nep-rmg New Economics Papers
on Risk Management
Issue of 2007‒01‒28
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Risk, Return and Dividends By Andrew Ang; Jun Liu
  2. Phase-Locking and Switching Volatility in Hedge Funds By Monica Billio; Mila Getmansky; Loriana Pelizzon
  3. Predictability in Financial Markets: What Do Survey Expectations Tell Us? By Philippe Bacchetta; Elmar Mertens; Eric van Wincoop
  4. A generalized Dynamic Conditional Correlation Model for Portfolio Risk Evaluation By Monica Billio; Massimiliano Caporin
  5. A Data-Driven Optimization Heuristic for Downside Risk Minimization By Manfred Gilli; Evis Këllezi; Hilda Hysi

  1. By: Andrew Ang; Jun Liu
    Abstract: We characterize the joint dynamics of dividends, expected returns, stochastic volatility, and prices. In particular, with a given dividend process, one of the processes of the expected return, the stock volatility, or the price-dividend ratio fully determines the other two. For example, together with dividends, the stock volatility process fully determines the dynamics of the expected return and the price-dividend ratio. By parameterizing one or more of expected returns, volatility, or prices, common empirical specifications place strong, and sometimes counter-factual, restrictions on the dynamics of the other variables. Our relations are useful for understanding the risk-return trade-off, as well as characterizing the predictability of stock returns.
    JEL: G12
    Date: 2007–01
  2. By: Monica Billio (Department of Economics, University Of Venice Ca’ Foscari); Mila Getmansky (; Loriana Pelizzon (
    Abstract: This article aims to investigate the phase-locking and switching volatility in the idiosyncratic risk factor of hedge funds using switching regime beta models. This approach allows the analysis of hedge fund tail event behavior and in particular the changes in hedge fund exposure to various risk factors potentially related to liquidity risk, conditional on different states of the market. We and that in a normal state of the market, the exposure to risk factors could be very low but as soon as the market risk factor captured by the S&P500 moves to a down-market state characterized by negative returns and high volatility, the exposure of hedge fund indexes to the S&P500 and especially to other risk factors changes signi?cantly presenting evidence of phase-locking. We further extend the regime switching model to allow for non-linearity in residuals and show that switching regime models are able to capture and forecast the evolution of the idiosyncratic risk factor in terms of changes from a low volatility regime to a distressed state that are not directly related to market risk factors.
    Keywords: Hedge Funds; Risk Management; Regime-Switching Models, Liquidity
    JEL: G12 G29 C51
    Date: 2006
  3. By: Philippe Bacchetta (Study Center Gerzensee); Elmar Mertens (Study Center Gerzensee); Eric van Wincoop (University of Virginia)
    Abstract: There is widespread evidence of excess return predictability in financial markets. In this paper we examine whether this predictability is related to expectational errors. To consider this issue, we use data on survey expectations of market participants in the stock market, the foreign exchange market, and the bond and money markets in various countries. We find that the predictability of expectational errors coincides with the predictability of excess returns: when a variable predicts expectational errors in a given market, it typically predicts the excess return as well. Understanding expectational errors appears crucial for explaining excess return predictability.
    Date: 2006–06
  4. By: Monica Billio (Department of Economics, University Of Venice Ca’ Foscari); Massimiliano Caporin (
    Abstract: We propose a generalization of the Dynamic Conditional Correlation multivariate GARCH model of Engle (2002) and of the Asymmetric Dynamic Conditional Correlation model of Cappiello et al. (2006). The model we propose introduces a block structure in parameter matrices that allows for interdependence with a reduced number of parameters. Our model nests the Flexible Dynamic Conditional Correlation model of Billio et al. (2006) and is named Quadratic Flexible Dynamic Conditional Correlation Multivariate GARCH. In the paper, we provide conditions for positive definiteness of the conditional correlations. We also present an empirical application to the Italian stock market comparing alternative correlation models for portfolio risk evaluation.
    Keywords: Dynamic correlations, Block-structures, Flexible correlation models
    JEL: C51 C32 G18
    Date: 2006
  5. By: Manfred Gilli (University of Geneva); Evis Këllezi (Mirabaud & cie); Hilda Hysi (University of Geneva - Department of Econometrics)
    Abstract: In practical portfolio choice models risk is often defined as VaR, expected short-fall, maximum loss, Omega function, etc. and is computed from simulated future scenarios of the portfolio value. It is well known that the minimization of these functions can not, in general, be performed with standard methods. We present a multi-purpose data-driven optimization heuristic capable to deal efficiently with a variety of risk functions and practical constraints on the positions in the portfolio. The efficiency and robustness of the heuristic is illustrated by solving a collection of real world portfolio optimization problems using different risk functions such as VaR, expected shortfall, maximum loss and Omega function with the same algorithm.
    Keywords: Portfolio optimization, Heuristic optimization, Threshold accepting, Downside risk
    JEL: C61 C63 G11 G32

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