No abstract

A change in executive leadership is a significant event in the life of a firm. Our paper investigates a potentially significant consequence of a CEO turnover: a change in equity volatility. We develop several hypotheses about how CEO changes might affect stock price volatility, and test these hypotheses using a sample of 872 CEO changes over the [1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989][1990][1991][1992][1993][1994][1995] period. We find that volatility increases following a CEO turnover, even for the most frequent type, when a CEO leaves voluntarily and is replaced by someone from inside the firm. Our results indicate that forced turnovers, which are expected to result in large strategy changes, increase volatility more than voluntary turnovers. Outside successions, which are expected to result in a successor CEO with less certain skill in managing the firm's operations, increase volatility more than inside turnovers. We also document a greater stock-price response to earnings announcements around CEO turnover, consistent with more informative signals of value driving the increased volatility. Controls for firm-specific characteristics indicate that the volatility changes cannot be entirely attributed to factors such as changes in firm operations, firm size, and both volatility change and performance prior to the turnover.1

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe explore the cross-sectional pricing of volatility risk by decomposing equity market volatility into short-and long-run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short-run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long-run component relates closely to business cycle risk. Furthermore, a three-factor pricing model with the market return and the two volatility components compares favorably to benchmark models. 1When market volatility is stochastic, intertemporal models predict that asset risk premia are not only determined by covariation of returns with the market return, but also covariation with the state variables that govern market volatility. To study this prediction, we model the log-volatility of the market portfolio as the sum of a short-and a long-run volatility component. This approach parsimoniously captures shocks to systematic risk at different horizons.Market volatility is a significant cross sectional asset pricing factor as shown by Ang et al. (2006).1 Their two-factor model with the market return and market volatility does reduce pricing errors compared to the capital asset pricing model (CAPM), though not by as much as the Fama-French model. In contrast, our benchmark asset pricing model with the market return and the two volatility components as cross sectional pricing factors achieves lower pricing errors than the Fama and French (1993) model for size and book-to-market sorted portfolios. Our finding that the short-and long-run volatility components have negative, highly significant prices of risk is robust across sets of portfolios, sub-periods, and volatility model specifications.Consistent with previous research, we also find that the average compensation for volatility risk is positive. This is because the prices of risk of both volatility components are negative, and average sensitivities to the volatility components are also negative. Our two-factor decomposition shows that the average risk premium for short-run volatility is 0.17% monthly versus 0.23% monthly for long-run volatility.Across individual portfolios, we see a wide dispersion in sensitivity to the volatility components, which generates cross sectional varia...

Integrated risk management in a financial institution requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. In this paper, we construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions, both conditional and unconditional, that capture some of the essential empirical features of these risks such as skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk, whether measured by value-at-risk or expected shortfall. We find that given a risk type, total risk is more sensitive to differences in business mix or risk weights than to differences in inter-risk correlations. There is a complex relationship between volatility and fat-tails in determining the total risk: depending on the setting, they either offset or reinforce each other. The choice of copula (normal versus Student-t), which determines the level of tail dependence, has a more modest effect on risk. We then compare the copula-based method with several conventional approaches to computing risk.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe explore the cross-sectional pricing of volatility risk by decomposing equity market volatility into short-and long-run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short-run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long-run component relates closely to business cycle risk. Furthermore, a three-factor pricing model with the market return and the two volatility components compares favorably to benchmark models. 1When market volatility is stochastic, intertemporal models predict that asset risk premia are not only determined by covariation of returns with the market return, but also covariation with the state variables that govern market volatility. To study this prediction, we model the log-volatility of the market portfolio as the sum of a short-and a long-run volatility component. This approach parsimoniously captures shocks to systematic risk at different horizons.Market volatility is a significant cross sectional asset pricing factor as shown by Ang et al. (2006).1 Their two-factor model with the market return and market volatility does reduce pricing errors compared to the capital asset pricing model (CAPM), though not by as much as the Fama-French model. In contrast, our benchmark asset pricing model with the market return and the two volatility components as cross sectional pricing factors achieves lower pricing errors than the Fama and French (1993) model for size and book-to-market sorted portfolios. Our finding that the short-and long-run volatility components have negative, highly significant prices of risk is robust across sets of portfolios, sub-periods, and volatility model specifications.Consistent with previous research, we also find that the average compensation for volatility risk is positive. This is because the prices of risk of both volatility components are negative, and average sensitivities to the volatility components are also negative. Our two-factor decomposition shows that the average risk premium for short-run volatility is 0.17% monthly versus 0.23% monthly for long-run volatility.Across individual portfolios, we see a wide dispersion in sensitivity to the volatility components, which generates cross sectional varia...