NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Asset Pricing

November 7-8, 2013
John Cochrane and Lubos Pastor both of University of Chicago, Organizers

Francesco Franzoni, Swiss Finance Institute, and Martin Schmalz, University of Michigan

Fund Flows in Rational Markets

Franzoni and Schmalz model the allocation of capital to mutual funds by rational risk-averse investors who are uncertain about both managers' skill and the funds' risk loadings. Uncertainty about risk loadings arises because fund portfolios are not continuously observed. Under these assumptions, investors learn more about alpha in downturns than in upturns. The reason is that in downturns the noise coming from the loading on aggregate risk is smaller, which increases the signal-to-noise ratio and thus simplifies the inference about skill. As a result, in downturns investors reallocate more wealth between funds and the flow-performance sensitivity is higher than in upturns. The authors test the model's cross-sectional and difference-in-difference predictions across fund types and market states, as well as its nonlinear predictions, and find supporting evidence.


Andrea Frazzini and Cliff Asness, AQR Capital Management, and Lasse Pedersen, Copenhagen Business School and NBER

Quality Minus Junk

Asness, Frazzini, and Pedersen define a quality security as one that has characteristics for which, all else equal, an investor would be willing to pay a higher price: stocks that are safe, profitable, growing, and well-managed. High-quality stocks do have higher prices on average, but not by a very large margin. Perhaps because of this puzzlingly modest impact of quality on price, high-quality stocks have high risk-adjusted returns. Indeed, a quality-minus-junk (QMJ) factor that goes long high-quality stocks and shorts low-quality stocks earns significant risk-adjusted returns in the United States and across 24 countries. The price of quality—that is, how much extra investors pay for higher quality stocks—varies over time, reaching a low during the internet bubble. Further, a low price of quality predicts a high future return of QMJ. Finally, controlling for quality resurrects the otherwise moribund size effect.


Hui Chen and Leonid Kogan, MIT and NBER, and Wei Dou, MIT

Measuring the "Dark Matter" in Asset Pricing Models

Models of rational expectations endow agents with precise knowledge of the probability laws inside the models. This assumption becomes more tenuous when a model's performance is highly sensitive to the parameters that are difficult to estimate directly, such as when a model relies on "dark matter." Chen, Dou, and Kogan present new measures of model fragility by quantifying the informational burden that a rational expectations model places on the agents. By measuring the informativeness of the cross-equation restrictions implied by a model, the authors' measures can systematically detect the direction in the parameter space in which the model's performance is the most fragile. Their methodology provides new ways to conduct sensitivity analysis on quantitative models. It helps identify situations where parameter or model uncertainty cannot be ignored. It also helps with evaluating competing classes of models that try to explain the same set of empirical phenomena from the perspective of the robustness of their implications.

Torben Andersen, Northwestern University and NBER, and Nicola Fusari and Viktor Todorov, Northwestern University

The Risk Premia Embedded in Option Panels

Andersen, Fusari, and Todorov study the dynamic relation between aggregate stock market risks and risk premia via an exploration of the time series of equity-index option surfaces. The analysis is based on estimating a general parametric asset pricing model for the risk-neutral equity market dynamics using a panel of options on the S&P 500 index, while remaining fully nonparametric about the actual evolution of market risks. The authors find that the risk-neutral jump intensity, which controls the pricing of left tail risk, cannot be spanned by the market volatility (and its components), so an additional factor is required to account for its dynamics. This tail factor has no incremental predictive power for future equity return volatility or jumps beyond what is captured by the current and past level of volatility. In contrast, the novel factor is critical in predicting the future market excess returns over horizons up to one year, and it explains a large fraction of the future variance risk premium. The authors contrast their findings with those implied by structural asset pricing models that seek to rationalize the predictive power of option data. Relative to those studies, their findings suggest a wider wedge between the dynamics of equity market risks and the corresponding risk premia with the latter typically displaying a far more persistent reaction following market crises.


Robert Stambaugh, University of Pennsylvania and NBER; Jianfeng Yu, University of Minnesota; and Yu Yuan, SAIF

Arbitrage Asymmetry and the Idiosyncratic Volatility Puzzle (NBER Working Paper 18560)

Many investors purchase stock but are reluctant or unable to sell short. Stambaugh, Yu, and Yuan find that combining this arbitrage asymmetry with the arbitrage risk represented by idiosyncratic volatility (IVOL) explains the negative relation between IVOL and average return. The effect of IVOL on return is negative among overpriced stocks but positive among underpriced stocks, with mispricing determined by combining 11 return anomalies. The negative effect is stronger, consistent with arbitrage asymmetry, and therefore aggregating across all stocks yields a negative relation. Further supporting the authors' explanation is a negative relation over time between the IVOL effect and investor sentiment, especially among overpriced stocks.


Adrien Verdelhan, MIT and NBER

The Share of Systematic Variation in Bilateral Exchange Rates

Two factors account for 20 to 90 percent of the daily, monthly, quarterly, and annual exchange rate movements. These two factors—carry and dollar—are risk factors: the former accounts for the cross-section of interest rate-sorted currency returns, while the latter accounts for a novel cross-section of dollar beta-sorted currency returns. Verdelhan explains that they point to large shares of global shocks in the dynamics of exchange rates, as well as large differences across countries. The different shares of systematic currency risk are related to the comovement of international capital flows. The author's results offer new challenges for international finance models.


 
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