C58 - Financial Econometrics

• On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity

  Staff Working Paper 2020-42 Ruben Hipp

  Various business interactions of banks create a network of hidden relationships, which cannot be directly inferred from the correlation of bank stock returns. Without causality, it remains unclear how policy interventions change the network. Thus, this paper aims to find the causal network as 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

• Credit Risk and Collateral Demand in a Retail Payment System

  Staff Discussion Paper 2016-16 Héctor Pérez Saiz, Gabriel Xerri

  The recent financial crisis has led to the development of new regulations to control risk in designated payment systems, and the implementation of new credit risk management standards is one of the key issues. In this paper, we study various credit risk management schemes for the Canadian retail payment system (ACSS) that are designed to cover the exposure of a defaulting member.

  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

• Measuring Systemic Risk Across Financial Market Infrastructures

  Staff Working Paper 2016-10 Fuchun Li, Héctor Pérez Saiz

  We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant.

  Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial stability, Payment clearing and settlement systems JEL Code(s): C, C5, C58, G, G2, G21, G23