We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure...

The aim of this paper is to empirically investigate the in sample and out of sample forecasting performance of several GARCH-type models such as GARCH, EGARCH and APARCH model with Gaussian, student-t, Generalized error distribution (GED), student-t with fixed DOF 10 and GED with fixed parameter...

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods...

We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and...

Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk management; they have become one of the major topics in financial econometrics and they are principally and continuously used in the pricing of financial assets and the Value at Risk,...

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of...

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal...

The contributions of error distributions have been ignored while modeling stock market volatility in Nigeria and studies have shown that the application of appropriate error distribution in volatility model enhances efficiency of the model. Using Nigeria All Share Index from January 2, 2008 to...

A model-free methodology is used for the first time to estimate a daily volatility index (VIBEX-NEW) for the Spanish financial market.We use a public data set of daily option prices to compute this index and showthat daily changes in VIBEXNEW display a negative, tight contemporaneous...