Time Varying Market Integration and Expected Rteurns in Emerging Markets

We use a simple model in which the expected returns in emerging markets depend on their systematic risk as measured by their beta relative to the world portfolio as well as on the level of integration in that market.The level of integration is a time-varying variable that depends on the market value of the assets that can be held by domestic investors only versus the market value of the assets that can be traded freely.Our empirical analysis for 30 emerging markets shows that there are strong effects of the level of integration or segmentation on the expected returns in emerging markets.The expected returns depend both on the level of segmentation of the emerging market itself and on the regional segmentation level.We also find that there is significant time-variation in the betas relative to the world portfolio because of the level of segmentation.For the composite index of the emerging markets we find an annual increase in beta of 0.09 due to decreased segmentation of the emerging markets in our sample period.In terms of expected returns the total effect on the composite index translates into an average decrease of 4.5 percent per annum.As predicted by our model, the noninvestable assets are more sensitive to the local and less to the regional level of segmentation than the investable assets.These conclusions do not change when using additional control variables. We do not find a clear pattern between volatility and segmentation, however.return on investment;economic integration;international financial markets;capital markets

High frequency analysis of lead-lag relationships between financial markets

High frequency data are often observed at irregular intervals, which complicates the analysis of lead-lag relationships between financial markets. Frequently, estimators have been used that are based on observations at regular intervals, which are adapted to the irregular observations case by ignoring some observations and imputing others. In this paper we propose an estimator that avoids imputation and uses all available transactions to calculate (cross) covariances. This creates the possibility to analyze lead-lag relationships at arbitrarily high frequencies without additional imputation bias, as long as weak identifiability conditions are satisfied. We also provide an empirical application to the lead-lag relationship between the SP500 index and futures written on it.Financial Markets;finance

An Empirical Analysis of Legal Insider Trading in the Netherlands

In this paper, we employ a registry of legal insider trading for Dutch listed firms to investigate the information content of trades by corporate insiders. Using a standard event-study methodology, we examine short-term stock price behavior around trades. We find that purchases are followed by economically large abnormal returns. This result is strongest for purchases by top execu- tives and for small market capitalization firms, which is consistent with the hypothesis that legal insider trading is an important channel through which information flows to the market. We analyze also the impact of the implementation of the Market Abuse Directive (European Union Directive 2003/6/EC), which strengthens the existing regulation in the Netherlands. We show that the new regulation reduced the information content of sales by top executives.Insider trading;Financial market regulation

Libor and Swap Market Models for the Pricing of Interest Rate Derivatives: An Empirical Analysis

In this paper we empirically analyze and compare the Libor and Swap Market Models, developed by Brace, Gatarek, and Musiela (1997) and Jamshidian (1997), using paneldata on prices of US caplets and swaptions.A Libor Market Model can directly be calibrated to observed prices of caplets, whereas a Swap Market Model is calibrated to a certain set of swaption prices.For both one-factor and two-factor models we analyze how well they price caplets and swaptions that were not used for calibration.We show that the Libor Market Models in general lead to better prediction of derivative prices that were not used for calibration than the Swap Market Models.A one-factor Libor Market Model that exhibits mean-reversion gives a good fit of the derivative prices, and adding a second factor only decreases pricing errors to a small extent.We also find that models that are chosen to exactly match certain derivative prices are overfitted. Finally, a regression analysis reveals that the pricing errors are correlated with the shape of the term structure of interest rates.Term Structure Models;Interest Rate Derivatives;Lognormal Pricing Models;Black Formula