Showing 1 - 10 of 10
We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post. This enables a Wald-type conditional quantile forecast...
Persistent link: https://www.econbiz.de/10010603357
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight...
Persistent link: https://www.econbiz.de/10011843275
This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight...
Persistent link: https://www.econbiz.de/10011543115
Persistent link: https://www.econbiz.de/10011623670
Persistent link: https://www.econbiz.de/10009706180
Persistent link: https://www.econbiz.de/10010053968
This paper proposes a new bivariate modeling approach for setting daily equity-trading risk limits using high-frequency data. We construct one-day-ahead Value-at-Risk (VaR) forecasts by taking into account the different dynamics of the overnight and daytime return processes and their covariance....
Persistent link: https://www.econbiz.de/10013036878
This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...
Persistent link: https://www.econbiz.de/10013105936
We make use of quantile regression theory to obtain a combination of individual potentially-biased VaR forecasts that is optimal because it meets by construction ex post the correct out-of-sample conditional coverage criterion. This enables a Wald-type conditional quantile forecast encompassing...
Persistent link: https://www.econbiz.de/10013092448
This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...
Persistent link: https://www.econbiz.de/10013096415