This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

a r t i c l e i n f o JEL classification: C32 C51 L94 Q40 Keywords: Wholesale spot electricity price markets Constant and dynamic conditional correlation Multivariate GARCH This paper examines the interrelationships of wholesale spot electricity prices among the four regional A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Dynamic c...

This paper investigates the forecasting ability of five different versions of GARCH models. The five GARCH models applied are bivariate GARCH, GARCH-ECM, BEKK GARCH, GARCH-X and GARCH-GJR. Forecast errors based on four emerging stock futures portfolio return (based on forecasted hedge ratio) forecasts are employed to evaluate out-ofsample forecasting ability of the five GARCH models. Daily data...

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns. In this financial analysis, both these components are modeled as a GARCH. We compare GDFM+GARCH and ...

It is well-established that the …nancial time series display some stylized fatcs such as volatility clustering, high kurtosis, low starting and slow-decaying autocorrelation function and the Talyor e¤ect as well. In order to evaluate volatility models’capacity in capturing such facts, we apply both standard and robust measures of kurtosis and autocorrelation of squares to GARCH, EGARCH and ARSV...

Given the growing need for managing financial risk, risk prediction plays an increasing role in banking and finance. In this study, we compare the out-of-sample performance of existing methods and some new models for predicting Value-at-Risk. Using more than 30 years of the daily return data on the NASDAQ Composite Index, we find that most approaches perform inadequately, although several model...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

In this paper, we introduce a two−dimensional Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model for clutter modeling and anomaly detection. The one−dimensional GARCH model is widely used for modeling financial time series. Extending the one−dimensional GARCH model into two dimensions yields a novel clutter model which is capable of taking into account important characteris...

This paper develops a closed-form option pricing formula for a spot asset whose variance follows a GARCH process. The model allows for correlation between returns of the spot asset and variance and also admits multiple lags in the dynamics of the GARCH process. The single-factor (one-lag) version of this model contains Heston’s (1993) stochastic volatility model as a diffusion limit and therefo...

In the light of regime switching and volatility clustering in the dynamics of SHIBOR, regime-switching CIR model (RSCIR) and regime-switching GARCH CIR model (RSCIR-GARCH) are established by introducing regime-switching and GARCH specifications into CIR model successively. Then, a contrast study among CIR, RSCIR and RSCIR-GARCH models is performed based on SHIBOR sample data, which indicates th...