By extending Easley, Kiefer, O'Hara and Paperman's (1996) framework to an intraday model, I empirically estimate the intraday probability of informed trading (PIN) for the 30 stocks in DJIA index. I document a U-shaped PIN pattern over the time of a day, and the consequent test validates this...

We detected bubbles in 22 emerging stockmarkets using both standard and threshold cointegration. Eighteen stockmarkets experienced explosive bubbles (and some of them periodically collapsing bubbles as well). The remaining four markets experienced periodically collapsing bubbles only.

In this paper, we investigate the information contents of S&P 500 VIX index and range-based volatilities by comparing their benefits on the GJR-based volatility forecasting performance. To reveal the statistical significance and ensure obtaining robust results, we employ Hansen's SPA test (2005)...

In this paper, we investigate the information contents of S&P 500 VIX index and range-based volatilities by comparing their benefits on the GJR-based volatility forecasting performance. To reveal the statistical significance and ensure obtaining robust results, we employ Hansen's SPA test (2005)...

In this the paper we investigate the oil price volatility, by studying the causal relationships between different volatilities captured at different time scales. We first decompose the oil price volatility at various scales of resolution or frequency ranges by using wavelet analysis. We then...

This paper examines the effects of the current financial crisis on the correlations of four international banking stocks. We find that in the beginning of the crisis banks generally show a transition to a higher correlation followed by a dramatic decline towards the end of 2008. These findings...

This paper aims to describe bias estimates when non-stationary variance is not detected. We first present a theoretical multivariate GARCH model with structural changes in variance. Then we describe the non-stationary variance and Volatility Causality in the case of the US and the three...

This paper examines effects of realized covariance matrix estimators based on high-frequency data on large-scale minimum-variance equity portfolio optimization. The main results are: (i) the realized covariance matrix estimators yield a lower standard deviation of large-scale portfolio returns...

In this paper we use high-frequency multivariate data and attempt to model the joint distribution (dependence structure) of daily KSE-100 returns, S&P 500 and SSE 180 index. We compute portfolio Value at Risk (VaR) using Archimedean copula for three multivariate models, which were used to model...

The presence of long memory in Finnish stock market return data is tested using nonparametric methods. The data set has daily returns on six indices and forty companies. Depending on the testing method used, statistically significant long memory is detected in 24% to 67% of the series. This is...