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Fortin, Ines; Kuzmics, Christoph - 2002

The empirical joint distribution of return-pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric-tail...

Persistent link: https://www.econbiz.de/10010292792

Forecasting Value-at-Risk using Block Structure Multivariate Stochastic Volatility Models

Asai, Manabu; Caporin, Massimiliano; McAleer, Michael - 2013

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on...

Persistent link: https://www.econbiz.de/10010326487

Realized Volatility Risk

Allen, David E.; McAleer, Michael; Scharth, Marcel - 2013

In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even...

Persistent link: https://www.econbiz.de/10010326350

Time series analysis for financial market meltdowns

Rachev, Svetlozar T.; Bianchi, Michele Leonardo; Mitov, Ivan - 2010

There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this...

Persistent link: https://www.econbiz.de/10010301728

Comparing and evaluating Bayesian predictive distributions of assets returns

Geweke, John; Amisano, Gianni - 2008

Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative...

Persistent link: https://www.econbiz.de/10011605015

Optimal Prediction Pools

Geweke, John; Amisano, Gianni - 2009

A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...

Persistent link: https://www.econbiz.de/10011605063

Interval estimation of value-at-risk based on nonparametric models

Khraibani, Hussein; Nehme, Bilal; Strauss, Olivier - In: Econometrics 6 (2018) 4, pp. 1-30

the sources of uncertainty stems from the dependence of the VaR estimation on the choice of the computation method. As we … nonparametric approach called maxitive kernel estimation of the VaR. This estimation is based on a coherent extension of the kernel …-based estimation of the cumulative distribution function to convex sets of kernel. We thus obtain a convex set of VaR estimates …

Persistent link: https://www.econbiz.de/10011995231

Value-at-risk and expected shortfall when there is long range dependence

Härdle, Wolfgang Karl; Mungo, Julius - 2008

portfolio (Bayer, Siemens and Volkswagen). Classical V aR estimation methodology such as exponential moving average (EMA) as …

Persistent link: https://www.econbiz.de/10010274140

Evaluating Density Forecasts with an Application to Stock Market Returns

Raunig, Burkhard; de Raaij, Gabriela - 2002

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

Persistent link: https://www.econbiz.de/10010295725

Value-at-Risk and Extreme Returns

Daníelsson, Jón; de Vries, Casper G. - 1998

Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...

Persistent link: https://www.econbiz.de/10010324710