C58 - Financial Econometrics

• Deriving Longer-Term Inflation Expectations and Inflation Risk Premium Measures for Canada

  Staff Discussion Paper 2024-9 Bruno Feunou, Zabi Tarshi

  We present two models for long-term inflation expectations and inflation risk premiums for Canada.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C58, E, E4, E43, E47, G, G1, G12

• Finding the balance—measuring risks to inflation and to GDP growth

  Staff Analytical Note 2023-18 Bruno Feunou, James Kyeong

  Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17

• Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency

  Staff Discussion Paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong

  This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.

  Content Type(s): Staff research, Staff discussion papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17

• Generalized Autoregressive Gamma Processes

  Staff Working Paper 2023-40 Bruno Feunou

  We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.

  Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C5, C58, G, G1, G12

• 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