Macroeconomic news announcements are elaborate and multi-dimensional. We consider a framework in which jumps in asset prices around macroeconomic news and monetary policy announcements reflect both the response to observed surprises in headline numbers and latent factors, reflecting other...

The primary objective of the paper is to forecast the beta values of companies listed on Sensex, Bombay Stock Exchange (BSE). The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies. To reach out the predefined objectives of the research, Auto...

We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a...

This paper examines the relationship between volatility and the probability of occurrence of expected extreme returns in the Canadian market. Four measures of volatility are examined: implied volatility from firm option prices, conditional volatility calculated using an EGARCH model,...

Common predictors variables for the equity premium such as financial ratios exhibit high persistence and thus are borderline non-stationary. This article sheds light on the possibility of fractional differencing those ratios in order to attain stationarity yet preserving the long-run memory....

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...

Testing for constant expected returns and forecasting future returns necessitate the information beyond a single predictor. We consider the predictive regression model with multiple predictors which are potentially strongly persistent and cointegrated. Instrumental variables based tests for...

Providing a more accurate covariance matrix forecast can substantially improve the performance of optimized portfolios. Using out-of-sample tests, in this paper, we evaluate alternative covariance matrix forecasting methods by looking at (1) their forecast accuracy, (2) their ability to track...

We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S&P 500 index. We assess the...