This article studies the impact of modeling time-varying covariances/correlations of hedge fund returns in terms of hedge fund portfolio construction and risk measurement. We use a variety of static and dynamic covariance/correlation prediction models and compare the optimized portfolios’ out-of-sample performance. We find that dynamic covariance/correlation models construct portfolios with lower risk and higher out-of-sample risk-adjusted realized return. The tail-risk of the constructed portfolios is also lower. Using a mean-conditional-value-at-risk framework we show that dynamic covariance/correlation models are also successful in constructing portfolios with minimum tail-risk.

The acquisition of a partial stake in a target firm has been positively linked to the likelihood that the target will be involved in a follow on full acquisition involving either the original bidder or a third party bidder. Existing studies provide only suggestive evidence of this linkage by comparing the abnormal returns to partial targets that are ultimately acquired to those that are not. Using a sample of partial acquisitions, we identify characteristics that impact the probability of a full acquisition and provide a tangible link between partial target gains and the ex ante probability of acquisition. Partial targets experience positive announcement effects, and the gains are greater for subsequently acquired targets. Partial bids initiated by corporate bidders are more likely to result in a full acquisition, and the size of the acquired stake and the level of institutional ownership are positively linked to the probability of acquisition. Further, the partial target gains are positively linked to the ex ante probability of acquisition even after controlling for any increased monitoring and discipline that the partial bidder is expected to impose. The findings are robust across various time horizons and model specifications.

In this paper we develop a strongly stable (L-stable) and highly accurate method for pricing exotic options. The method is based on Padé schemes and also utilizes partial fraction decomposition to address issues regarding accuracy and computational efficiency. Due to non-smooth payoffs, which cause discontinuities in the solution (or its derivatives), standard A-stable methods are prone to produce large and spurious oscillations in the numerical solutions which would mislead to estimating options accurately. The proposed method does not suffer these drawbacks while being easy to implement on concurrent processors. Numerical results are presented for digital options, butterfly spread and barrier options in one and two assets. In addition, the methods are tested on the Heston stochastic volatility model.

Most bank merger studies do not control for hidden bailouts, which may lead to biased results. In this study we employ a unique data set of approximately 1000 mergers to analyze the determinants of bank mergers. We use undisclosed information on banks’ regulatory intervention history to distinguish between distressed and non-distressed mergers. Among merging banks, we find that improving financial profiles lower the likelihood of distressed mergers more than the likelihood of non-distressed mergers. The likelihood to acquire a bank is also reduced but less than the probability to be acquired. Both distressed and non-distressed mergers have worse CAMEL profiles than non-merging banks. Hence, non-distressed mergers may be motivated by the desire to forestall serious future financial distress and prevent regulatory intervention.