Lerby Ergun - Latest

• Covariates Hiding in the Tails

  Staff Working Paper 2021-45 Milian Bachem, Lerby Ergun, Casper G. de Vries

  We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Strategic Uncertainty in Financial Markets: Evidence from a Consensus Pricing Service

  Staff Working Paper 2020-55 Lerby Ergun, Andreas Uthemann

  We look at the informational content of consensus pricing in opaque over-the-counter markets. We show that the availability of price data informs participants mainly about other participants’ valuations, rather than about the value of a financial security.

  Content Type(s): Staff research, Staff working papers Topic(s): Financial institutions, Financial markets, Market structure and pricing JEL Code(s): C, C5, C58, D, D5, D53, D8, D83, G, G1, G12, G14

• Extreme Downside Risk in Asset Returns

  Staff Working Paper 2019-46 Lerby Ergun

  Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions.

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C1, C14, G, G1, G11, G12

• Tail Index Estimation: Quantile-Driven Threshold Selection

  Staff Working Paper 2019-28 Jon Danielsson, Lerby Ergun, Casper G. de Vries, Laurens de Haan

  The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58

• Challenges in Implementing Worst-Case Analysis

  Staff Working Paper 2018-47 Jon Danielsson, Lerby Ergun, Casper G. de Vries

  Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

  Content Type(s): Staff research, Staff working papers Topic(s): Financial stability JEL Code(s): C, C0, C01, C1, C14, C5, C58