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TOPIC:
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WE MODELED LONG MEMORY WITH JUST ONE LAG!
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ABSTRACT
We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time-series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system.
Keywords: Bayesian estimation, Ridge regression, Vector autoregressive model, Forecasting.
JEL Codes: C10, C32, C58.
Click here to view the CV.
Click here to view the paper.
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PRESENTER
Guillaume Chevillon
ESSEC
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RESEARCH FIELDS
Econometrics
Empirical Macroeconomics
Forecasting
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DATE:
8 February 2023 (Wednesday)
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TIME:
9.30am - 11am
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VENUE:
Interactive Learning Room
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903
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