Gitanjali Persand

The Effect of Asymmetries on Optimal Hedge Ratios

There is widespread evidence that the volatility of stock returns displays an asymmetric response to good and bad news. This article considers the impact of asymmetry on time-varying hedges for financial futures. An asymmetric model that allows forecasts of cash and futures return volatility to respond differently to positive and negative return innovations gives superior in-sample hedging performance. However, the simpler symmetric model is not inferior in a hold-out sample. A method for evaluating the models in a modern risk-management framework is presented, highlighting the importance of allowing optimal hedge ratios to be both time-varying and asymmetric.

Seasonality in Southeast Asian stock markets: some new evidence on day-of-the-week effects

This paper examines the evidence for a day-of-the-week effect in five Southeast Asian stock markets: South Korea, Malaysia, the Philippines, Taiwan and Thailand. Findings indicate significant seasonality for three of the five markets. Market risk, proxied by the return on the FTA World Price Index, is not sufficient to explain this calendar anomaly. Although an extension of the risk-return equation to incorporate interactive seasonal dummy variables can explain some significant day-of-the-week effects, market risk alone appears insufficient to characterize this phenomenon.

Benchmarks and the accuracy of GARCH model estimation

This paper reviews nine software packages with particular reference to their GARCH model estimation accuracy when judged against a respected benchmark. We consider the numerical consistency of GARCH and EGARCH estimation and forecasting. Our results have a number of implications for published research and future software development. Finally, we argue that the establishment of benchmarks for other standard non-linear models is long overdue.

Model Choice and Value-at-Risk Performance

Broad agreement exists among both the investment banking and regulatory communities that the use of internal risk management models is an efficient means for calculating capital risk requirements. The determination of model parameters laid down by the Basle Committee on Banking Supervision as necessary for estimating and evaluating the capital adequacies, however, has received little academic scrutiny. We investigate a number of issues of statistical modeling in the context of determining market-based capital risk requirements. We highlight several potentially serious pitfalls in commonly applied methodologies and conclude that simple methods for calculating value at risk often provide superior performance to complex procedures. Our results thus have important implications for risk managers and market regulators. Broad agreement exists in both the investment banking and regulatory communities that the use of internal risk management models can provide an efficient means for calculating capital risk requirements. The determination of the model parameters necessary for estimating and evaluating the capital adequacies laid down by the Basle Committee on Banking Supervision, however, has received little academic scrutiny. We extended recent research in this area by evaluating the statistical framework proposed by the Basle Committee and by comparing several alternative ways to estimate capital adequacy. The study we report also investigated a number of issues concerning statistical modeling in the context of determining market-based capital risk requirements. We highlight in this article several potentially serious pitfalls in commonly applied methodologies. Using data for 1 January 1980 through 25 March 1999, we calculated value at risk (VAR) for six assets—three for the United Kingdom and three for the United States. The U.K. series consisted of the FTSE All Share Total Return Index, the FTA British Government Bond Index (for bonds of more than 15 years), and the Reuters Commodities Price Index; the U.S. series consisted of the S&P 500 Index, the 90-day T-bill, and a U.S. government bond index (for 10-year bonds). We also constructed two equally weighted portfolios containing these three assets for the United Kingdom and the United States. We used both parametric (equally weighted, exponentially weighted, and generalized autoregressive conditional heteroscedasticity) models and nonparametric models to measure VAR, and we applied a method based on the generalized Pareto distribution, which allowed for the fat-tailed nature of the return distributions. Following the Basle Committee rules, we determined the adequacy of the VAR models by using backtests (i.e., out-of-sample tests), which counted the number of days during the past trading year that the capital charge was insufficient to cover daily trading losses. We found that, although the VAR estimates from the various models appear quite similar, the models produce substantially different results for the numbers of days on which the realized losses exceeded minimum capital risk requirements. We also found that the effect on the performance of the models of using longer runs of data (rather than the single trading year required by the Basle Committee) depends on the model and asset series under consideration. We discovered that a method based on quantile estimation performed considerably better in many instances than simple parametric approaches based on the normal distribution or a more complex parametric approach based on the generalized Pareto distribution. We show that the use of critical values from a normal distribution in conjunction with a parametric approach when the actual data are fat tailed can lead to a substantially less accurate VAR estimate (specifically, a systematic understatement of VAR) than the use of a simple nonparametric approach. Finally, the closer quantiles are to the mean of the distribution, the more accurately they can be estimated. Therefore, if a regulator has the desirable objective of ensuring that virtually all probable losses are covered, using a smaller nominal coverage probability (say, 95 percent instead of 99 percent), combined with a larger multiplier, is preferable. Our results thus have important implications for risk managers and market regulators.

A word of caution on calculating market-based minimum capital risk requirements

This paper demonstrates that the use of GARCH-type models for the calculation of minimum capital risk requirements (MCRRs) may lead to the production of inaccurate and therefore inefficient capital requirements. We show that this inaccuracy stems from the fact that GARCH models typically overstate the degree of persistence in return volatility. A simple modification to the model is found to improve the accuracy of MCRR estimates in both back- and out-of-sample tests. Given that internal risk management models are currently in widespread usage in some parts of the world (most notably the USA), and will soon be permitted for EC banks and investment firms, we believe that our paper should serve as a valuable caution to risk management practitioners who are using, or intend to use this popular class of models.

The trading profitability of forecasts of the gilt-equity yield ratio

Research has highlighted the usefulness of the Gilt–Equity Yield Ratio (GEYR) as a predictor of UK stock returns. This paper extends recent studies by endogenising the threshold at which the GEYR switches from being low to being high or vice versa, thus improving the arbitrary nature of the determination of the threshold employed in the extant literature. It is observed that a decision rule for investing in equities or bonds, based on the forecasts from a regime switching model, yields higher average returns with lower variability than a static portfolio containing any combinations of equities and bonds. A closer inspection of the results reveals that the model has power to forecast when investors should steer clear of equities, although the trading profits generated are insufficient to outweigh the associated transaction costs.

A note on estimating market-based minimum capital risk requirements: a multivariate GARCH approach

Internal risk management models of the kind popularized by J. P. Morgan are now used widely by the world’s most sophisticated financial institutions as a means of measuring risk. Using the returns on three of the most popular futures contracts on the London International Financial Futures Exchange, in this paper we investigate the possibility of using multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models for the calculation of minimum capital risk requirements (MCRRs). We propose a method for the estimation of the value at risk of a portfolio based on a multivariate GARCH model. We find that the consideration of the correlation between the contracts can lead to more accurate, and therefore more appropriate, MCRRs compared with the values obtained from a univariate approach to the problem.