Michael S. Gibson

Bio: Michael S. Gibson is an academic researcher from Federal Reserve System. The author has contributed to research in topics: Portfolio & Risk management. The author has an hindex of 19, co-authored 36 publications receiving 2752 citations.

Pitfalls in Tests for Changes in Correlations

Abstract: Correlations are crucial for pricing and hedging derivatives whose payoff depends on more than one asset. Typically, correlations computed separately for ordinary and stressful market conditions differ considerably, a pattern widely termed "correlation breakdown." As a result, risk managers worry that their hedges will be useless when they are most needed, namely during "stressful" market situations. We show that such worries may not be justified since "correlation breakdowns" can easily be generated by data whose distribution is stationary and, in particular, whose correlation coefficient is constant. We make this point analytically, by way of several numerical examples, and via an empirical illustration. But, risk managers should not necessarily relax. Although "correlation breakdown" can be an artifact of poor data analysis, other evidence suggests that correlations do in fact change over time, though not in a way that is correlated with "stressful" market conditions.

Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities

Abstract: This paper proposes a method for constructing a volatility risk premium, or investor risk aversion, index. The method is intuitive and simple to implement, relying on the sample moments of the recently popularized model-free realized and option-implied volatility measures. A small-scale Monte Carlo experiment suggests that the procedure works well in practice. Implementing the procedure with actual S&P 500 option-implied volatilities and high-frequency five-minute-based realized volatilities results in significant temporal dependencies in the estimated stochastic volatility risk premium, which we in turn relate to a set of underlying macro-finance state variables. We also find that the extracted volatility risk premium helps predict future stock market returns.

Is Corporate Governance Ineffective in Emerging Markets

Abstract: I test whether corporate governance is ineffective in emerging markets by estimating the link between CEO turnover and firm performance for over 1,200 firms in eight emerging markets. I find two main results. First, CEOs of emerging market firms are more likely to lose their jobs when their firm's performance is poor, suggesting that corporate governance is not ineffective in emerging markets. Second, for the subset of firms with a large domestic shareholder, there is no link between CEO turnover and firm performance. For this subset of emerging market firms, corporate governance appears to be ineffective.

Is Corporate Governance Ineffective in Emerging Markets

Abstract: I test whether corporate governance is ineffective in emerging markets by estimating the link between CEO turnover and firm performance for over 1,200 firms in eight emerging markets. While previous papers on corporate governance in emerging markets have studied corporate governance mechanisms, such as concentrated ownership, I study a corporate governance outcome: are poorly performing managers replaced? Others have answered this question in the affirmative for the United States and other developed countries. This paper is the first to address this question for emerging markets. I find two main results. First, CEOs of emerging market firms are more likely to lose their jobs when their firm’s performance is poor, suggesting that corporate governance is not ineffective in emerging markets. The magnitude of the relationship is surprisingly similar to what Kaplan (1994a) found for the United States. Second, for the subset of firms with a large domestic shareholder, there is no link between CEO turnover and firm performance. For this subset of emerging market firms, corporate governance appears to be ineffective.

No Contagion, Only Interdependence: Measuring Stock Market Comovements

Abstract: Heteroskedasticity biases tests for contagion based on correlation coefficients. When contagion is defined as a significant increase in market comovement after a shock to one country, previous work suggests contagion occurred during recent crises. This paper shows that correlation coefficients are conditional on market volatility. Under certain assumptions, it is possible to adjust for this bias. Using this adjustment, there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash. There is a high level of market comovement in all periods, however, which we call interdependence.

No Contagion, Only Interdependence: Measuring Stock Market Co-Movements

Abstract: This paper examines stock market co-movements. It begins with a discussion of several conceptual issues involved in measuring these movements and how to test for contagion. Standard tests examine if cross-market correlation in stock market returns increase during a period of crisis. The measure of cross-market correlations central to this standard analysis, however, is biased. The unadjusted correlation coefficient is conditional on market movements over the time period under consideration, so that during a period of turmoil when stock market volatility increases, standard estimates of cross-market correlations will be biased upward. It is straightforward to adjust the correlation coefficient to correct for this bias. The remainder of the paper applies these concepts to test for stock market contagion during the 1997 East Asian crises, the 1994 Mexican peso collapse, and the 1987 U.S. stock market crash. In each of these cases, tests based on the unadjusted correlation coefficients find evidence of contagion in several countries, while tests based on the adjusted coefficients find virtually no contagion. This suggests that high market co-movements during these periods were a continuation of strong cross-market linkages. In other words, during these three crises there was no contagion, only interdependence.

What Do a Million Observations on Banks Say about the Transmission of Monetary Policy

Abstract: We study the monetary-transmission mechanism with a data set that includes quarterly observations of every insured U.S. commercial bank from 1976 to 1993. We find that the impact of monetary policy on lending is stronger for banks with less liquid balance sheets--i.e., banks with lower ratios of securities to assets. Moreover, this pattern is largely attributable to the smaller banks, those in the bottom 95 percent of the size distribution. Our results support the existence of a "bank lending channel" of monetary transmission, though they do not allow us to make precise statements about its quantitative importance.

Extreme Correlation of International Equity Markets

Abstract: 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. Using “extreme value theory” to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, 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 MARKET CORRELATION has been widely studied. Previous studies 1 suggest that correlation is larger when focusing on large absolutevalue returns, and that this seems more important in bear markets. The conclusion that international correlation is much higher in periods of volatile markets ~large absolute returns! has indeed become part of the accepted wisdom among practitioners and the financial press. However, one should exert great care in testing such a proposition. The usual approach is to condition the estimated correlation on the observed ~or ex post! realization of market returns. Unfortunately correlation is a complex function of returns and such tests can lead to wrong conclusions, unless the null hypothesis and

Risk Management: Correlation and Dependence in Risk Management: Properties and Pitfalls

Abstract: Modern risk management calls for an understanding of stochastic dependence going beyond simple linear correlation. This paper deals with the static (non-time-dependent) case and emphasizes the copula representation of dependence for a random vector. Linear correlation is a natural dependence measure for multivariate normally and, more generally, elliptically distributed risks but other dependence concepts like comonotonicity and rank correlation should also be understood by the risk management practitioner. Using counterexamples the falsity of some commonly held views on correlation is demonstrated; in general, these fallacies arise from the naive assumption that dependence properties of the elliptical world also hold in the non-elliptical world. In particular, the problem of finding multivariate models which are consistent with prespecified marginal distributions and correlations is addressed. Pitfalls are highlighted and simulation algorithms avoiding these problems are constructed.