Synthetic portfolio for event studies: Estimating the effects of volatility call auctions

We propose a method denoted as synthetic portfolio for event studies in market microstructure that is particularly interesting to use with high frequency data and thinly traded markets. The method is based on Synthetic Control Method and provides a robust data driven method to build a counterfactual for evaluating the effects of the volatility call auctions. We find that SMC could be used if the loss function is defined as the difference between the returns of the asset and the returns of a synthetic portfolio. We apply SCM to test the performance of the volatility call auction as a circuit breaker in the context of an event study. We find that for Colombian Stock Market securities, the asynchronicity of intraday data reduces the analysis to a selected group of stocks, however it is possible to build a tracking portfolio. The realized volatility increases after the auction, indicating that the mechanism is not enhancing the price discovery process.

El exceso de confianza y el statu quo dentro de la estructuración de capital: una perspectiva de las finanzas corporativas del comportamiento

El presente artículo, presenta un análisis de las decisiones de estructuración de capital de la compañía Merck Sharp & Dome S.A.S, desde la perspectiva de las finanzas comportamentales, comparando los métodos utilizados actualmente por la compañía seleccionada con la teoría tradicional de las finanzas, para así poder evaluar el desempeño teórico y real. Incorporar elementos comportamentales dentro del estudio permite profundizar más sobre de las decisiones corporativas en un contexto más cercano a los avances investigativos de las finanzas del comportamiento, lo cual lleva a que el análisis de este artículo se enfoque en la identificación y entendimiento de los sesgos de exceso de confianza y statu quo, pero sobre todo su implicación en las decisiones de financiación. Según la teoría tradicional el proceso de estructuración de capital se guía por los costos, pero este estudio de caso permitió observar que en la práctica esta relación de costo-decisión está en un segundo lugar, después de la relación riesgo-decisión a la hora del proceso de estructuración de capital.

Measuring and testing for the systemically important financial institutions

This paper analyzes the measure of systemic importance ∆CoV aR proposed by Adrian and Brunnermeier (2009, 2010) within the context of a similar class of risk measures used in the risk management literature. In addition, we develop a series of testing procedures, based on ∆CoV aR, to identify and rank the systemically important institutions. We stress the importance of statistical testing in interpreting the measure of systemic importance. An empirical application illustrates the testing procedures, using equity data for three European banks.

Network externalities across financial institutions

We propose and estimate a financial distress model that explicitly accounts for the interactions or spill-over effects between financial institutions, through the use of a spatial continuity matrix that is build from financial network data of inter bank transactions. Such setup of the financial distress model allows for the empirical validation of the importance of network externalities in determining financial distress, in addition to institution specific and macroeconomic covariates. The relevance of such specification is that it incorporates simultaneously micro-prudential factors (Basel 2) as well as macro-prudential and systemic factors (Basel 3) as determinants of financial distress. Results indicate network externalities are an important determinant of financial health of a financial institutions. The parameter that measures the effect of network externalities is both economically and statistical significant and its inclusion as a risk factor reduces the importance of the firm specific variables such as the size or degree of leverage of the financial institution. In addition we analyze the policy implications of the network factor model for capital requirements and deposit insurance pricing.