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Related Experiment Video

Updated: May 29, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Introducing shrinkage in heavy-tailed state space models to predict equity excess returns.

Florian Huber¹, Gregor Kastner², Michael Pfarrhofer³

¹Department of Economics, University of Salzburg, Salzburg, Austria.

Empirical Economics

|February 7, 2025

Summary

This study introduces a flexible Bayesian econometric model to forecast S&P 500 excess returns, outperforming traditional methods. The advanced model incorporates non-Gaussian features for more accurate financial market predictions.

Keywords:

Dynamic regression Fundamental factors Non-Gaussian models S Stochastic volatility

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Area of Science:

Econometrics
Financial Modeling
Bayesian Statistics

Background:

Forecasting excess returns of the S&P 500 index is crucial for investment strategies.
Existing models often struggle with non-Gaussian features and overparameterization.

Purpose of the Study:

To develop and evaluate a flexible Bayesian econometric state space model for forecasting S&P 500 excess returns.
To incorporate non-Gaussian features, shrinkage priors, and heavy-tailed innovations for improved forecast accuracy.

Main Methods:

Utilized a flexible Bayesian econometric state space model.
Employed global-local shrinkage priors to control overparameterization.
Incorporated heavy-tailed state innovations and leptokurtic stochastic volatility.

Main Results:

The proposed Bayesian model demonstrated superior performance compared to common competitors.
The model achieved better point and density forecasts for S&P 500 excess returns.
Shrinkage priors and heavy-tailed innovations effectively managed model complexity and potential breaks.

Conclusions:

The flexible Bayesian state space model offers a robust framework for financial return forecasting.
Accounting for non-Gaussian features and employing advanced Bayesian techniques enhances predictive accuracy.
This approach provides a valuable tool for quantitative analysts and researchers in finance.