In this paper, we take the advantage of high frequency data to develop option pricing model and select the Realized GARCH model to describe the volatility of assets, use NIG distribution to describe the distribution of underlying assets, and also build the Realized-GARCH-NIG model to price the option. Finally, we obtain the dynamic option pricing model based on the Realized-GARCH-NIG approach. ...

This paper presents an effective way of combining two popular, yet distinct approaches used in the hedging literature – dynamic programming (DP) and time-series (GARCH) econometrics. Theoretically consistent yet realistic and tractable models are developed for traders interested in hedging a portfolio. Results from a bootstrapping experiment used to construct confidence bands around the competi...

By extending the GARCH option pricing model of Duan (1995) to more exible volatility estimation it is shown that the prices of out-of-the-money options strongly depend on volatility features such as asymmetry. Results are provided for the properties of the stationary pricing distribution in the case of a threshold GARCH model. For a stock index series with a pronounced leverage eeect, simulated...

We perform Markov chain Monte Carlo simulations for a Bayesian inference of the GJR-GARCH model which is one of asymmetric GARCH models. The adaptive construction scheme is used for the construction of the proposal density in the Metropolis-Hastings algorithm and the parameters of the proposal density are determined adaptively by using the data sampled by the Markov chain Monte Carlo simulation...

Wepropose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box-Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the non-linear GARCH framework. Our simulation studies indicate that t...

We present a general framework for a GARCH (1,1) type of process with innovations using a probability law of the mean-variance mixing type. We call the process the mean variance mixing GARCH (1,1) or MVM GARCH (1,1). One implication of this particular specification is a GARCH process with skewed innovations and constant mean dynamics. This is achieved without using a location parameter to compe...

a r t i c l e i n f o JEL classification: C53 G17 Keywords: GARCH Higher conditional moments Approximate predictive distributions Value-at-Risk S&P 500 Treasury bill rate Euro–US dollar exchange rate It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation parameters. Differently from the trad...

Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatil...