This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modeled by and joint distributions multivariate standardized innovations characterized parametric copulas with nonparametric marginal distributions. The extend those Chen Fan (2006) to allow for conditional means volatilities, estimated via method sieves. fitted residuals t...

modeling dependence structure in financial economics is of paramount importance when estimating portfolio’s value at risk, since risk of an asset in addition to its own behavior is also dependent on the behavior of other assets in the portfolio. application of joint distribution copula is one of the methods for incorporation dependence at lower and upper tail of returns’ distribution in financi...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...

Rice plays an especial role in Iranian households' nutrition basket. The volatilities of its price during recent years caused consumers' dissatisfaction. This paper investigates spillover effects of price volatilities (at the wholesale and retail levels) in the Guilan Province rice market. The Generalized Autoregressive Conditional Hetroscedasitic (GARCH) model was used for the monthly time per...

Many static and dynamic models exist to forecast Value-at-Risk other quantile-related metrics used in financial risk management. Industry practice favours simpler, such as historical simulation or its variants. Most academic research focuses on the GARCH family. While numerous studies examine accuracy of multivariate for forecasting metrics, there is little accurately predicting entire distribu...