No-arbitrage dynamic term structure models (DTSMs) have regularly been used to estimate interest rate expectations and term premia, but are beset by empirical challenges. I propose augmenting DTSMs with overnight indexed swap (OIS) rates to better estimate the decomposition along the term structure at daily frequencies. A Gaussian affine DTSM, augmented with 3 to 24-month OIS rates, generates estimates of US expectations that closely correspond to survey-implied measures out to a 10-year horizon and are more stable across sub-samples, compared to existing models. In addition, I provide narrative evidence, in the form of an event study around US unconventional monetary policy announcements, to further exemplify the benefits from OIS augmentation.

To capture systemic risk related to network structures, this paper introduces a measure that complements direct exposures with common exposures, as well as compares these to each other. Trying to address the interconnected nature of financial systems, researchers have recently proposed a range of approaches for assessing network structures. Much of the focus is on direct exposures or market-based estimated networks, yet little attention has been given to the multivariate nature of systemic risk, indirect exposures and overlapping portfolios. In this regard, we rely on correlation network models that tap into the multivariate network structure, as a viable means to assess common exposures and complement direct linkages. Using BIS data, we compare correlation networks with direct exposure networks based upon conventional network measures, as well as we provide an approach to aggregate these two components for a more encompassing measure of interconnectedness.

In this study we examine the effect of boardroom social capital, defined as the aggregate benefits from the social networks of outside directors, on director compensation. Using a large panel of nine thousand firm-year observations for the period 2007–2013, we find that boardroom social capital is positively priced. Further analysis shows that firms pay a premium for networked directors. Firms that have suffered adverse events such as a bad merger, performance declines, or dividend cuts, pay a higher connection premium. We determine that well-connected directors perform important board roles and hold multiple directorships. Overall, our results are consistent with an efficient contracting explanation for boardroom pay.

We offer new evidence on the risk versus mispricing explanations for the R&D anomaly. Return covariance with a characteristic-based factor captures the cross-sectional return variation on R&D portfolios not explained by asset pricing models. This is consistent with both covariance risk and mispricing. Under the framework of the ICAPM, we find little economic justification that an R&D factor is a proxy for innovations to a state variable. The characteristic subsumes the factor loading in direct tests, providing support to the mispricing hypothesis. Investigating the mispricing explanation further, we reject the assertion that the R&D anomaly arises from the correction of stocks mispriced by investor sentiment. A natural experiment exploiting the pilot program under Regulation SHO shows no evidence that the anomaly persists due to limits to arbitrage in the form of short sale constraints.

Using comprehensive bank-loan contract information, we show that the power of a firm relative to its suppliers eases its terms of bank financing, specifically through lower loan prices and less restrictive non-price contract terms. Our results are robust to controlling for product-market competition. Supply chain power enables the firm to achieve a greater level of control over its inventory, constituting a significant portion of the reduction in its overall loan cost. We argue that it is important to consider supply-chain related issues when analyzing the external-financing capacity of firms.

Analysis of shareholdings reveals that corporate directors generate positive alpha when they hold board interlocked stocks, where they are not an insider but a current co-board member is. In contrast, directors do not outperform when they hold inside stocks or other stocks unconnected to the board network. Analysis of trades shows that directors outperform when they buy or sell their own company's stock as insiders. They also outperform when they buy interlocked stocks. Results are similar for trades made before firm-specific information events. We also find limited support for the hypothesis that industry familiarity improves performance.

Portfolio selection based on high-dimensional covariance matrices is a key challenge in data-rich environments with the curse of dimensionality severely affecting most of the available covariance models. We challenge several multivariate Dynamic Conditional Correlation (DCC)-type and Stochastic Volatility (SV)-type models to obtain minimum-variance and mean-variance portfolios with up to 1000 assets. We conclude that, in a realistic context in which transaction costs are taken into account, although DCC-type models lead to portfolios with lower variance, modeling the covariance matrices as latent Wishart processes with a shrinkage towards the diagonal covariance matrix delivers more stable optimal portfolios with lower turnover and higher information ratios. Our results reconcile previous findings in the portfolio selection literature as those claiming for equicorrelations, a smooth dynamic evolution of correlations or correlations close to zero.

We use Finnish OMX Helsinki data to examine the relationship between demographic variables, individual investors' broker choices and trade informativeness. We find that men prefer to use Full-Service-Retail over Discount-Retail brokers and that a higher level of income leads to a higher likelihood of using Discount-Retail brokers. Women present more heterogenous broker choice behaviors. However, both genders are more likely to carry out larger trades through Discount-Retail brokers. We show that trades executed via Discount-Retail brokers are more informative than those of Full-Service-Retail brokers and that only Discount-Retail brokers show trade informativeness differences across gender after controlling for age. Collectively, women make more informative trades then men, but this result reverses after partitioning by age. We conclude that conditioning on the type of broker reduces unobserved individual investor heterogeneity and that demographic variables are essential to the understanding of broker clientele effect. Furthermore, clientele differences observed across broker types are market specific and dominate the effects of financial advice in determining trade informativeness.