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
On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity Staff Working Paper 2020-42 Ruben Hipp Banks’ business interactions create a network of relationships that are hidden in the correlations of bank stock returns. But for policy interventions, we need causality to understand how the network changes. Thus, this paper looks for the causal network anticipated by investors. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial markets, Financial stability JEL Code(s): C, C1, C3, C32, C5, C58, L, L1, L14
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
Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects Staff Working Paper 2019-16 Kerem Tuzcuoglu Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals. Content Type(s): Staff research, Staff working papers Topic(s): Credit risk management, Econometric and statistical methods, Economic models JEL Code(s): C, C2, C23, C25, C5, C58, G, G2, G24
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
Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement Staff Working Paper 2018-21 Elena Goldman, Xiangjin Shen We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Payment clearing and settlement systems JEL Code(s): C, C5, C58, G, G1, G19, G2, G23, G28
A Calibrated Model of Intraday Settlement Staff Discussion Paper 2018-3 Héctor Pérez Saiz, Siddharth Untawala, Gabriel Xerri This paper estimates potential exposures, netting benefits and settlement gains by merging retail and wholesale payments into batches and conducting multiple intraday settlements in this hypothetical model of a single "calibrated payments system." The results demonstrate that credit risk exposures faced by participants in the system are largely dependent on their relative activity in the retail and wholesale payments systems. Content Type(s): Staff research, Staff discussion papers Topic(s): Econometric and statistical methods, Financial stability, Payment clearing and settlement systems JEL Code(s): C, C5, C58, G, G2, G21, G23
Tail Risk in a Retail Payment System: An Extreme-Value Approach Staff Discussion Paper 2018-2 Héctor Pérez Saiz, Blair Williams, Gabriel Xerri The increasing importance of risk management in payment systems has led to the development of an array of sophisticated tools designed to mitigate tail risk in these systems. In this paper, we use extreme value theory methods to quantify the level of tail risk in the Canadian retail payment system (ACSS) for the period from 2002 to 2015. Content Type(s): Staff research, Staff discussion papers Topic(s): Econometric and statistical methods, Financial stability, Payment clearing and settlement systems JEL Code(s): C, C5, C58, G, G2, G21, G23
On the Tail Risk Premium in the Oil Market Staff Working Paper 2017-46 Reinhard Ellwanger This paper shows that changes in market participants’ fear of rare events implied by crude oil options contribute to oil price volatility and oil return predictability. Using 25 years of historical data, we document economically large tail risk premia that vary substantially over time and significantly forecast crude oil futures and spot returns. Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods, Financial markets JEL Code(s): C, C5, C53, C58, D, D8, D84, E, E4, E44, G, G1, G12, G13, Q, Q4, Q43