Kasper Johansson, Mehmet Giray Ogut, Markus Pelger, Thomas Schmelzer, Stephen Boyd
arXiv 31 May 2023 · Econometrics · publishedFoundations and Trends® in Econometrics (2023) · 12 citations (OpenAlex)
arXiv:2305.19484 · PDF · DOI · OpenAlex · Extracted main text
We consider the well-studied problem of predicting the time-varying covariance matrix of a vector of financial returns. Popular methods range from simple predictors like rolling window or exponentially weighted moving average (EWMA) to more sophisticated predictors such as generalized autoregressive conditional heteroscedastic (GARCH) type methods. Building on a specific covariance estimator suggested by Engle in 2002, we propose a relatively simple extension that requires little or no tuning or fitting, is interpretable, and produces results at least as good as MGARCH, a popular extension of GARCH that handles multiple assets. To evaluate predictors we introduce a novel approach, evaluating the regret of the log-likelihood over a time period such as a quarter. This metric allows us to see not only how well a covariance predictor does over all, but also how quickly it reacts to changes in market conditions. Our simple predictor outperforms MGARCH in terms of regret. We also test covariance predictors on downstream applications such as portfolio optimization methods that depend on the covariance matrix. For these applications our simple covariance predictor and MGARCH perform similarly.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Engle, R (2002) Dynamic Conditional Correlation | 1.000 | 6 | 3 | 100% |
| 2 | Boyd, S. and Vandenberghe, L (2004) Convex optimization self | 1.000 | 5 | 4 | 100% |
| 3 | Barratt, S. and Boyd, S (2022) Covariance prediction via convex optimization self | 1.000 | 5 | 3 | 100% |
| 4 | Patton, A. and Sheppard, K (2009) Evaluating volatility and correlation forecasts | 0.811 | 4 | 2 | 100% |
| 5 | Wharton Research Data Services | 0.644 | 2 | 2 | 100% |
| 6 | Engle, R (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation | 0.644 | 2 | 2 | 100% |
| 7 | Bauwens, L. and Laurent, S. and Rombouts, J (2006) Multivariate GARCH models: a survey | 0.644 | 2 | 2 | 100% |
| 8 | Patton, A (2011) Volatility forecast comparison using imperfect volatility proxies | 0.644 | 2 | 2 | 100% |
| 9 | Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity | 0.511 | 2 | 1 | 100% |
| 10 | Boyd, S. and Vandenberghe, L (2023) Convex Optimization Additional Exercises self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | VAR models with an index structure: A survey with new results | 0.405 | 1 | 1 |