Risk Measure Estimation On Fiegarch Processes
Taiane S. Prass, S\'ilvia R.C. Lopes
TL;DR
This paper investigates the estimation of Value at Risk (VaR) on FIEGARCH processes, comparing different risk measures through simulation for portfolios of Brazilian stocks.
Contribution
It provides a simulation-based analysis of risk measure estimation specifically tailored to FIEGARCH models, including a comparison of VaR, ES, and MaxLoss.
Findings
VaR estimation performance varies across models
Comparison shows differences between risk measures
Application to Brazilian stock portfolio
Abstract
We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We present a simulated study regarding the estimation of the risk measure on FIEGARCH processes. We consider the distribution function of the portfolio log-returns (univariate case) and the multivariate distribution function of the risk-factor changes (multivariate case). We also compare the performance of the risk measures , and MaxLoss for a portfolio composed by stocks of four Brazilian companies.
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Market Dynamics and Volatility · Monetary Policy and Economic Impact