We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.

We provide empirical evidence on the degree of systemic risk in Australia before, during and after the global financial crisis. We calculate a daily index of systemic risk from 2004 to 2013 in order to understand how real economy firms influence the outcomes for the rest of the economy. This is done via a mapping of the interconnectedness of the financial and non-financial sectors. The financial sector is in general home to the most consistently systemically risky firms in the economy. The materials sector occasionally becomes as systemically risky as the financial sector, reflecting the importance of understanding these linkages.

We investigate how family ownership influences the industry-diversifying nature of M&As by listed companies in Continental Europe and the corresponding shareholder value effects at deal announcement. For a large sample of 3485 M&As during 2005-2013, we observe that acquirers having a family as the largest shareholder are less inclined to take over an unrelated target firm than lone-founder and other types of non-family firms. However, as the size of the family ownership stake increases, family firms become more eager to follow an industry-diversifying M&A strategy. While industry-diversifying M&As are associated with lower abnormal returns for acquirer shareholders on average, we also observe that family ownership fully reverses this negative effect. We therefore conclude that those unrelated M&As, although still representing a conflict of interest with the family firm's minority investors, do not destroy shareholder value on average.

Literature on mergers and acquisitions (M&As) performance and wealth effects is abundant. Yet, we know very little about the pre-completion stage, in particular about aspects such as the likelihood of deal closing and time to completion. Understanding the drivers of completion time is however important as prolonged deal duration is costly and postpones realizing synergy gains. In this article, we study the antecedents of deal duration for a sample of 1150 M&As between listed US companies during 1994-2011. Not surprisingly, deal complexity critically affects time to completion. Stock offers, deal hostility, mergers and larger deals are characterized by a lengthier acquisition duration. Strong and clear shareholder support accelerates deal completion, as does the likelihood of overpayment. Finally, experienced bidders succeed in more rapidly completing transactions, implying learning effects.

The level of acquisition premia is of paramount importance in light of the vast sums paid to target shareholders and the often disappointing returns realized by corporate buyers. In this letter, we focus on the impact of R&D investments by targets on the acquisition premium contingent upon the acquirer's financing choices. Based on a unique hand-collected sample of 407 listed European transactions, we find a positive effect of target R&D on premia paid. Yet, when acquirers finance the acquisition of an R&D intensive target with debt, the positive relation disappears. Consequently, we establish that financing sources affect bidding strategies of acquiring companies in case of difficult-to-value targets.

We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under five data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that synchronizing with previous tick interpolation combined with the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances. A minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics.

theories mentioned in research on executive compensation to each of these paradigms