In this paper we show how to formulate and solve robust portfolio selection problems. The objective of these robust formulations is to systematically combat the sensitivity of the optimal portfolio to statistical and modeling errors in the estimates of the relevant market parameters. We introduce "uncertainty structures" for the market parameters and show that the robust portfolio selection problems corresponding to these uncertainty structures can be reformulated as second-order cone programs and, therefore, the computational effort required to solve them is comparable to that required for solving convex quadratic programs. Moreover, we show that these uncertainty structures correspond to confidence regions associated with the statistical procedures used to estimate the market parameters. We demonstrate a simple recipe for efficiently computing robust portfolios given raw market data and a desired level of confidence.

We estimate and test the conditional version of an International Capital Asset Pricing Model using a parsimonious multivariate GARCH process. Since our approach is fully parametric, we can recover any quantity that is a function of the first two conditional moments. Our findings strongly support a model which includes both market and foreign exchange risk. However, both sources of risk are only detected when their prices are allowed to change over time. The evidence also indicates that, with the exception of the U.S. equity market, the premium for bearing currency risk often represents a significant

Hedge funds are known to exhibit non-linear option-like exposures to standard asset classes and therefore the traditional linear factor model provides limited help in capturing their risk-return tradeoffs. We address this problem by augmenting the traditional model with option-based risk factors. Our results show that a large number of equity-oriented hedge fund strategies exhibit payoffs resembling a short position in a put option on the market index, and therefore bear significant left-tail risk, risk that is ignored by the commonly used mean-variance framework. Using a mean-conditional Value-at-Risk framework, we demonstrate the extent to which the mean-variance framework underestimates the tail risk. Working with the underlying systematic

We investigate the portfolio choices of mean-variance-optimizing investors who use sample evidence to update prior beliefs centered on either risk-based or characteristic-based pricing models. With dogmatic beliefs in such models and an unconstrained ratio of position size to capital, optimal portfolios can differ across models to economically significant degrees. The differences are substantially reduced by modest uncertainty about the models ’ pricing abilities. When the ratio of position size to capital is subject to realistic constraints, the differences in portfolios across models become even less important, nonexistent in some cases.

I review empirical research on the relation between capital markets and financial statements.The principal sources of demand for capital markets research in accounting are fundamental analysis and valuation, tests of market efficiency, and the role of accounting numbers in contracts and the political process.The capital markets research topics of current interest to researchers include tests of market efficiency with respect to accounting information, fundamental analysis, and value relevance of financial reporting.Evidence from research on these topics is likely to be helpful in capital market investment decisions, accounting standard setting, and corporate financial

: We aim to provide insight into the observed equity home bias phenomenon by analyzing the determinants of U.S. holdings of equities across a wide range of countries. In particular, we explore the role of information costs in determining the country distribution of U.S. investors' equity holdings using a comprehensive new data set on U.S. ownership of foreign stocks. We find that U.S. holdings of a country's equities are positively related to the share of that country's stock market that is listed on U.S. exchanges, even after controlling for capital controls, trade links, transaction costs, and historical risk-adjusted returns. We attribute this finding to the fact that foreign firms that list on U.S. exchanges are obliged to provide standardized, credible financial information, thereby reducing information costs incurred by U.S. investors. This obligation stems from U.S. investor protection regulations, which include stringent disclosure requirements, reconciliation of financial stat...

This article presents evidence on persistence in the relative investment performance of large, institutional equity managers. Similar to existing evidence for mutual funds, we find persistent performance concentrated in the managers with poor prior-period performance measures. A conditional approach, using time-varying measures of risk and abnormal performance, is better able to detect this persistence and to predict the future performance of the funds than are traditional methods.

INTRODUCTION The CBOE Market Volatility Index (VIX) is an average of S&P 100 option (OEX) implied volatilities. As such, it represents a market- consensus estimate of future stock market volatility. 1 The computation and dissemination of VIX on a real-time basis offers practitioners and academics an important new source of information. Practitioners, for This research was supported by the Futures and Options Research Center at the Fuqua School of Business, Duke University. We gratefully acknowledge the helpful comments and suggestions of Fischer Black, Mark Rubinstein, and two anonymous referees. We also thank participants at the University of Pennsylvania, the University of Texas at Dallas, and the University of Waterloo/KPMG Peat Marwick Thorne seminars, as well as attendees of the 1993 Conference on Financial Innovation: 20 Years of Black/Scholes and Merton (Duke University) and the 1994 Berkeley Program in Finance, Ojai Valley, California. Since OEX options are the mos

Abstract: We investigate the effect of scale on performance in the active money management industry. We first document that fund returns, both before and after fees and expenses, decline with lagged fund size, even after adjusting these returns by various performance benchmarks. We then explore a number of potential explanations for this relationship. We find that this relationship is most pronounced among funds that have to invest in small and illiquid stocks, which suggests that the adverse effects of scale are related to liquidity. Controlling for its size, a fund’s performance actually increases with the asset base of the other funds in the family that the fund belongs to. This suggests that scale need not be bad for fund returns depending on how the fund is organized. Finally, we explore the idea that scale erodes fund performance because of the interaction of liquidity and organizational diseconomies.

An integrated econometric view of maximum likelihood methods and more traditional two-pass approaches to estimating beta-pricing models is presented. Several aspects of the well-known “errors-in-variables problem ” are considered, and an earlier conjecture concerning the merits of simultaneous estimation of beta and price of risk parameters is evaluated. The traditional inference procedure is found, under standard assumptions, to overstate the precision of price of risk estimates and an asymptotically valid correction is derived. Modifications to accommodate serial correlation in market-wide factors are also discussed Sharpe (1964) and Lintner (1965) demonstrate that, in equilibrium, a financial asset’s expected return must be positively linearly related to its “beta, ” a measure of systematic risk or co-movement with the market portfolio return: 1 This article is an extension of the second chapter of my doctoral dissertation at Carnegie Mellon University. Recent versions were presented in seminars