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 ...

Conditional quantile estimation is an essential ingredient in modern risk management. Although GARCH processes have proven highly successful in modeling financial data it is generally recognized that it would be useful to consider a broader class of processes capable of representing more flexibly both asymmetry and tail behavior of conditional returns distributions. In this paper, we study esti...

The skewness in physical distributions of equity index returns and the implied volatility skew in the risk neutral measure are subjects of extensive academic research. Much attention is now being focused on models that are able to capture time-varying conditional skewness and kurtosis. For this reason normal mixture GARCH(1,1) models have become very popular in financial econometrics. We introd...

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a...

It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, threshold GARCH and Double threshold heteroskedastic model with auxiliar...

A class of semiparametric fractional autoregressive GARCH models (SEMIFARGARCH), which includes deterministic trends, difference stationarity and stationarity with shortand long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error ...

This paper proposes a simple test for threshold nonlinearity in either the mean or volatility equation, or both, of a heteroskedastic time series. Our proposal adopts existing Bayesian Markov chain Monte Carlo methods to fit a general double threshold GARCH model, which may have an explosive regime, then forms posterior credible intervals on model parameters to detect and specify threshold nonl...

This paper attempts to model the behavior of 1-minute high frequency exchange rate data of 5 currencies : the Japanese Yen, the Australian Dollar, the Canadian Dollar, the Euro, the Pound sterling against the US Dollar, on 21 July 2005 when the Chinese Yuan was revaluated. The data shows the following distinctive features: (1) There is a large jump at the time of the Yuan revaluation, (2) Large...

this paper investigates the nature of volatility characteristics of stock returns in the bangladesh stock markets employing daily all share price index return data of dhaka stock exchange (dse) and chittagong stock exchange (cse) from 02 january 1993 to 27 january 2013 and 01 january 2004 to 20 august 2015 respectively.  furthermore, the study explores the adequate volatility model for the stoc...