Forecasting Value-at-Risk using block structure multivariate stochastic volatility models
نویسندگان
چکیده
منابع مشابه
Block Structure Multivariate Stochastic Volatility Models
Most multivariate variance models suffer from a common problem, the " curse of dimensionality ". For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose was to combine the need for interpretability and efficiency faced by...
متن کاملDEPARTMENT OF ECONOMICS AND FINANCE COLLEGE OF BUSINESS AND ECONOMICS UNIVERSITY OF CANTERBURY CHRISTCHURCH, NEW ZEALAND Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models
Most multivariate variance or volatility models suffer from a common problem, the " curse of dimensionality ". For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose was to combine the need for interpretability and effic...
متن کاملForecasting 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...
متن کاملforecasting value-at-risk using conditional volatility models: evidence from tehran stock exchange
in this paper, we investigate the performance of parametric arch class models to forecast out-of-sample var for two portfolios of tehran stock exchange (tse) companies (market portfolio and a portfolio of 50 liquid companies), using a number of distributional assumptions and sample sizes at low and high confidence levels. we find, first, that leptokurtic distributions are able to produce better...
متن کاملMultivariate stochastic volatility using state space models
A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distribution to allow for different correlation structure between the observation and state innovation vectors and we extend the convolution between the Wishart and the multivariate singular beta distribut...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Review of Economics & Finance
سال: 2015
ISSN: 1059-0560
DOI: 10.1016/j.iref.2015.02.004