Enhancing CVaR portfolio optimisation performance with GAM factor models

ArXiv ID: 2401.00188 “View on arXiv”

Authors: Unknown

Abstract

We propose a discrete-time econometric model that combines autoregressive filters with factor regressions to predict stock returns for portfolio optimisation purposes. In particular, we test both robust linear regressions and general additive models on two different investment universes composed of the Dow Jones Industrial Average and the Standard & Poor’s 500 indexes, and we compare the out-of-sample performances of mean-CVaR optimal portfolios over a horizon of six years. The results show a substantial improvement in portfolio performances when the factor model is estimated with general additive models.

Keywords: Autoregressive filters, Factor regressions, General additive models (GAM), Mean-CVaR optimization, Out-of-sample performance, Equities (Indices)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric techniques including autoregressive filters, generalized hyperbolic distributions, and GAMs for factor modeling, resulting in high math complexity. It is also empirically rigorous, featuring a six-year out-of-sample backtest on two major indices (Dow Jones and S&P 500) with clear performance metrics, though lacking code or public datasets.

  flowchart TD
    A["Research Goal"] --> B["Data Preparation<br/>DJIA & S&P 500 Indices"]
    B --> C["Model Estimation<br/>GAM vs. Robust Linear Regression"]
    C --> D["Forecast Generation<br/>Autoregressive Filters + Factor Regressions"]
    D --> E["Portfolio Optimization<br/>Mean-CVaR Optimal Portfolios"]
    E --> F["Out-of-Sample Evaluation<br/>6-Year Horizon"]
    F --> G["Key Finding<br/>GAM factor models substantially<br/>improve portfolio performance"]