Search

Content Types

Topics

JEL Codes

Locations

Departments

Authors

Sources

Statuses

Published After

Published Before

16 Results

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.

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.

Tail Index Estimation: Quantile-Driven Threshold Selection

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.

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.

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.

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.

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.

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.
Go To Page