We present a new approach to generalised autoregressive conditional het-eroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nancial time series, for example, asymmet...

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...

Risk management in this paper is focused on multivariate risk-return decision making assuming time-varying estimation. Empirical research in risk management showed that the static "mean-variance" methodology in portfolio optimization is very restrictive with unrealistic assumptions. The objective of this paper is estimation of time-varying portfolio stocks weights by constraints on risk measure...

Structural shifts characterize the volatility of the Korean stock and foreign exchange markets during the 1997 Asian financial crisis. This paper employs an unrestricted bivariate GARCH-M model of stock market returns to investigate empirically the effects of daily currency depreciation on Korean stock market returns. The evidence shows that currency depreciation significantly affects stock mar...

This paper considers a plethora of time-series measures of uncertainty for inflation and real output growth, which are widely used in empirical studies. Their relative performances are compared to a benchmark measure using the uncertainty measure reported by individual forecasters in the Survey of Professional Forecasters (SPF) for the period 1982-2008. The results show that the use of real-tim...

This paper examines the effects of inflation and currency substitution volatility on the average rates of inflation and currency substitution for twelve emerging market economies. Using a bivariate GARCH-in-Mean model, which accommodates for asymmetric and spillover effects of inflation and currency substitution innovations on their volatilities, we find that for the majority of the countries i...

In view of the recent documented hedging bias attributable to failing to accommodate volatility long memory, we suggest to use the simple, yet superior, realized variancecovariance (RVCOV) in dynamic hedging. For its incremental value from intradaily information, model-free and inherent long memory, RVCOV has been shown to be accurate without misspecification bias and easily generalized to high...

We present a new approach to generalised autoregressive conditional heteroscedasitic (GARCH) modelling for asset returns. Instead of attempting to choose a speciic distribution for the errors, as in the usual GARCH model formulation, we use a nonparametric distribution to estimate these errors. This takes into account the common problems encountered in nan-cial time series, for example, asymmet...

This paper introduces a spatial framework for high-frequency returns and a faster double-conditional smoothing algorithm to carry out bivariate kernel estimation of the volatility surface. A spatial multiplicative component GARCH with random effects is proposed to deal with multiplicative random effects found from the data. It is shown that the probabilistic properties of the stochastic part an...