Hedging Effectiveness under Conditions of Asymmetry

We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using crude oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value at Risk (CVAR). Comparisons are applied to a number of hedging strategies including OLS and both Symmetric and Asymmetric GARCH models. Our findings show that asymmetry reduces in-sample hedging performance and that there are significant differences in hedging performance between short and long hedgers. Thus, tail specific performance metrics should be applied in evaluating hedging effectiveness. We also find that the Ordinary Least Squares (OLS) model provides consistently good performance across different measures of hedging effectiveness and estimation methods irrespective of the characteristics of the underlying distribution

Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived

Exact optimal and adaptive inference in regression models under heteroskedasticity and non-normality of unknown forms

In this paper, we derive simple point-optimal sign-based tests in the context of linear and nonlinear regression models with fixed regressors. These tests are exact, distribution-free, robust against heteroskedasticity of unknown form, and they may be inverted to obtain confidence regions for the vector of unknown parameters. Since the point-optimal sign tests depend on the alternative hypothesis, we propose an adaptive approach based on split-sample techniques in order to choose an alternative such that the power of point-optimal sign tests is close to the power envelope. The simulation results show that when using approximately 10% of sample to estimate the alternative and the rest to calculate the test statistic, the power of point-optimal sign test is typically close to the power envelope. We present a Monte Carlo study to assess the performance of the proposed “quasi”-point-optimal sign test by comparing its size and power to those of some common tests which are supposed to be robust against heteroskedasticity. The results show that our procedures are superior

Measuring financial risk : comparison of alternative procedures to estimate VaR and ES

We review several procedures for estimating and backtesting two of the most important measures of risk, the Value at Risk (VaR) and the Expected Shortfall (ES). The alternative estimators differ in the way the specify and estimate the conditional mean and variance and the conditional distribution of returns. The results are illustrated by estimating the VaR and ES of daily S&P500 returns

Accurate value-at-risk forecast with the (good) old normal-GARCH model

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L. Klassifizierung: C22, C53, C63, G12Die Normalverteilung ist, entgegen ihrer hohen Verbreitung in der empirischen Finanzanalyse, im allgemeinen nicht dazu geeignet, die Renditen von Finanzmarkt-Zeitreihen adäquat zu beschreiben. Ein viel beobachtetes PhÄanomen ist insbesondere die über die Zeit variierende Volatilität der Renditen, die eine bedingte Modellierung der Renditen notwendig erscheinen läßt. Der wohl am weitesten verbreitete Ansatz um solche Volatilitätsschwankungen zu modellieren ist das GARCH-Modell. Doch auch bei Berücksichtigung der VolatilitÄatschwankungen, d.h. bei bedingter Modellierung der Renditen mit Hilfe eines GARCH-Modells, ist die Normalverteilung im allgemeinen nicht dazu geeignet, die Verteilung der GARCH-gefilterten Renditen ausreichend genau zu beschreiben. Insbesondere Value-at-Risk (VaR) Prognosen sind mit dem normal-GARCH Modell im allgemeinen verzerrt, da die Normalverteilung die Enden der Rendite-Verteilung nur unzureichend beschreibt. Mögliche Auswege scheinen die Erweiterung und Modifikation der GARCH Dynamik, sowie die Verwendung anderer Verteilungen. Dies führt jedoch im allgemeinen dazu, daß diese Modelle sowohl theoretisch, als auch praktisch schwerer zu beherrschen sind. In der vorliegenden Studie entwickeln wir eine auf dem Bootstrap basierende Methode mit einem Verzerrungs-Korrektur Schritt, um die VaR Prognoseeigenschaften des normal-GARCH Modells zu verbessern. Im Vergleich zur Verwendung von komplexeren GARCH Spezifikationen und/oder Verteilungsannahmen ist diese neue Methode schnell, einfach zu implementieren, numerisch zuverlässig und (abgesehen von einer zu wählenden Fensterlänge L für den Schritt zur Korrektur der VaR-Verzerrung) vollst ndig Daten getrieben. Die vorgeschlagene Methode wird in langen out-of-sample Prognosezeiträumen auf ihre VaR Prognosefähigkeiten geprüft. Sowohl für verschiedene Finanzmarkt-Reihen, als auch für simulierte Daten, erweist sich die neue Methode als sehr gut geeignet, die VaR Prognosen der normal-GARCH Modells entscheidend zu verbessern und liefert auch im Vergleich zu komplexeren Modellen sehr gute Ergebnisse