RCA models with correlated errors

AbstractFinancial time series data cannot be adequately modelled by a normal distribution and empirical evidence on the non-normality assumption is very well documented in the financial literature; see [R.F. Engle, Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica 50 (1982) 987–1008] and [T. Bollerslev, Generalized autoregressive conditional heteroscedasticity, J. Econometrics 31 (1986) 307–327] for details. The kurtosis of various classes of RCA models has been the subject of a study by Appadoo et al. [S.S. Appadoo, M. Gharahmani, A. Thavaneswaran, Moment properties of some volatility models, Math. Sci. 30 (2005) 50–63] and Thavaneswaran et al. [A. Thavaneswaran, S.S. Appadoo, M. Samanta, Random coefficient GARCH models, Math. Comput. Modelling 41 (2005) 723–733]. In this work we derive the kurtosis of the correlated RCA model as well as the normal GARCH model under the assumption that the errors are correlated

Application of the Family of Sign RCA Models for Obtaining the Selected Risk Measures

Accurate modelling of risk is very important in finance. There are many alternative risk measures, however none of them is dominating. This paper proposes to use the family of Sign RCA models to obtain the Value-at-Risk (VaR) and Expected Shortfall (ES) measures. For models from the family of Sign RCA models and AR-GARCH model the one-step forecasts of VaR were calculated based on rolling estimates from the given model using different window sizes. To obtain the VaR and ES measures the filtered historical simulation was used in new version proposed by Christoffersen. The results were verified using backtesting and the loss function.Family of Sign RCA Models, risk measures, Value at Risk, Expected Shortfall.

The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures

Evaluating Value at Risk (VaR) methods of predictive accuracy in an objective and effective framework is important for both efficient capital allocation and loss prediction. From this reasons, finding an adequate method of estimating and backtesting is crucial for both the regulators and the risk managers’. The Sign RCA models may be useful to obtain the accurate forecasts of VaR. In this research one briefly describes the Sign RCA models, the Value at Risk and backtesting. We compare the predictive accuracy of alternative VaR forecasts obtained from different models. Empirical example is mainly related to the PBG Capital Group shares on the Warsaw Stock Exchange.Family of Sign RCA Models, Value at Risk, backtesting, loss function.

Application of the Family of Sign RCA Models for Obtaining the Selected Risk Measures

Accurate modelling of risk is very important in finance. There are many alternative risk measures, however none of them is dominating. This paper proposes to use the family of Sign RCA models to obtain the Value-at-Risk (VaR) and Expected Shortfall (ES) measures. For models from the family of Sign RCA models and AR-GARCH model the one-step forecasts of VaR were calculated based on rolling estimates from the given model using different window sizes. To obtain the VaR and ES measures the filtered historical simulation was used in new version proposed by Christoffersen. The results were verified using backtesting and the loss function

The Sign RCA Models: Comparing Predictive Accuracy of VaR Measures

Evaluating Value at Risk (VaR) methods of predictive accuracy in an objective and effective framework is important for both efficient capital allocation and loss prediction. From this reasons, finding an adequate method of estimating and backtesting is crucial for both the regulators and the risk managers’. The Sign RCA models may be useful to obtain the accurate forecasts of VaR. In this research one briefly describes the Sign RCA models, the Value at Risk and backtesting. We compare the predictive accuracy of alternative VaR forecasts obtained from different models. Empirical example is mainly related to the PBG Capital Group shares on the Warsaw Stock Exchange