Published articles in peer-reviewed journals:
10. Improved Tracking-Error Management for Active and Passive Investing
The Journal of Portfolio Management, Forthcoming (with Olivier Ledoit and Michael Wolf)
9. Factor-Mimicking Portfolios for Climate Risk
Financial Analysts Journal, 2024 (with Robert F. Engle and Bryan Kelly)
Swiss Risk Award 2021 and FAN Awards 2025 nominee; Top 10 of 2024 (FAJ papers)
8. Non-Standard Errors
Journal of Finance, 2024 (with Albert Menkveld etc.)
7. Improved Inference in Financial Factor Models
International Review of Economics and Finance, 2023 (with Elliot Beck and Michael Wolf)
6. Using, Taming or Avoiding the Factor Zoo? A Double-Shrinkage Estimator for Covariance Matrices
Journal of Empirical Finance, 2023 (with Zhao Zhao)
5. Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data
Journal of Banking and Finance, 2022 (with Robert F. Engle, Olivier Ledoit and Michael Wolf)
Swiss Risk Award 2020 nominee and invited to present at SoFiE seminar series
4. Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage
Journal of Financial Econometrics, 2022
3. Subsampled Factor Models for Asset Pricing: The Rise of Vasa
Journal of Forecasting, 2022 (with Simon Hediger and Markus Leippold)
2. A Large-Dimensional Test for Cross-Sectional Anomalies: Efficient Sorting Revisited
International Review of Economics and Finance, 2022 (with Zhao Zhao)
1. Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly
Journal of Financial Econometrics, 2021 (with Olivier Ledoit and Michael Wolf)
Improved Tracking-Error Management for Active and Passive Investing Tracking-error management is largely absent from the academic literature but ubiquitous in real life: Most portfolio managers are tied to a benchmark. Some of them aim to track a benchmark (such as the S&P 500), which is not necessarily a trivial task because the benchmark often contains assets that are difficult or expensive to trade. In this case, the objective is to minimize tracking error. Other managers aim to take on an active tilt without deviating too much from a benchmark. In this case, the objective is to control tracking error. In both cases, managers need an estimator of the covariance matrix of many (excess) returns for their objective. This article demonstrates the benefit of sophisticated shrinkage estimators (in conjunction with multivariate GARCH models) to this end, relative to the commonly used sample covariance matrix. | |
Factor-Mimicking Portfolios for Climate Risk We propose and implement a procedure to optimally hedge climate change risk. First, we construct climate risk indices through textual analysis of newspapers. Second, we present a new approach to compute factor-mimicking portfolios to build climate risk hedge portfolios. The new mimicking portfolio approach is much more efficient than traditional sorting or maximum correlation approaches by taking into account new methodologies of estimating large-dimensional covariance matrices in short samples. In an extensive empirical out-of-sample performance test, we demonstrate the superior all-around performance delivering markedly higher and statistically significant alphas and betas with the climate risk indices. | |
Non-Standard Errors In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants. | |
Improved Inference in Financial Factor Models Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama–French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil. | |
Using, Taming or Avoiding the Factor Zoo? A Double-Shrinkage Estimator for Covariance Matrices Existing factor models struggle to model the covariance matrix for a large number of stocks and factors. Therefore, we introduce a new covariance matrix estimator that first shrinks the factor model coefficients and then applies nonlinear shrinkage to the residuals and factors. The estimator blends a regularized factor structure with conditional heteroskedasticity of residuals and factors and displays superior all-around performance against various competitors. We show that for the proposed double-shrinkage estimator, it is enough to use only the market factor or the most important latent factor(s). Thus there is no need for laboriously taking into account the factor zoo. | |
Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return. | |
Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage Existing shrinkage techniques struggle to model the covariance matrix of asset returns in the presence of multiple-asset classes. Therefore, we introduce a Blockbuster shrinkage estimator that clusters the covariance matrix accordingly. Besides the definition and derivation of a new asymptotically optimal linear shrinkage estimator, we propose an adaptive Blockbuster algorithm that clusters the covariance matrix even if the (number of) asset classes are unknown and change over time. It displays superior all-around performance on historical data against a variety of state-of-the-art linear shrinkage competitors. Additionally, we find that for small- and medium-sized investment universes the proposed estimator outperforms even recent nonlinear shrinkage techniques. Hence, this new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of asset returns. Furthermore, due to the general structure of the proposed Blockbuster shrinkage estimator, the application is not restricted to financial problems. | |
Subsampled Factor Models for Asset Pricing: The Rise of Vasa We propose a new method, variable subsample aggregation (VASA), for equity return prediction using a large-dimensional set of factors. To demonstrate the effectiveness, robustness, and dimension reduction power of VASA, we perform a comparative analysis between state-of-the-art machine learning algorithms. As a performance measure, we explore not only the global predictive but also the stock-specific r-squared's and their distribution. While the global r-squared reflects the average forecasting accuracy, we find that high variability in stock-specific r-squared's can be detrimental for the portfolio performance. Since VASA shows minimal variability, portfolios formed on this method outperform the portfolios based on random forests and neural nets. | |
A Large-Dimensional Test for Cross-Sectional Anomalies: Efficient Sorting Revisited Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long–short portfolios based on their firm characteristics, with microcap biases alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for (mimicking) the portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. We propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, | |
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns. |