conduct three experiments to examine its forecast, nowcast, and backcast performance. We find that the mean absolute … summary, deep learning is a promising tool in economic forecast with time series data sampled at different frequencies …

positive effect of the MOVE shock on treasury rate volatility for up to about 4 months . Also, forecast error variance …English Abstract: This study selected predictors for interest rate volatility and empirically analyzed the dynamic … relationship among selected variables using time series methods. In doing so, it considered realized volatility treasury bonds rate …

English Abstract: This study has attempted to seek a volatility forecasting model that can reflect sufficiently the … long memory characteristic in the volatility of four Eastern European emerging stock markets, namely, Hungary, Poland … capture the long memory property in the volatility of these markets than the GARCH and IGARCH models. More importantly, the …

English Abstract: In this paper, we investigate if the weather affects the stock returns, the volatility of stock … volatility of stock returns is however affected …

volatility, while inflation resembles that from BK filtering. This implies that the usual practice of applying a single filtering …

forecasts are decreasing in the forecast horizon …

serious type II error problem in out-sample forecast. The implication is that SEM based on broader panel data even though it … should be inferior in in-sample forecast turns out to be superior in out sample forecast essentially because it has more case …

English Abstract: We study the volatility forecasts in Korean stock market based on the GARCHMIDAS(GARCH with the Mixed … work, we analyze the effect of long-term components of macroeconomic variables on predictability of stock market volatility … stock volatility. In addition to in-sample estimation, we conduct out-of-sample forecasts to investigate adequacy of …