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420 Results

Gazing at r-star: A Hysteresis Perspective

Staff working paper 2023-5 Paul Beaudry, Katya Kartashova, Césaire Meh
Many explanations for the decline in real interest rates over the last 30 years point to the role that population aging or rising income inequality plays in increasing the long-run aggregate demand for assets. Notwithstanding the importance of such factors, the starting point of this paper is to show that the major change driving household asset demand over this period is instead an increased desire—for a given age and income level—to hold assets.

Good Volatility, Bad Volatility and Option Pricing

Staff working paper 2017-52 Bruno Feunou, Cédric Okou
Advances in variance analysis permit the splitting of the total quadratic variation of a jump diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions.

Macroeconomic Predictions Using Payments Data and Machine Learning

Staff working paper 2022-10 James Chapman, Ajit Desai
We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.

Estimation and Inference for Stochastic Volatility Models with Heavy-Tailed Distributions

Statistical inference--both estimation and testing--for stochastic volatility (SV) models is known to be challenging and computationally demanding. We propose simple and efficient estimators for SV models with conditionally heavy-tailed error distributions, particularly the Student’s t and Generalized Exponential Distributions (GED). The estimators rely on a small set of moment conditions derived from ARMA-type representations of SV models, with an option to apply “winsorization” to improve stability and finite-sample performance. Except for the degrees of-freedom parameter, closed-form expressions are available for all other parameters, extending Ahsan and Dufour (2019, 2021), thus eliminating the need for numerical optimization or initial values. We derive the estimators’ asymptotic distribution and show that, due to their analytical tractability, they support reliable, and even exact, simulation-based inference via Monte Carlo or bootstrap methods. We assess their performance through extensive simulations and demonstrate their practical relevance in financial return data, which strongly reject the normality assumption in favor of heavy-tailed models.

Losing Contact: The Impact of Contactless Payments on Cash Usage

Staff working paper 2020-56 Marie-Hélène Felt
Contactless payment cards are a competitive alternative to cash. Using Canadian panel data from 2010 to 2017, this study investigates whether contactless credit cards are an important contributor to the decline in the transactional use of cash. 

On-the-run Premia, Settlement Fails, and Central Bank Access

Staff working paper 2025-19 Fabienne Schneider
The premium on “on-the-run” Treasuries is an anomaly. I explain it using a model in which primary dealers hold inventories of Treasuries. I use the model to analyze the effects of granting access to central bank facilities.

Fiscal and Monetary Stabilization Policy at the Zero Lower Bound: Consequences of Limited Foresight

Staff working paper 2021-51 Michael Woodford, Yinxi Xie
How do outcomes of monetary and fiscal stabilization policies at the zero lower bound change when decision makers have finite planning horizons in the economy? We explore the effects of limited foresight on policy tools and the interaction between monetary and fiscal policy.

Interest Rate Uncertainty as a Policy Tool

Staff working paper 2020-13 Fabio Ghironi, Galip Kemal Ozhan
We study a novel policy tool—interest rate uncertainty—that can be used to discourage inefficient capital inflows and to adjust the composition of external account between shortterm securities and foreign direct investment (FDI).
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