This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models’ performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by linking spot volatility with different dates by using the series of the underlying’s returns. Moreover, in contrast to traditional returns-based Maximum Likelihood Estimation (MLE), a joint MLE with returns and VIX improves option pricing performance, and for NGARCH, joint MLE can yield empirically almost the same out-of-sample option pricing performance as direct calibration does to in-sample options, but without costly computations. Finally, consistently with the existing research, this paper finds that non-affine models clearly outperform affine models.

Regulators and governance activists are pressuring firms to abolish CEO duality (the Chief Executive Officer is also the Chairman of the Board). However, the literature provides mixed evidence on the relation between CEO duality and firm performance. Using the exogenous shock of the 1989 Canada–United States Free Trade Agreement, we find that duality firms outperform non-duality firms by 3–4% when their competitive environments change. Further, the performance difference is larger for firms with higher information costs and better corporate governance. Our results underscore the benefits of CEO duality in saving information costs and making speedy decisions.

The paper empirically explores how more trade transparency affects market liquidity. The analysis takes advantage of a unique setting in which the Shanghai Stock Exchange offered more trade transparency to market participants subscribing to a new software package. First, the results show that the additional data disclosure increased trading activity, but also increased transactions costs through wider bid–ask spreads. Thus, in contrast to popular policy belief, the paper finds that more transparency need not improve market liquidity. Second, the paper finds a particularly strong immediate liquidity impact accompanied by altered trading behavior, which suggests a significant impact on institutional traders subscribing relatively early. Lastly, since the effective level of market transparency is bound to depend on how many traders are subscribing to the data, the study can empirically establish the functional form between market-wide transparency and liquidity. The relationship is non-monotonic, which can explain the lack of consensus in the existing literature where each empirical study is naturally confined to specific parts of the transparency domain.

Sousa (2010a) shows that the residuals from the common trend among consumption, financial wealth, housing wealth and human capital, cday, can predict quarterly stock market returns better than cay from Lettau and Ludvigson (2001), which considers aggregate wealth instead. In this paper, we use a more appropriate proxy of human capital, which alleviates the potential correlation between the residuals and the regressors and makes the estimation more precise. In addition, we extend housing wealth to household capital by taking durable goods into consideration. The new predictor is proposed accordingly. Empirically, we find that our predictor is superior to the other alternatives.

Portfolios selected based on the sample covariance estimates may not be stable or robust, particularly so in situations with a large number of assets. The l1 or l2 norm constrained portfolio optimization method has been used as a robust method to control the sparsity or to shrink the estimated weights of assets. In this paper, we propose to add an additional l∞ norm constraint or to add a pairwise l∞ norm constraint in the l1 norm constrained minimum-variance portfolio (MVP) problem. The l∞ constraint controls the largest absolute component of the weight vector and the pairwise l∞ constraint encourages retaining the cluster structure of highly correlated assets in MVP optimization. By simulation study and analysis of empirical data, we find that the proposed portfolios often have better out-of-sample performance in terms of Sharpe ratios, variances and turn-overs than existing popular portfolio strategies including the l1 norm constrained MVP, l2 norm constrained MVP and the 1/N portfolio. In addition, we provide moment shrinkage interpretations of the new strategies and an upper bound of errors in the approximation of the empirical optimal portfolio risk based on the theoretical optimal portfolio risk.