Extreme correlation of international equity markets

Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. This paper focuses on extreme correlation, that is to say the correlation between returns in either the negative or positive tail of the multivariate distribution. Using "extreme value theory" to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Using monthly data on the five largest stock markets from 1958 to 1996, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets.International equity markets; volatility; correlation and extreme value theory

On the term structure of default premia in the Swap and Libor markets

Existing theories of the term structure of swap rates provide an analysis of the Treasury-swap spread based on either a liquidity convenience yield in the Treasury market, or default risk in the swap market. While these models do not focus on the relation between corporate yields and swap rates (the LIBOR-Swap spread), they imply that the term structure of corporate yields and swap rates should be identical. As documented previously (e.g. in Sun, Sundares and Wang (1993)) this is counter-factual. Here, we propose a simple model of the (complex) default risk imbedded in the swap term structure that is able to explain the LIBOR-swap spread. Whereas corporate bonds carry default risk, we argue that swaps should bear less default risk. In fact, we assume that swap contracts are free of default risk. Because swaps are indexed on "refreshed"-credit-quality LIBOR rates, the spread between corporate yields and swap rates should capture the market's expectations of the probability of deterioration in credit quality of a corporate bond issuer. We model this feature and use our model to estimate the likelihood of future deterioration in credit quality from the LIBOR-swap spread. The analysis is important because it shows that the term structure of swap rates does not reflect the borrowing cost of a standard LIBOR credit quality issuer. It also has implications for modeling the dynamics of the swap term structure.Credit risk; asset pricing; international finance

Correlation Structure of International Equity Markets During Extremely Volatile Periods

Correlation in international equity returns is unstable over time. It has been suggested that the international correlation of large stock returns, especially negative ones, differs from that of usual returns. It is in periods of extreme negative returns that the benefits of international risk diversification are most desired and that the question of international correlation is most relevant to risk-averse agents. If return distributions are not multivariate normal, the usual standard deviation and correlation of returns do not provide sufficient information. Additional information can be gained by focusing directly on the properties of extreme returns. While the interest in stock market crashes and booms is large, no study has specifically focused on the correlation between large price movements. A major econometric issue is to specify the multivariate distribution of extreme returns implied by a given distribution of returns. In this paper, we work directly on large returns and study the dependence structure of international equity markets during extremely volatile periods. We use the results of extreme value theory to model the multivariate distribution of large returns, using monthly data from January 1959 to December 1996 for the five largest stock markets. We find that the correlation of large positive returns are not inconsistent with the assumption of multivariate normality. However, the correlation of large negative returns is much greater than expected, suggesting that the benefits of international risk reduction in extremely volatile periods have been overstated.international equity market; volatility; correlation; extreme value theory

What Determines Expected International Asset Returns?

This paper characterizes the forces that determine time-variation in expected international asset returns. We offer a number of innovations. By using the latent factor technique, we do not have to prespecify the sources of risk. We solve for the latent premiums and characterize their time-variation. We find evidence that the first factor premium resembles the expected return on the world market portfolio. However, the inclusion of this premium alone is not sufficient to explain the conditional variation in the returns. We find evidence of a second factor premium which is related to foreign exchange risk. Our sample includes new data on both international industry portfolios and international fixed income portfolios. We find that the two latent factor model performs better in explaining the conditional variation in asset returns than a prespecified two factor model. Finally, we show that differences in the risk loadings are important in accounting for the cross-sectional variation in the international returns.

What Determines Expected International Asset Returns?

This paper characterizes the forces that determine time-variation in expected international asset returns. We offer a number of innovations. By using the latent factor technique, we do not have to prespecify the sources of risk. We solve for the latent premiums and characterize their time-variation. We find evidence that the first factor premium resembles the expected return on the world market porfolio. However, the inclusion of this premium alone is not sufficient to explain the conditional variation in the returns. We find evidence of a second factor premium which is related to foreign exchange risk. Our sample includes new data on both international industry portfolios and international fixed income portfolios. We find that the two latent factor model performs better in explaining the conditional variation in asset returns than a prespecified two factor model. Finally, we show that differences in the risk loadings are important in accounting for the cross-sectional variation in the international returns.International investment, Asset pricing, Latent variables, Exchange rate risk, Factor models

Teager-Kaiser Operator improves the accuracy of EMG onset detection independent of signal-to-noise ratio

A temporal analysis of electromyographic (EMG) activity has widely been used for non-invasive study of muscle activation patterns. Such an analysis requires robust methods to accurately detect EMG onset. We examined whether data conditioning, supplemented with Teager–Kaiser Energy Operator (TKEO), would improve accuracy of the EMG burst onset detection. EMG signals from vastus lateralis, collected during maximal voluntary contractions, performed by seventeen subjects (8 males, 9 females, mean age of 46 yrs), were analyzed. The error of onset detection using enhanced signal conditioning was significantly lower than that of onset detection performed on signals conditioned without the TKEO (40 ±99 ms vs. 229 ±356 ms, t-test, p = 0.023). The Pearson correlations revealed that neither accuracy after enhanced conditioning nor accuracy after standard conditioning was significantly related to signal-to-noise ratio (SNR) (r = −0.05, p = 0.8 and r = −0.19, p = 0.46, respectively). It is concluded that conditioning of the EMG signals with TKEO significantly improved the accuracy of the threshold-based onset detection methods, regardless of SNR magnitude. Originally published Acta Bioeng Biomech, Vol. 10, No. 2, 200

Principal Regression Analysis and the index leverage effect

We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call `Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode {\it away} from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.Comment: 10 pages, 7 figure

Stock market integration for the transition economies: Time-varying conditional correlation approach

This is the accepted version of the following article: WANG, P. and MOORE, T. (2008), Stock market integration for the transition economies: Time-varying conditional correlation approach. The Manchester School, 76: 116–133. doi: 10.1111/j.1467-9957.2008.01083.x, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/j.1467- 9957.2008.01083.x/abstract.In this paper, we investigate the extent to which the three emerging Central Eastern European stock markets have become integrated with the aggregate eurozone market over the sample period from 1994 to 2006 by utilizing the dynamic conditional correlation. We find a higher level of the stock market correlation during the period after the Asian and Russian crises and also during the post-entry period to the European Union. It is found that financial market integration seems to be a largely self-fuelling process, depending on existing levels of financial sector development for the Czech Republic and Hungary

An Innovative Influenza Vaccination Policy: Targeting Last Season's Patients

Influenza vaccination is the primary approach to prevent influenza annually. WHO/CDC recommendations prioritize vaccinations mainly on the basis of age and co-morbidities, but have never considered influenza infection history of individuals for vaccination targeting. We evaluated such influenza vaccination policies through small-world contact networks simulations. Further, to verify our findings we analyzed, independently, large-scale empirical data of influenza diagnosis from the two largest Health Maintenance Organizations in Israel, together covering more than 74% of the Israeli population. These longitudinal individual-level data include about nine million cases of influenza diagnosed over a decade. Through contact network epidemiology simulations, we found that individuals previously infected with influenza have a disproportionate probability of being highly connected within networks and transmitting to others. Therefore, we showed that prioritizing those previously infected for vaccination would be more effective than a random vaccination policy in reducing infection. The effectiveness of such a policy is robust over a range of epidemiological assumptions, including cross-reactivity between influenza strains conferring partial protection as high as 55%. Empirically, our analysis of the medical records confirms that in every age group, case definition for influenza, clinical diagnosis, and year tested, patients infected in the year prior had a substantially higher risk of becoming infected in the subsequent year. Accordingly, considering individual infection history in targeting and promoting influenza vaccination is predicted to be a highly effective supplement to the current policy. Our approach can also be generalized for other infectious disease, computer viruses, or ecological networks