This study applies the methodology of Guan and Ederington (1998) to Canadian data. Historical volatility is estimated then forecast to examine the comparative performance of various time series models in forecasting volatility. Statistically significant regressions were provided that showed that forecasts of volatility produced low forecast errors, comparable to those found in Guan and Ederington. These errors, reported as the Root Mean Squared Forecast Error (RMSFE), were calculated using in-sample and out-of-sample data. Both static and dynamic forecasts were used. Static forecasts of volatility consistently produced lower forecast errors than dynamic forecasts
Recent research has suggested that forecast evaluation on the basis of stan-dard statistical loss fu...
In this paper, we aim at forecasting the stochastic volatility of key financial market variables wit...
We compare forecasts of the volatility of the Australian Dollar exchange rate to alternative measure...
The forecasting ability of the most popular volatility forecasting models is examined and an alterna...
The time to time studies enclosed, delved into the contrasting and diverging substantiation and endo...
This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility ...
The existing literature contains conflicting evidence regarding the relative quality of stock market...
This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility ...
Recent research has suggested that forecast evaluation on the basis of standard statistical loss fun...
This study evaluates a battery of forecasting volatility models using daily data of the FTSE Bursa M...
The major conflict is regarding the quality of existing literatures in stock market. Evidence shows ...
Applying modern option valuation theory requires the user to forecast the volatility of the underlyi...
The three main purposes of forecasting volatility are for risk management, for asset alloca-tion, an...
The author investigates the forecasting performance of a number of simple prediction techniques for ...
This paper analyses the forecasting performance of historical volatility models and GARCH-class mode...
Recent research has suggested that forecast evaluation on the basis of stan-dard statistical loss fu...
In this paper, we aim at forecasting the stochastic volatility of key financial market variables wit...
We compare forecasts of the volatility of the Australian Dollar exchange rate to alternative measure...
The forecasting ability of the most popular volatility forecasting models is examined and an alterna...
The time to time studies enclosed, delved into the contrasting and diverging substantiation and endo...
This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility ...
The existing literature contains conflicting evidence regarding the relative quality of stock market...
This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility ...
Recent research has suggested that forecast evaluation on the basis of standard statistical loss fun...
This study evaluates a battery of forecasting volatility models using daily data of the FTSE Bursa M...
The major conflict is regarding the quality of existing literatures in stock market. Evidence shows ...
Applying modern option valuation theory requires the user to forecast the volatility of the underlyi...
The three main purposes of forecasting volatility are for risk management, for asset alloca-tion, an...
The author investigates the forecasting performance of a number of simple prediction techniques for ...
This paper analyses the forecasting performance of historical volatility models and GARCH-class mode...
Recent research has suggested that forecast evaluation on the basis of stan-dard statistical loss fu...
In this paper, we aim at forecasting the stochastic volatility of key financial market variables wit...
We compare forecasts of the volatility of the Australian Dollar exchange rate to alternative measure...