Research
Working Papers
Factor Pricing Across Asset Classes (with T.D. Dang and M. Prokopczuk)
We study factor pricing across seven major asset classes, including U.S. and international equities, corporate bonds, commodities, currencies, equity indices, and government bonds. The pricing power of pure models from one asset class for others is typically limited. Therefore, we reject perfect market integration. Next, we use a factor selection method to create an optimal integrated factor model across asset classes. The optimal model includes multiple equity and corporate bond factors, but prices test assets from many asset classes without requiring factors from each. The results indicate that there are multiple systematic return drivers and important cross-market linkages.
The Index Effect: Evidence from the Option Market (with C. Wese Simen)
Award for Best Paper on Derivative Markets, 30th Finance Forum, Malaga
Presented at (among others): 2023 Financial Management Association (FMA) European Conference, Aalborg; 2023 Finance Forum, Malaga; 2023 Conference of the Asia-Pacific Association of Derivatives, Busan; 2023 Financial Econometrics Conference, Lancaster; 2021 German Finance Association (DGF), Innsbruck; 2021 North American Summer Meeting of the Econometric Society, Montreal (Online Conference); 2021 Financial Management Association (FMA), Denver; 2021 Africa Meeting of the Econometric Society, Abidjan (Online Conference); 2021 French Finance Association (AFFI), Online Conference; 2021 China Meeting of the Econometric Society, Online Conference; 2021 Derivative Market Conference, Online Conference; Finance Research Seminar, University of Sussex; Finance Research Seminar, University of Bremen
We document a novel index effect in options markets: delta-hedged call options yield a statistically significant placebo- and risk-adjusted return of 0.90% over the one-day announcement window. The effect is stronger for options offering more leverage, reverses over time, and is also present in delta-hedged put options. The option trading volume peaks immediately after the announcement, while the stock trading volume peaks later on the effective date. The results are consistent with demand pressure effects ahead of the predictable stock rebalancing trades of benchmarked institutional investors. The demand pressure comes primarily from firms, especially proprietary traders.
Published Papers
27. Estimating Stock Market Betas via Machine Learning (with W. Drobetz, T. Otto, and M. Prokopczuk)
Journal of Financial and Quantitative Analysis, forthcoming
Machine learning-based market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we document that machine learning-based estimators produce the lowest forecast and hedging errors. They also help create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random foests perform best overall. Model complexity is highly time-varying. Historical stock market betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves predictive performance.
26. Predicting the Equity Premium around the Globe: Comprehensive Evidence from a Large Sample (with M. Prokopczuk, B. Tharann, and C. Wese Simen)
International Journal of Forecasting, forthcoming
Examining 81 countries over a period of up to 145 years, using various predictor variables and forecasting specifications, we provide a detailed analysis of equity premium predictability. We find that excess returns are more predictable in emerging and frontier markets than in developed markets. For all groups, forecast combinations perform very well out-of-sample. Analyzing the cross-section of countries, we find that market inefficiency is an important driver of return predictability. We also document significant cross-market return predictability. Finally, domestic inflation-adjusted returns are significantly more predictable than USD returns.
25. Measuring Tail Risk (with M. Dierkes, M. Prokopczuk, and C. Würsig)
Journal of Econometrics 2024, Vol. 241(2), 105769.
We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate their statistical as well as their economic validity. The option-implied measure of Bollerslev and Todorov (2011b) (BT11Q) performs best overall. While some other tail risk measures excel at specialized tasks, BT11Q performs well in all tests: First, BT11Q can predict both future tail events and future tail volatility. Second, it has predictive power for returns in both the time series and the cross-section, as well as for real economic activity. Finally, a simulation analysis shows that the main driver of performance is measurement error.
24. Market Power and Systematic Risk (with M. Prokopczuk and C. Würsig)
Financial Management 2024, Vol. 53(2), pp. 233-266, Lead Article
We examine the impact of product market competition on firms' systematic risk. Using a measure of total product market similarity, we document a strong negative relationship between market power and market betas. The effect more than triples in the most recent period of low competition. Anti-competitive mergers result in a significant reduction in market betas. Firms facing less competition seem to be partially insulated from systematic discount rate shocks. Lower equity costs therefore imply that market power is partly self-perpetuating.
23. Which Factors for Corporate Bond Returns? (with T.D. Dang and M. Prokopczuk)
Review of Asset Pricing Studies 2023, Vol. 13(4), pp. 615-652, Editor's Choice Article
Factors related to carry, duration, equity momentum, and the term structure are the most important risk factors in corporate bond markets. From a large set of factor candidates, we condense an optimal model with a two-step approach. First, we filter out factors that do not systematically move bond prices. Second, we use a Bayesian model selection approach to determine the optimal, parsimonious model. Many prominent factors do not move prices, or are redundant. We document the new model's good performance compared to that of existing models in time-series and cross-sectional tests and analyze the economic drivers of the factors.
22. How Robust are Empirical Factor Models to the Choice of Breakpoints? (with M. Prokopczuk and V. Voigts)
Quarterly Journal of Finance 2023, Vol. 13(4), 2350011
We comprehensively investigate the robustness of well-known factor models to altered factor formation breakpoints. Deviating from the standard 30th and 70th percentile selection, we use an extensive set of anomaly test portfolios to uncover two main findings: First, there is a trade-off between specification and diversification. More centered breakpoints tend to result in less (idiosyncratic) risk. More extreme sorts lead to greater exposure to the underlying anomalies and thus to higher average returns. Second, the models are robust to varying degrees. The Hou, Xue, and Zhang (2015) model is much more sensitive to changes in breakpoints than the Fama–French models.
21. Probability Distortions, Collectivism, and International Stock Prices (with V. Sejdiu)
Journal of Behavioral and Experimental Finance 2023, Vol. 39, 100836
There are substantial differences in the return premia due to probability distortions in individualist and collectivist cultures. Consistent with the substantially lesser degree of probabilistic thinking in collectivist cultures documented by the psychology literature, probability-distortion-related return premia are substantially higher there than in individualist cultures. Our methodology applies a novel composite probability distortion (CPD) score based on cumulative prospect theory and salience theory. This measure is priced among all size groups in the cross-section of international stock returns: low-CPD-score stocks are underpriced while those with high scores appear overpriced. Collectivism is the main driver of differences in the CPD premium across countries and U.S. states.
20. Managing the Market Portfolio (with M. Prokopczuk)
Management Science 2023, Vol. 69(6), pp. 3675-3696
We analyze the relation between time-series predictability and factor investing. We use a large set of financial, macroeconomic, and technical variables to time-series-manage the market portfolio. A combination of the out-of-sample market excess return forecasts of all variables yields a managed market portfolio that generates alphas relative to cross-sectional factor models that exceed 5% per annum. More broadly, the relation between time-series evaluation measures and (multifactor) alphas is weakly positive, but complex. The variables' predictability for future returns is more important than that for volatility. Finally, we document that managed market portfolios based on lagged factor realizations also perform well.
19. Testing Factor Models in the Cross-Section (with M. Prokopczuk)
Journal of Banking and Finance 2022, Vol. 145, 106626
The standard full-sample time-series asset pricing test suffers from poor statistical properties, look-ahead bias, constant-beta assumptions, and rejects models when average factor returns deviate from risk premia. We therefore confront prominent equity pricing models with the classical Fama and MacBeth (1973) cross-sectional test. For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) cross-sectional factor risk premium estimates are generally far below the average factor excess returns; and (iii) they are usually not statistically significant. Overall, all new factor models are inconsistent with no-arbitrage pricing and cannot accurately explain the cross-section of stock returns.
18. The World of Anomalies: Smaller Than We Think?
Journal of International Money and Finance 2022, Vol. 129, 102741
I examine a large set of 124 cross-sectional anomalies in international equity markets. Many of the significant U.S. anomalies replicate in equal-weighted portfolios. However, international equal-weighted portfolios are dominated by microcaps with very limited investment capacity. Only few anomalies survive when mitigating the impact of tiny stocks, accounting for multiple testing, and using factor models to adjust for expected returns. Accounting for the former two, only 15 anomalies yield significant long-short returns in the ex-U.S. world cross-section. Across regions, value anomalies are strongest. In all international markets, except for Asia Pacific, the best U.S. factor models help to further shrink the cross-sections significantly.
17. How do Bond Investors Measure Performance? Evidence from Mutual Fund Flows (with T.D. Dang and M. Prokopczuk)
Journal of Banking and Finance 2022, Vol. 142, 106553
Which factor model do investors in corporate bonds use? We examine this question by tracking investors’ decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. Our main result is that all bond factor models are dominated by the simple Sharpe ratio. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio can be easily manipulated in bond markets, our findings have potentially severe implications for fund mangers as well as active traders and buy-and-hold corporate bond mutual fund investors.
16. Local, Regional, or Global Asset Pricing?
Journal of Financial and Quantitative Analysis 2022, Vol. 57(1), pp. 291-320
Analyzing several Developed and Emerging international markets, I test the ability of global, regional, and local models to explain a large set of 134 cross-sectional anomalies. My main finding is that both global and regional factor models create substantially larger average absolute alphas than local factor models. Annual (absolute) anomaly portfolio alphas are on average 1.7 and 1.1 percentage points higher, respectively, with global and regional than with local factor models. Even for the most recent period, there is no evidence of a catch-up of global and regional factor models. There is substantial potential for international diversification of anomaly strategies.
15. Anomalies in Commodity Futures Markets (with M. Prokopczuk and B. Tharann)
Quarterly Journal of Finance 2021, Vol. 11(4), 2150017
Published Paper, SSRN Version, Absolut Alternative Article
In recent years, commodity markets have become increasingly popular among financial investors. While previous studies document a factor structure, not much is known about how prominent anomalies are priced in commodity futures markets. We examine a large set of such anomaly variables. We identify sizable premia for jump risk, momentum, skewness, and volatility-of-volatility. Other prominent variables, such as downside beta, idiosyncratic volatility, and MAX, are not priced in commodity futures markets. Commodity investors should rebalance their portfolios regularly. Returns for annual holding periods are substantially weaker than for monthly rebalancing.
14. Predictability in Commodity Markets: Evidence from More Than a Century (with M. Prokopczuk, B. Tharann, and C. Wese Simen)
Journal of Commodity Markets 2021, Vol. 24, 100171
Using more than 140 years of data, we comprehensively analyze the predictive power of a broad set of business cycle variables for risk and return in commodity spot markets. We find that industrial production growth and inflation are the strongest predictors for future commodity returns. Several further variables help predict future commodity volatilities. The introduction of derivatives generally reduces the predictability in the most active commodity markets but increases the predictability in others. Thus, derivatives likely make markets more efficient, but also attract most of the price discovery activity. Commodity spot volatilities generally rise after futures introduction.
13. The Memory of Beta (with J. Becker, M. Prokopczuk and P. Sibbertsen)
Journal of Banking and Finance 2021, Vol. 124, 106026
Researchers and practitioners employ a variety of time-series processes to forecast betas, either using short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: betas show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long-memory model reliably provides superior beta forecasts compared to all alternatives. Accounting for long memory in beta also pays off economically for portfolio formation. We widely document the robustness of these results.
12. The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas (with M. Prokopczuk and C. Wese Simen)
Management Science 2020, Vol. 66(6), pp. 2474-2494
When using high-frequency data, the conditional CAPM can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as 3 out of 6 of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions.
11. Estimating Beta: The International Evidence
Journal of Banking and Finance 2020, Vol. 121, 105968
This paper examines the estimation of global and local betas for a large set of Developed and Emerging international markets. Estimators based on daily data clearly outperform those based on monthly or quarterly data. For global and local market betas, the optimal window length is at roughly 24 and 12 months, respectively, for most Developed Markets. It tends to be somewhat longer for Emerging Markets. The best estimators include a double-shrinkage, a long memory (FI), and a simple combination approach. For hedging the market risk exposure in anomaly portfolios, the FI and combination estimators also perform overall best.
10. Variance Risk: A Bird's Eye View (with C. Wese Simen)
Journal of Econometrics 2020, Vol. 215(2), pp. 518-535
The literature documents a significantly negative average variance swap payoff (VSP) for the S&P 500 index but generally not for the constituent stocks. We show that this result is affected by biases arising from (i) an intraday momentum effect and (ii) the use of an incoherent measure of return variation. Accounting for these issues, we find stronger evidence of a significant average VSP both at the index level and also for equities. We decompose the index variance risk premium (VRP) into factors related to the VRP of equities and the correlation risk premium (CRP) and assess their predictive power for aggregate stock returns.
9. Beta Uncertainty (with M. Prokopczuk and C. Wese Simen)
Journal of Banking and Finance 2020, Vol. 116, 105834
A stock's exposure to systematic risk factors is surrounded by substantial uncertainty. This beta uncertainty is both economically and statistically significantly priced in the cross-section of stock returns. Stocks with high beta uncertainty substantially underperform those with low beta uncertainty: a two-standard-deviation increase in the measure decreases average annual returns by 9.7%. These results cannot be explained by previously discovered determinants of cross-sectional stock returns. Aggregate beta uncertainty negatively predicts market excess returns in the short and medium term. We find supporting evidence for a mispricing explanation of the beta uncertainty premium.
8. Volatility Term Structures in Commodity Markets (with M. Prokopczuk and C. Würsig)
Journal of Futures Markets 2020, Vol. 40(4), pp. 527-555
In this study, we comprehensively examine the volatility term structures in commodity markets. We model state-dependent spillovers in principal components (PCs) of the volatility term structures of different commodities, as well as that of the equity market. We detect strong economic links and a substantial interconnectedness of the volatility term structures of commodities. Accounting for intra-commodity-market spillovers significantly improves out-of-sample forecasts of the components of the volatility term structure. Spillovers following macroeconomic news announcements account for a large proportion of this forecast power. There thus seems to be substantial information transmission between different commodity markets.
7. Asset Prices and "the Devil(s) You Know" (with D.B.B. Nguyen and M. Prokopczuk)
Journal of Banking and Finance 2019, Vol. 105, pp. 20-35
In this paper, we study the asset pricing implications of persistence in the risk-neutral return distribution's central moments. We detect a both economically and statistically significant premium of stocks with low over stocks with high such persistence. Annual value-weighted excess (risk-adjusted) returns are 4.38% (3.06%). These results cannot be explained by factors and characteristics documented in the previous literature. Furthermore, it is not the persistence of only one of the individual distributional moments but rather the joint persistence in all central moments of the risk-neutral distribution that is priced.
6. Estimating Beta: Forecast Adjustments and the Impact of Stock Characteristics for a Broad Cross-Section (with M. Prokopczuk and C. Wese Simen)
Journal of Financial Markets 2019, Vol. 44, pp. 91-118
Published Paper, SSRN Version, Press Coverage
Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as simple shrinkage adjustments yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors and fail to create market-neutral anomaly portfolios. Finally, we document a robust link between stock characteristics and beta predictability.
5. International Tail Risk and World Fear (with D.B.B. Nguyen, M. Prokopczuk, and C. Wese Simen)
Journal of International Money and Finance 2019, Vol. 93, pp. 244-259
We examine the pricing of tail risk in international stock markets. Studying all MSCI Developed and Emerging Markets countries, we find that the tail risk of these countries is highly integrated. We find that both local and our newly computed global tail risk strongly predict global equity index excess returns. These results hold both in-sample and out-of-sample. Sorting countries into portfolios by their tail risk generates sizable excess returns across various holding periods. Finally, we find that global tail risk is linked to international economic activity.
4. The Term Structure of Systematic and Idiosyncratic Risk (with M. Prokopczuk and C. Wese Simen)
Journal of Futures Markets 2019, Vol. 39(4), pp. 435-460
We study the term structure of variance (total risk), systematic, and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little indication of a time-varying term premium. Turning the focus to individual stocks, we cannot reject the expectations hypothesis for systematic variance, but we strongly reject it for idiosyncratic variance. Our results are robust to jumps and potential statistical biases.
3. Predicting the Equity Market mit Option-Implied Variables (with M. Prokopczuk, B. Tharann, and C. Wese Simen)
European Journal of Finance 2019, Vol. 25(10), pp. 937-965
We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.
2. How Aggregate Volatility-of-Volatility Affects Stock Returns (with M. Prokopczuk)
Review of Asset Pricing Studies 2018, Vol. 8(2), pp. 253-292
A stylized theoretical model with stochastic volatility suggests the existence of a tradeoff between returns and volatility-of-volatility. Using the VVIX, a measure of the option-implied volatility of the volatility index, we confirm this prediction and detect that time-varying aggregate volatility-of-volatility commands an economically substantial and statistically significant negative risk premium. We find that a two-standard deviation increase in aggregate volatility-of-volatility factor loadings is associated with a decrease in average annual returns of about 11%. These results are robust to controlling for aggregate volatility, jump risk, and several other characteristics and factor sensitivities, as well as to various further tests.
1. Estimating Beta (with M. Prokopczuk)
Journal of Financial and Quantitative Analysis 2016, Vol 51(4), pp. 1437-1466
We conduct a comprehensive comparison of market beta estimation techniques. We study the performance of several historical, time-series model, and option implied estimators for estimating realized market beta. Thereby, we find the hybrid methodology of Buss and Vilkov (2012) to consistently outperform all other approaches. In addition, all other approaches, including fully implied and GARCH-based methods for dynamic conditional beta, are dominated by a simple beta estimate based on historical (co-) variances and a Kalman filter based approach. Our conclusions remain unchanged after performing several robustness checks.