock market crashes in China, Iceland, and the US in the 2007–2009 period. The bond stock earnings yield difference model is used as a prediction tool. Historically, when the measure is too high, meaning that long bond interest rates are too high relative to the trailing earnings over price ratio, then there usually is a crash of 10% or more within four to twelve months. The model did in fact predict all three crashes. Iceland had a drop of fully 95%, China fell by two thirds and the US by 57%.

The following sections are included:

• ABOUT THE RISK, BANKING AND FINANCE SOCIETY

• ABOUT THE INTERNATIONAL RISK MANAGEMENT CONFERENCE