varılmaktadır. The study aims to augment commonly applied volatility models with support vector machines and neural networks. The … returns in Istanbul ISE100 stock index. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics …

In this paper, an attempt has been made to model the volatility of NIFTY index of National Stock Exchange (NSE) and … comprises 3736 data points for the analysis by using Box-Jenkins or ARIMA model. The volatility in the Indian stock market … suggested by Hannan-Rissanen. As per the analysis, ARIMA (1,0,1) model was found to be the best fit to forecast the volatility …

This paper introduces a volatility model with a component structure allowing for a realized measure based on high …-frequency data (e.g realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the … traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in …

Recent empirical literature shows that Internet search activity is closely associated with volatility prediction in … evaluate the net contribution of the Internet search activity data in forecasting volatility. We conduct in-sample analysis and … markets once the model includes implied volatility. A further common component analysis shows that most of the predictive …

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The … the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast … models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting …

Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its … standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility … standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more …

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility … clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time … superior next trading day's realized volatility forecasts …

Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data …. They often employ the approximate factor model that leads to a low-rank plus sparse structure for the integrated volatility … matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these …

volatility description and forecasting performances. A set of three major asynchronous international stock market indices is used …