The literature on shareholder voting has mostly focused on the influence of proxy advisors on shareholder votes. We exploit a unique empirical setting enabling us to provide a direct estimate of management's influence. Analyzing shareholder votes on the frequency of future say on pay votes, we find that a management recommendation for a particular frequency is associated with a 26% increase in voting support for that frequency. Additional tests suggest that the documented association is likely to capture a causal effect. Management influence varies across firms and is smaller at firms where perceived management credibility is lower. Compared to firms adopting an annual frequency, firms following management's recommendation to adopt a triennial frequency are significantly less likely to change their compensation practices in response to an adverse say on pay vote, consistent with the notion that a less frequent vote results in lower management accountability.
This article studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these results, we obtain optimal bounded-influence versions of the classical likelihood-based tests for parametric hypotheses. We propose a feasible and efficient algorithm for the computation of our robust estimators, which uses analytical Laplace approximations to estimate the auxiliary recentering vectors, ensuring Fisher consistency in robust estimation. This strongly reduces the computation time by avoiding the simulation of multidimensional integrals, a task that typically must be addressed in the robust estimation of nonlinear models for time series. In some Monte Carlo simulations of an AR(1)-ARCH(1) process, we show that our robust procedures maintain a very high efficiency under ideal model conditions and at the same time perform very satisfactorily under several forms of departure from conditional normality. In contrast, classical pseudo-maximum likelihood inference procedures are found to be highly inefficient under such local model misspecifications. These patterns are confirmed by an application to robust testing for autoregressive conditional heteroscedasticity.
I examine the information content of a limit order book in a purely order-driven market. I analyze how the state of the limit order book affects a trader's strategy. I develop an econometric technique to study order aggressiveness and provide empirical evidence on the recent theoretical models on limit order book markets. My results show that patient traders become more aggressive when the own (opposite) side book is thicker (thinner), the spread wider, and the temporary volatility increases. Also, I find that the buy and the sell sides of the book affect the order submission differently.
Arbitrage ensures that covered interest parity holds. The condition
is central to price foreign exchange forwards and interbank lending
rates, and reflects the efficient functioning of markets. Normally,
deviations from arbitrage, if any, last seconds and reach a few basis
points. But after the Lehman bankruptcy, arbitrage broke down.
By replicating exactly two major arbitrage strategies and using high
frequency prices from novel datasets, this paper shows that arbitrage
profits were large, persisted for months and involved borrowing in dollars.
Empirical analysis suggests that insufficient funding liquidity in
dollars kept traders from arbitraging away excess profits.
This study is an empirical analysis of the intraday market liquidity and volume concentration on the Swiss Stock Exchange. The intraday market liquidity on the Swiss market exhibits a triple-U shaped pattern. An intraday pattern of volume concentration also exists. The empirical evidence shows that the US market influences the Swiss trading day to a remarkable extent. The results also suggest the dynamics of an order-driven market. Disequilibrium between demand and supply conditions are associated with an increase in trading volume and a thinner limit order book. In this market condition, trades engender a wider spread and price volatility.
This note is aimed at familiarizing the reader with state space geometry, a useful tool in teaching asset pricing concepts. Building on the analogy between expectation and dot product, I visualize basic notions using Euclidean geometry in 2D and 3D. Numerical examples are given such that the reader could easily follow the explanations.
