Most pricing and hedging models rely on the long run temporal stability of a sample covariance matrix. Using a large dataset of equity prices from four countries, the US, UK, Japan and Germany, we test the rolling stability of realized sample covariance matrices using two complementary...

This paper studies the relative importance of discount rates and cash flows with a focus on the differences between time-series and cross-sectional variance tests. I show that the following holds for the market, different types of portfolios, and individual stocks: (a) changes in expected...

We examine the private information associated with insider trades using a Chinese data set. Insider buys positively forecast individual stock returns and insider sales negatively forecast individual stock returns. Classifying insiders as corporate managers and institutional investors, we find...

We start this paper by presenting compelling evidence of short-term momentum in the excess returns on the S&P Composite stock price index. For the first time ever, we assume that the excess returns follow an autoregressive process of order p, AR(p), and evaluate the parameters of this process....

Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis....

Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex...

Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of...

Asset returns in efficient markets should not display serial correlations. Otherwise, asset prices would be predictable to a certain extent and arbitrage opportunities would appear, contradicting the assumption of efficiency.Lack of serial correlation is considered to be true for most...

Prediction of stock prices using econometrics and machine learning based approaches poses significant challenges to the research community since the movement of stock prices are essentially random in its nature. With the rapid development and evolution of algorithms using sophisticated machine...

This paper introduces a new class of long memory model for volatility of stock returns, and applies the model on squared returns for BRICS (Brazil, Russia, India, China, and South Africa) countries. The conditional first- and second-order moments are provided. The CLS, FGLS and QML estimators...