Job Market Paper
Disaster and Fortune Risk in Asset Returns
Do Disaster risk and Fortune risk fetch a premium or discount in the pricing of individual assets? Disaster risk and Fortune risk are measures for the co-movement of individual stocks with the market, given that the state of the world is extremely bad and extremely good, respectively. To address this question measures of Disaster risk and Fortune risk, derived from statistical Extreme Value Theory, are constructed. The measures are non-parametric and the number of order statistics to be used in the analysis is based on the Kolmogorov-Smirnov distance. This alleviates the problem of an arbitrarily chosen extreme region. The extreme dependence measures are used in Fama-MacBeth cross-sectional asset pricing regressions including Market, Fama-French, Liquidity and Momentum factors. I find that Disaster risk fetches a significant premium of 0.34% for the average stock.
Ergun, L.M., Stork, P.A., 2013. Tail Risk Reduction Strategies. In: Wehn, C.S., Hoppe, C., Gregoriou, G.N. (Eds)., Rethinking Valuation and Pricing Models: Lessons Learned from the Crisis and Future Challenges. Academic Press, Elsevier Inc., pp. 457–470.
Tail Index Estimation: Quantile Driven Threshold Selection
(with Jon Danielsson,Laurens de Haan and Casper De Vries)
The selection of upper order statistics in tail estimation is notoriously difficult. Most methods are based on asymptotic arguments, like minimizing the asymptotic mse, that do not perform well in finite samples. Here we advance a data driven method that minimizes the maximum distance between the fitted Pareto type tail and the observed quantile. To analyse the finite sample properties of the metric we organize a horse race between the other methods proposed by the literature. In most cases the finite sample based methods perform best. To demonstrate the economic relevance of choosing the proper methodology we use daily equity return data from the CRSP database and find economically relevant variation between the tail index estimates.
Bias Reduction for Worst Case based Stress Tests using EVT
(with Jon Danielsson and Casper De Vries)
Given that the return distribution has a heavy tail, the non-parametric worst case analysis, i.e the minimum of the sample, is always upwards biased. Relying on semi-parametric extreme value theory (EVT) reduces the bias considerably in the case of very heavy tails. For the less heavy tails this relationship is reversed. We derive the bias for the non-parametric heavy tailed order statistics and contrast it with the semi-parametric EVT approach. Estimates for a large sample of US stock returns indicates that these patterns in the bias are also present in financial data. With respect to risk management, this induces an overly conservative capital allocation.
Identifying Information Flows in Over-the-Counter Markets: Evidence from a Consensus Pricing Service
(with Andreas Uthemann)
This paper provides empirical evidence on information flows in the over-the-counter market for S&P 500 index options using a proprietary data set on price estimates provided by major broker-dealers to a consensus pricing service. We develop a structural model of learning about asset values from public and private information which we then estimate. From the parameter estimates we derive model-implied measures of the informational content of broker-dealers' price estimates and the informational value of the consensus price feedback for its subscribers. We compare these measures across the options' strike price and time-to-maturity space. We find that institutions' price estimates contain a significant amount of new information about option valuations for all strike prices and times-to-maturities. Even the least informationally rich price estimates put almost half of their weight on new information. This figure increases to above 95 percent for the most informationally rich price estimates. The consensus price feedback itself is found to be a valuable source of pricing information across the volatility surface.
Research in progress
Limits to Liquidity Arbitrage in the Treasury Market
(with Andreas Uthemann and Shengxing Zhang)
In this paper we estimate the effect of monetary policy on bond market liquidity through securities lending. Using the change in the yield on treasury bond futures before and after FOMC announcements, we estimate the impact unexpected rate changes have on the cost of borrowing securities in the securities lending market for treasury and corporate bonds.In turn we examine the impacts of this exogenous shock of borrowing on liquidity conditions in the various markets. We approximate liquidity in the bond markets with the noise measure by Hu, Pan and Wang (2013). Our preliminary results indicate that tightening monetary policy increases the cost of securities lending and therefore deteriorates liquidity of the bond market.