Book publications

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.

Working Papers

Identifying Information Flows in Over-the-Counter Markets: Evidence from a Consensus Pricing Service
(with Andreas Uthemann)

Winner ESEM Best paper Award 2017

We assess the ability of an information aggregation mechanism to reduce valuation uncertainty in an over-the-counter market. The analysis is based on a unique dataset of price estimates for S&P 500 index options that major financial institutions provide to a consensus pricing service. We consider two dimensions of uncertainty: uncertainty about fundamental asset values and strategic uncertainty about competitors' valuations. We estimate a structural model of learning from prices. From this, we obtain empirical measures of fundamental and strategic uncertainty that are based on market participants' posterior beliefs. Both dimensions of valuation uncertainty vary substantially across the different segments of the market. We use the structural model to assess subscribers' welfare under alternative information structures. We show that the main contribution of the service is to reduce subscribers' uncertainty about competitors' valuations rather than uncertainty about asset values themselves.

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.

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.

Online Appendix

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.

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.

The Term Structure of Uncertainty
(with Andreas Uthemann, Jon Danielsson and Jean-Pierre Zigrand)

Hedge Fund Activism in the Securities Lending Market
(Miguel Burguet, Andreas Uthemann, and Cheng Zhang)