Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions
نویسندگان
چکیده
The use of GARCH models with stable Paretian innovations in financial modeling has been recently suggested in the literature. This class of processes is attractive because it allows for conditional skewness and leptokurtosis of financial returns without ruling out normality. This contribution illustrates their usefulness in predicting the downside risk of financial assets in the context of modeling foreign exchange-rates and demonstrates their superiority over use of normal or Student’s t GARCH models. JEL Classification: C22, C51, G10
منابع مشابه
Dynamic Cross Hedging Effectiveness between Gold and Stock Market Based on Downside Risk Measures: Evidence from Iran Emerging Capital Market
This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and c...
متن کاملStochastic Volatility Models: Conditional Normality versus Heavy-Tailed Distributions
Most of the empirical applications of the stochatic volatility (SV) model are based on the assumption that the conditional distribution of returns given the latent volatility process is normal. In this paper the SV model based on a conditional normal distribution is compared with SV speciications using conditional heavy-tailed distributions, especially Student's t-distribution and the generaliz...
متن کاملStochastic models for risk estimation in volatile markets: a survey
The problem of portfolio risk estimation in volatile markets requires employing fat-tailed models for financial instrument returns combined with copula functions to capture asymmetries in dependence and a true downside risk measure for risk estimation. In this survey, we discuss how these three essential components can be combined together in a Monte Carlo based framework for risk estimation an...
متن کاملEstimating L-Functionals for Heavy-Tailed Distributions and Application
L-functionals summarize numerous statistical parameters and actuarial risk measures. Their sample estimators are linear combinations of order statistics L-statistics . There exists a class of heavy-tailed distributions for which the asymptotic normality of these estimators cannot be obtained by classical results. In this paper we propose, by means of extreme value theory, alternative estimators...
متن کاملInterval Estimation of Value-at-Risk Based on GARCH Models with Heavy Tailed Innovations
ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed innovations, we characterize the limiting distribution of an estimator of the conditional Value-at-Risk (VaR), which corresponds to the extremal quantile of the conditional distribution of the GARCH process. We propose two methods, the normal ...
متن کامل