Rating Banks in Emerging Markets: What Credit Rating Agencies Should Learn From Financial Indicators
by Liliana Rojas-Suarez
Institute for International Economics

Abstract
The rating agencies’ and bank supervisors’ records of prompt identification of banking problems in emerging markets has not been satisfactory. This paper suggests that such deficiencies could be explained by the use of financial indicators that, while appropriate for industrial countries, do not work in emerging
markets. Among the conclusions, this paper shows that the most commonly used indicator of banking problems in industrial countries, the capital-to-asset ratio, has performed poorly as an indicator of banking problems in Latin America and East Asia...

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Rational prepayment and the valuation of mortgage-backed securities
by R. Stanton
Haas School of Business, University of California, Berkeley
1995

Abstract
This article presents a new model of mortgage prepayments, based on rational decisions by mortgage holders. These mortgage holders face heterogeneous transaction costs, which are explicitly modeled. The model is estimated using a version of Hansen's (1982) generalized method of moments, and is shown to capture many of the empirical features of mortgage prepayment...

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Real Interest Rates and the Default Rate on High-Yield Bonds
by Martin Fridson, Christopher Garman, & Sheng Wu
Merrill Lynch & Co.
Fall, 1997

Abstract
Determinants of the aggregate default rate on high-yield bonds have been of interest at least since 1990 - 1991, when the proportion of defaulting issued reached effectively its highest level since the Great Depression. With recovery by the mid-19902, and a new record volume of issuance in 1993, analysts have wondered whether another wave of financial failures will follow. This research shows that nominal interest rates are not highly correlated with aggregate default rates on high-yield bonds...

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Realized Beta: Persistence and Predictability
by Torben G. Andersen, Tim Bollerslev, Francis X. Diebold, and Jin Wu
January, 2003

Abstract
A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a model of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent, and arguably best modeled as a standard stationary I(0) process...

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Regulatory Evaluation of Value-at-Risk Models
by Jose A. Lopez
Federal Reserve Bank of San Francisco
June 30, 1999

Abstract
Beginning in 1998, U.S. commercial banks may determine their regulatory capital
requirements for financial market risk exposure using value-at-risk (VaR) models. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial, interval forecast and distribution forecast methods. Given the low power often exhibited by their corresponding hypothesis tests, these methods can often misclassify forecasts from inaccurate models as acceptably accurate. An alternative evaluation method using loss functions based on probability forecasts is proposed. Simulation results indicate that this method is only as capable of differentiating between forecasts from accurate and inaccurate models as the other methods. However, its ability to directly incorporate regulatory loss functions into model evaluations make it a useful complement to the current regulatory evaluation of VaR models.

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The Relative Efficiency of Beta Estimates (Working Paper)
by Jan Bartholdy & Paula Peare
Aarhus School of Business
March, 2001

Abstract
When estimation of beta is based on the Capital Asset Pricing Model the standard
recommendation is to use five years of monthly data and a value-weighted index.
Given the importance of the beta estimate obtained for financial decisions, such as those involved in portfolio management, capital budgeting, and performance
evaluation, there is surprisingly little research evidence in support of this
recommendation. The objective of this paper is to address this shortcoming. For this purpose the relative efficiency of beta estimates which result from using different data frequencies, time periods, and indexes is examined. It is found that five years of monthly data and an equal-weighted index, as opposed to the commonly recommended value-weighted index, provides the most efficient estimate.

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The Relevance of Interest Rate Processes in Pricing Mortgage-Backed Securities
by Ren-Raw Chen & T.L. Tyler Yang
1995

Abstract
This article examines how different interest rate processes affect the pricing of fixed-income securities. Specifically, it explores four widely used interest rate processes: (1) the Ornstein-Uhlenbeck process, (2) the mean-reverting square-root process, (3) the log-normal process, and (4) the Ho-Lee discrete binomial process. After comparing the mean square errors for prices of Treasury bonds and Goverment National Mortgage Association (GNMA) prices, it appears that the ability to fit the initial term structure is the most important characteristic of a sound interest rate process. In general, the mean square errors of GNMA prices are greater than those of Treasury bonds owing to additional errors in fitting the prepayment function...

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Research in Emerging Markets Finance: Looking to the Future
by Geert Bekaert & Campbell R. Harvey
Columbia University, Duke University, & National Bureau of Economic Research
September 11, 2002

Abstract
Much has been learned about emerging markets finance over the past 20 years. These markets have attracted a unique interdisciplinary interest that bridges both investment and corporate finance with international economics, development economics, law, demographics and political science. Our paper focuses on the research areas that are ripe for exploration.

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Results on the Standard Error of the Coefficient Alpha Index of Reliability
by Adam Duhachek, Anne T. Coughlan, & Dawn Iacobucci
2005

Abstract
In this research, we investigate the behavior of Cronbach’s coefficient alpha and its new standard error. We systematically analyze the effects of sample size, scale length, strength of item intercorrelations, and scale dimensionality. We demonstrate the beneficial effects of sample size on alpha’s standard error and of scale length and the strengths of item intercorrelations (effects that are substitutes in their benefits) on both alpha and its standard error. Our findings also speak to this adage: Heterogeneity within the item covariance matrix
(e.g. through multidimensionality or poor items) negatively impacts reliability by decreasing the precision of the estimation. We also examined the question of “equilibrium ”scale length, showing the conditions for which it is optimal to add no items, or one, or multiple items to a scale. In terms of “best practices,” we recommend that researchers report a confidence interval or standard error along with the coefficient alpha point estimate.

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Return Distributions of Private Real Estate Investments
by Roger Jelks Brown
The Pennsylvania State University - The Graduate School
Department of Insurance and Real Estate
May, 2000

Abstract
This dissertation investigates the distribution of returns accruing to individual
owners of investment real estate property. Previously, most research in investment real estate concentrated upon large institutional owners using finance paradigms, tools and methodology.
Other researchers have questioned the use of finance models, predominantly
Modern Portfolio Theory (MPT), for real estate research...

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Risk2: Measuring the Risk in Value at Risk
by Philippe Jorion
December, 1996

Abstract
Jorion shows how value at risk (VAR), a measure of worst-case loss for a derivatives position, is itself subject to estimation risk. He evaluates two methods of estimation, the sample quantile method and the sample standard deviation method. A more precise VAR measure is obtained when derived as a weighted function of the standard deviation of portfolio value...

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Risk-Adjusted Performance Measures and Implied Risk-Attitudes
by Auke Plantinga & Sebastiaan De Groot
DePaul University & EIM Management (USA) Inc.

Abstract
In this article we study the relation between performance measures and preferences functions. In particular, we examine to what extent performance measures can be used as alternatives for preference functions. We study the Sharpe ratio, Sharpe's alpha, the expected return measure, the Sortino ratio, the Fouse index, and the upside potential ratio...

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Risk Arbitrage Performance for Stock Swap Offers with Collars
by Ben Branch & Jia Wang
University of Massachusetts at Amherst

Abstract
Herein we investigate the risk return characteristics of risk arbitrage for a sample
of 187 stock swap offers in the form of collars for the 1994-2003 period. Using cross sectional analysis, we find that arbitrage spreads, defined as the percentage difference between the offer price and the target market price after the merger announcement, are significantly positively correlated with the acquirer’s stock volatility and the deal duration. We also find that arbitrage spreads are significantly lower for successful deals than for failed deals; lower for challenged deals than for unchallenged deals. Using time series analysis, we identify a significant non-linear relationship in the risk return profile for risk arbitrage portfolios: Both strategy I (long the target for the fixed value collar offers; long the target and short the acquirer for the fixed ratio collar offers) and strategy II (delta hedging) produce returns that are strongly positively correlated with the market return in a severely declining market and are not significantly correlated with the market return in a flat or rising market. Given the nonlinear payoff pattern, linear asset pricing models tend to mis-estimate the magnitude of excess returns. For strategy I, our samples produced an estimated annual excess return of 6.3% when we used contingent claims analysis as our base model. This estimated excess return level is less than the 11.88% return when CAPM, or the 10.27% return when Fama-French three factor models are assumed to be the base model. For strategy II, assuming contingent claims analysis as our base model produced an annual excess return of 22.7%. Using CAPM and Fama-French
models produced annual excess returns of 9.25% and 8.6% respectively for our
sample of collar mergers.

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Risk Arbitrage Profits and the Probability of Takeover Success
by Ben Branch, Huong Ngo Higgins, & Kathryn Wilkens
University of Massachusetts at Amherst & Worcester Polytechnic Institute
January, 2003

Abstract
Prior literature shows that relative firm size, low firm leverage, and relatively low growth can increase a firm's chance of becoming a takeover target and increase the probability of merger success. We show that adding information about analyst coverage significantly improves models of merger success based on financial variables that proxy for firm size, leverage, and growth. The probability of merger success is inversely related to analyst coverage at the time of the announcement...

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Risk Arbitrage in U.S. Financial Markets
by Supreena Narayanan
Stockholm School of Economics
2004

Abstract
This paper analyses risk arbitrage in U.S. financial markets. The study by Mitchell, Mark and Todd Pulvino (2001) has been extended to study the U.S. financial markets scenario from 1963 to 2004. In particular, two research questions are pursued. What are the effects of stock market, business conditions as well as the Merger and Acquisition Trend on risk arbitrage activities in the U.S...

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The Risk in Fixed-Income Hedge Fund Styles
by William Fung & David A. Hsieh
London Business School & Duke University
August, 2002

Abstract
This paper studies the risk in fixed-income hedge fund styles. Principal component analysis is applied to groups of fixed-income hedge funds to extract common sources of risk and return. These common sources of risk are related to market risk factors, such as changes in interest rate spreads and options on interest rate spreads...

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Risk in Hedge Fund Strategies: Case of Convertible Arbitrage
by Vikas Agarwal, William H. Fung, Yee Cheng Loon, & Narayan Y. Naik
Georgia State University & London Business School
September 16, 2004

Abstract
Using data on Japanese and US convertible bonds and underlying stocks, we analyze the risk-return characteristics of convertible arbitrage funds. We hypothesize that there are three primitive trading strategies that explain convertible arbitrage funds’ returns – positive carry, volatility arbitrage, and credit arbitrage. We refer to these as asset-based style (“ABS”) factors...

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The Risk in Hedge Fund Strategies: Theory & Evidence from Trend Followers
by William Fung & David A. Hsieh
PI Asset Management, LLC & Duke University
2001

Abstract
Hedge fund strategies typically generate option-like returns. Linear-factor models using benchmark asset indices have difficulty explaining them. Following the suggestions in Glosten and Jagannarthan (1994), this article shows how to model hedge fund returns by focusing on the popular "trend-following" strategy...

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Risk Management for Hedge Funds: Introduction and Overview
by Andrew W. Lo
Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER)
June 7, 2001

Abstract
Although risk management has been a well-ploughed field in financial modeling for over two decades, traditional risk management tools such as mean-variance analysis, beta, and Value-at-Risk do not capture many of the risk exposures of hedge-fund investments. In this article, I review several aspects of risk management that are unique to hedge funds - survivorship bias, dynamic risk analytics, liquidity, and nonlinearities - and provide examples that illustrate their potential importance to hedge-fund managers and investors. I propose a research agenda for developing a new set of risk analytics specifically designed for hedge-fund investments, with the ultimate goal of creating risk transparency while, at the same time, protecting the proprietary nature of hedge-fund investment strategies...

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Risk Measurement: An Introduction to Value at Risk
by Thomas J. Linsmeier & Neil D. Pearson
University of Illinois at Urbana-Champaign
January, 1999

Abstract
This paper is a self-contained introduction to the concept and methodology of “value at risk,” which is a new tool for measuring an entity’s exposure to market risk. We explain the concept of value at risk, and then describe in detail the three methods for computing it: historical simulation; the variance-covariance method; and Monte Carlo or stochastic simulation. We then discuss the advantages and disadvantages of the three methods for computing value at risk...

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Risk Preference Estimation in the Non-Linear Mean Standard Deviation Approach
by Atanu Saha

Abstract
Risk preferences and technology are jointly estimated in the nonlinear mean-standard deviation framework for a competitive firm model under price risk. A utility function is proposed that nests various risk preference structures and risk neutrality as empirically refutable special cases. The empirical application using firm-level data finds evidence of decreasing absolute risk aversion, differences in the nature of relative risk aversion by firm size, and little support for the widely used linear mean-variance framework...

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RISK AND RETURN IN FIXED INCOME ARBITRAGE: NICKELS IN FRONT OF A STEAMROLLER?
by Jun Liu & Francis A. Longstaff
UCLA Anderson School & The NBER
April, 2004

Abstract
We conduct an analysis of the risk and return characteristics of fixed income arbi-
trage. We show that a widely-used fixed income arbitrage strategy based on swap spreads generates sizable positive excess returns over an extended period. We find, however, that most of these excess returns represent compensation for risk; there is very little “arbitrage” in this fixed income arbitrage strategy...

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The Risks Underlying Hedge Funds Strategies
by Maha Soueissy & Rami Sidani
Universite de Lausanne
December, 2003

Abstract
This paper establishes a theoretical risk matrix based on 23 different hedge funds
strategies and their underlying risk factors. It studies in parallel the impact of some of the major market crises of the last decade on ten of the largest hedge funds strategies. Dominant risks are identified for each crisis and each hedge fund style and put into matrices...

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Robust Tests of Market Efficiency using Statistical Arbitrage
by Melvyn Teo, Yiu Kuen Tse, & Mitch Warachka
Singapore Management University
April, 2004

Abstract
This paper develops robust tests of market efficiency using statistical arbitrage which circumvent the joint-hypotheses dilemma confounding the traditional literature. Hogan, Jarrow, Teo and Warachka (2004) identify statistical arbitrage opportunities in momentum and value strategies. However, their results are sensitive to the assumption that expected incremental trading profits are constant...

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An R-squared measure of goodness of fit for some common nonlinear regression models
by A. Colin Cameron & Frank A.G. Windmeijer
University of California - Davis & University College London
March 31, 1995

Abstract
For regression models other than the linear model, R-squared type goodness-of-fit summary statistics have been constructed for particular models using a variety of methods. We propose an R-squared measure of goodness of fit for the class of
exponential family regression models, which includes logit, probit, Poisson, geometric, gamma and exponential. This R-squared is defined as the proportionate reduction in uncertainty, measured by Kullback-Leibler divergence, due to the inclusion of regressors...

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R-squared and prediction in regression with ordered quantitative response
by Diane Dancer & Andrew Tremayne
University of Sydney, Australia & University of York, UK
July, 2005

Abstract
This paper is concerned with the use of regression methods to predict values of a response variable when that variable is naturally ordered. An application to the prediction of student examination performance is provided and it is argued that, although individual scores are unlikely to be well predicted at the extremes of the range using the conditional mean, conditional on covariates, it is possible to usefully predict where an individual is likely to feature in the rank order of performance...

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