Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly ...

Forecasting Value-at-risk by Using Garch Models

conditional copula-garch methods for value at risk of portfolio: the case of tehran stock exchange market

ارزش در معرض ریسک یکی از مهمترین معیارهای اندازه گیری ریسک در بنگاه های اقتصادی می باشد. برآورد دقیق ارزش در معرض ریسک موضوع بسیارمهمی می باشد و انحراف از آن می تواند موجب ورشکستگی و یا عدم تخصیص بهینه منابع یک بنگاه گردد. هدف اصلی این مطالعه بررسی کارایی روش copula-garch شرطی در برآورد ارزش در معرض ریسک پرتفویی متشکل از دو سهام می باشد و ارزش در معرض ریسک بدست آمده با روشهای سنتی برآورد ارزش د...

Forecasting Crude Oil prices Volatility and Value at Risk: Single and Switching Regime GARCH Models

Forecasting crude oil price volatility is an important issues in risk management. The historical course of oil price volatility indicates the existence of a cluster pattern. Therefore, GARCH models are used to model and more accurately predict oil price fluctuations. The purpose of this study is to identify the best GARCH model with the best performance in different time horizons. To achieve th...

Accurate value-at-risk forecasting based on the 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 resu...