The major purpose of this research exercise is to assess the volatility dynamics of the stock returns of the banks of India and to determine the factor which influence and explains the stock returns. For this the two important methodologies are applied, for understanding the sensitivity of stock...

This paper examines an interdependence of returns and volatilities among equity markets using spillover index methodology developed by Diebold and Yilmaz (2009). This paper is an extension of the work done by Diebold and Yilmaz to examine whether the inclusion of both risk and return in VAR...

This paper empirically investigates the volatility pattern of Indian stock market based on time series data which comprises of daily closing prices of the S&P CNX Nifty Index for a fifteen year period from 1st April 2001 to 31st March 2016. For this study the analysis has been done using both...

This paper extends wavelet methodology to handle co-movement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multi-scale correlations along time, in contrast with...

This paper evaluates if sentiment extracted from social media and options volume anticipates future asset return. Using both textual based data and a particular market data derived call-put ratio, between July 2009 and September 2012, this research shows that: 1) features derived from market...

This study sheds new light on the question of whether or not sentiment surveys, and the expectations derived from them, are relevant to forecasting economic growth and stock returns, and whether they contain information that is orthogonal to macroeconomic and financial data. I examine 16...

Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...