Return Distribution (return + distribution)

Distribution by Scientific Domains
Distribution within Business, Economics, Finance and Accounting

Selected Abstracts

Day-of-the-Week Effect in High Moments

by Dan Galai
C14; C31; G14 Evidence from equity markets worldwide indicates that the Day-of-the-Week anomaly appears to fade from the first moment of the distribution of daily returns. We report highly significant pair-wise weekend effects in high moments when comparing the first and last trading days of the week. The second moment alone appears to distinguish the return distribution of the first trading day from all others. A probable explanation of the phenomena appears to be information dissemination: corporate announcements released after closing of the last trading day of the week spill-over to the opening of the first trading day, increasing its variability and carrying the closing sign. [source]

Portfolio selection, diversification and fund-of-funds: a note

Simone Brands
G23 Abstract The present paper examines the performance and diversification properties of active Australian equity fund-of-funds (FoF). Simulation analysis is employed to examine portfolio performance as a function of the number of funds in the portfolio. The present paper finds that as the number of funds in an FoF portfolio increases, performance improves in a mean,variance setting; however, measures of skewness and kurtosis behave less favourably given an investor's preferences for the higher moments of the return distribution. The majority of diversification benefits are realized when a portfolio of approximately 6 active equity funds are included in the FoF portfolio. [source]

Hedging and value at risk: A semi-parametric approach

Zhiguang Cao
The non-normality of financial asset returns has important implications for hedging. In particular, in contrast with the unambiguous effect that minimum-variance hedging has on the standard deviation, it can actually increase the negative skewness and kurtosis of hedge portfolio returns. Thus, the reduction in Value at Risk (VaR) and Conditional Value at Risk (CVaR) that minimum-variance hedging generates can be significantly lower than the reduction in standard deviation. In this study, we provide a new, semi-parametric method of estimating minimum-VaR and minimum-CVaR hedge ratios based on the Cornish-Fisher expansion of the quantile of the hedged portfolio return distribution. Using spot and futures returns for the FTSE 100, FTSE 250, and FTSE Small Cap equity indices, the Euro/US Dollar exchange rate, and Brent crude oil, we find that the semiparametric approach is superior to the standard minimum-variance approach, and to the nonparametric approach of Harris and Shen (2006). In particular, it provides a greater reduction in both negative skewness and excess kurtosis, and consequently generates hedge portfolios that in most cases have lower VaR and CVaR. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:780,794, 2010 [source]

Value at risk and conditional extreme value theory via markov regime switching models

Yau Man Ze-to Samuel
This study develops a new conditional extreme value theory-based (EVT) model that incorporates the Markov regime switching process to forecast extreme risks in the stock markets. The study combines the Markov switching ARCH (SWARCH) model (which uses different sets of parameters for various states to cope with the structural changes for measuring the time-varying volatility of the return distribution) with the EVT to model the tail distribution of the SWARCH processed residuals. The model is compared with unconditional EVT and conditional EVT-GARCH models to estimate the extreme losses in three leading stock indices: S&P 500 Index, Hang Seng Index and Hang Seng China Enterprise Index. The study found that the EVT-SWARCH model outperformed both the GARCH and SWARCH models in capturing the non-normality and in providing accurate value-at-risk forecasts in the in-sample and out-sample tests. The EVTSWARCH model, which exhibits the features of measuring the volatility of a heteroscedastic financial return series and coping with the non-normality owing to structural changes, can be an alternative measure of the tail risk. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:155,181, 2008 [source]

Improved estimation of portfolio value-at-risk under copula models with mixed marginals

Douglas J. Miller
Portfolio value-at-risk (PVAR) is widely used in practice, but recent criticisms have focused on risks arising from biased PVAR estimates due to model specification errors and other problems. The PVAR estimation method proposed in this article combines generalized Pareto distribution tails with the empirical density function to model the marginal distributions for each asset in the portfolio, and a copula model is used to form a joint distribution from the fitted marginals. The copula,mixed distribution (CMX) approach converges in probability to the true marginal return distribution but is based on weaker assumptions that may be appropriate for the returns data found in practice. CMX is used to estimate the joint distribution of log returns for the Taiwan Stock Exchange (TSE) index and the associated futures contracts on SGX and TAIFEX. The PVAR estimates for various hedge portfolios are computed from the fitted CMX model, and backtesting diagnostics indicate that CMX outperforms the alternative PVAR estimators. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:997,1018, 2006 [source]

Testing range estimators of historical volatility

Jinghong Shu
This study investigates the relative performance of various historical volatility estimators that incorporate daily trading range: M. Parkinson (1980), M. Garman and M. Klass (1980), L. C. G. Rogers and S. E. Satchell (1991), and D. Yang and Q. Zhang (2000). It is found that the range estimators all perform very well when an asset price follows a continuous geometric Brownian motion. However, significant differences among various range estimators are detected if the asset return distribution involves an opening jump or a large drift. By adding microstructure noise to the Monte Carlo simulation, the finding of S. Alizadeh, M. W. Brandt, and F. X. Diebold (2002),that range estimators are fairly robust toward microstructure effects,is confirmed. An empirical test with S&P 500 index return data shows that the variances estimated with range estimators are quite close to the daily integrated variance. The empirical results support the use of range estimators for actual market data. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:297,313, 2006 [source]

Modeling and Forecasting Realized Volatility

ECONOMETRICA, Issue 2 2003
Torben G. Andersen
We provide a framework for integration of high,frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Building on the theory of continuous,time arbitrage,free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen/Dollar spot exchange rates, we find that forecasts from a simple long,memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal,normal mixture distribution produces well,calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation, and financial risk management applications. [source]

The Use of Archimedean Copulas to Model Portfolio Allocations

David A. Hennessy
A copula is a means of generating an n -variate distribution function from an arbitrary set of n univariate distributions. For the class of portfolio allocators that are risk averse, we use the copula approach to identify a large set of n -variate asset return distributions such that the relative magnitudes of portfolio shares can be ordered according to the reversed hazard rate ordering of the n underlying univariate distributions. We also establish conditions under which first- and second-degree dominating shifts in one of the n underlying univariate distributions increase allocation to that asset. Our findings exploit separability properties possessed by the Archimedean family of copulas. [source]

Les hedge funds ont-ils leur place dans un portefeuille institutionnel canadien?

Stéphanie Desrosiers
This article examines the return and risk of hedge funds (HF), and their correlations with traditional asset classes for the 1990,2002 period. Efficient frontiers resulting from optimizations with and without constraints demonstrate that it is worthwhile to include HF in a Canadian institutional investor's portfolio. HF offer a high potential return relative to risk, while weaker correlations with traditional asset classes create a beneficial diversification effect. Non-directional HF provide protection in bear markets and are more suitable for lower risk portfolios, whereas directional HF are better suited to higher risk portfolios. Caveats are necessary due to the skew-ness and kurtosis of the return distributions, potential biases in the return series, the lower liquidity, and the complexity of the HF industry. Résumé Cet article examine le rendement, le risque et les correélations des hedge funds (HF) avec les catégories d'actif traditionnelles sur la période 1990,2002. Des optimisations avec et sans contraintes montrent qu'il est avantageux d'inclure les HF dans un portefeuille institutionnel canadien du fait d'un potentiel de rendement élevé par rapport au risque encouru et de faibles corrélations. Les HF non-directionnels offrent une meilleure protection en marché baissier et sont plus appropriés pour des portefeuilles moins risqués. Les HF directionnels conviennent davantage aux portefeuilles prksentant un risque plus élevé. Des réserves doivent toutefois étre émises en raison des coefficients d'asymétrie et d'aplatissement de la distribution des rendements, des biais potentiels des données, de la faible liquidité, et de la complexité de l'industrie des HF. [source]

Value-at-risk for long and short trading positions

Pierre Giot
In this paper we model Value-at-Risk (VaR) for daily asset returns using a collection of parametric univariate and multivariate models of the ARCH class based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed density models when the left and right tails of the distribution of returns must be modelled. Thus, VaR for traders having both long and short positions is not adequately modelled using usual normal or Student distributions. We suggest using an APARCH model based on the skewed Student distribution (combined with a time-varying correlation in the multivariate case) to fully take into account the fat left and right tails of the returns distribution. This allows for an adequate modelling of large returns defined on long and short trading positions. The performances of the univariate models are assessed on daily data for three international stock indexes and three US stocks of the Dow Jones index. In a second application, we consider a portfolio of three US stocks and model its long and short VaR using a multivariate skewed Student density. Copyright © 2003 John Wiley & Sons, Ltd. [source]