Data Driven Value-at-Risk Forecasting Using a SVR-GARCH-KDE Hybrid

Forecasting Value-at-risk by Using Garch Models

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...

Forecasting Value at Risk (VAR) in the futures market using Hybrid method of Neural Networks and GARCH model

This study proposes a hybrid model, which combines GARCH and Neural Network, for estimating VAR in Nasdaq 100 and Dow Jones futures index market. Empirical results demonstrated that the hybrid method has certain outperformed the conventional method (historical simulation, variance/covariance and the Monte Carlo simulation) in estimating VAR. In terms of accuracy, the hybrid method is superior t...

Forecasting Conditional Correlation for Exchange Rates using Multivariate GARCH models with Historical Value-at-Risk application

The generalization from the univariate volatility model into a multivariate approach opens up a variety of modeling possibilities. This study aims to examine the performance of the two multivariate GARCH models BEKK and DCC, applied on ten years exchange rates data. Estimations and forecasts of the covariance matrix are made for the EUR/SEK and USD/SEK, whereby the forecasts are used in a pract...

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 ...