We shed light on a class of models that increase the flexibility of the seasonal pattern within a framework of the structural time series model. The basic idea is to drive the seasonal summation model by a moving average process rather than by a white noise or an AR process. Generally, such an...

The popular "airline" model for a seasonal time series assumes that a variable needsdouble differencing, i.e. first and seasonal (or annual) differencing.The resultant time series can usually be described by a low order movingaverage model with estimated roots close to the unit circle. This...

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological...

An empirical comparison of forecasting performance is undertaken for multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models in the estimation of intraday value at risk (VaR). This comparison aims to evaluate the applicability of such models to risk management using...