This paper proposes a procedure for using a GARCH or exponentially weighted moving average model in conjunction with historical simulation when computing value at risk. It involves adjusting historical data on each market variable to reflect the difference between the historical volatility of the market variable and its current volatility. We compare the approach using nine years of daily data ...

Purpose The purpose of the research is to assess risk financial market in digital economy through quantitative analysis model big data era. It a challenge for government carry out management Design/methodology/approach In this study, generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) constructed analyze economy. Additionally, correlation test and station...

We examine the effect of oil price uncertainty on sovereign credit risks in Gulf Cooperation Council (GCC) countries. Unlike past studies, we employ a structural vector autoregression with multivariate GARCH-in-mean (VAR-GARCH-in-mean) approach after filtering out outliers observed series. The findings show that market has positive impact Credit Default Swap (CDS) spreads GCC Furthermore, find ...

This paper revisits event-study methodology based on regression estimation of abnormal returns. The paper reviews the traditional event study and gives a more detailed discussion of the regression based approach with quantitative event variables. The paper discusses also briefly the the dummy variable regression which is a special case of the quantitative case. Use of GARCH to predict event per...

It is well known that the class of strong (Generalized) AutoRegressive Conditional Heteroskedasticity (or GARCH) processes is not closed under contemporaneous aggregation. This paper provides the dynamics followed by the aggregate process when the individual persistence parameters are drawn from the same (unknown) distribution. Assuming heterogeneity across individual parameters, the dynamics o...

This paper proposes a semiparametric approach by introducing a smooth scale function into the standard GARCH model so that conditional heteroskedasticity and scale change in a nancial time series can be modelled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic properties of...

A procedure is developed to test the null hypothesis of conditional homoskedasticity in the context of GARCH models. The approach is based on the quasi-likelihood function, leaving the true distribution of model disturbances completely unspecified. The presence of possible nuisance parameters in the testing problem is dealt with by using a pivotal bound and Monte Carlo resampling techniques to ...

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...

In recent years, fractionally-differenced processes have received a great deal of attention due to their flexibility in financial applications with long-memory. This paper revisits the class of generalized fractionally-differenced processes generated by Gegenbauer polynomials and the ARMA structure (GARMA) with both the long-memory and time-dependent innovation variance. We establish the existe...

A detailed comparison is made of volatility forecasting techniques on Australian value-weighted indices. The techniques compared are the naı̈ve approach (historical volatility), an improved extreme-value method (IEV), the ARCH/GARCH class of models and an exponentially weighted moving average (EWMA) of volatility. The study suggests that the EWMA technique appears to be the best volatility forec...