NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Asset Pricing

March 24, 2017
Ralph Koijen and Itamar Drechsler, both of New York University, Organizers

Michael Sockin, University of Texas at Austin, and Markus K. Brunnermeier and Wei Xiong, Princeton University and NBER

China's Model of Managing the Financial System

China's economic model involves active government intervention in financial markets. It relaxes/tightens market regulations and even directs asset trading with the objective to maintain market stability. Sockin, Brunnermeier, and Xiong develop a theoretical framework that anchors government intervention on a mission to prevent market breakdown and the explosion of volatility caused by the reluctance of short-term investors to trade against noise traders when the risk of trading against them is sufficiently large. In the presence of realistic information frictions about unobservable asset fundamentals, their framework shows that the government can alter market dynamics by making noise in its intervention program an additional factor driving asset prices, and can divert investor attention toward acquiring information about this noise rather than fundamentals. Through this latter channel, the widely-adopted objective of government intervention to reduce asset price volatility may exacerbate, rather than improve, the information efficiency of asset prices.


Nina Boyarchenko and Matthew C. Plosser, Federal Reserve Bank of New York, and Valentin Haddad, University of California, Los Angeles and NBER

The Federal Reserve and Market Confidence

Boyarchenko, Haddad, and Plosser discover a novel monetary policy shock that has a widespread impact on aggregate financial conditions and market confidence. The shock can be summarized by the response of long-horizon yields to FOMC announcements; not only is it orthogonal to changes in the near-term path of policy rates, but it also explains more than half of the abnormal variation in the yield curve on announcement days. The researchers find that their shock is positively related to changes in real interest rates and market volatility, and negatively related to market returns and mortgage issuance, consistent with policy announcements affecting market confidence. The results demonstrate that Federal Reserve pronouncements influence markets independent of changes in the stance of conventional monetary policy.


Tano Santos, Columbia University and NBER, and Pietro Veronesi, University of Chicago and NBER

Habits and Leverage (NBER Working Paper No. 22905)

Many stylized facts of leverage, trading, and asset prices follow from a frictionless general equilibrium model that features agents' heterogeneity in endowments and habit preferences. Santos and Veronesi's model predicts that aggregate debt increases in good times when stock prices are high, return volatility is low, and levered agents enjoy a "consumption boom." Their model is consistent with poorer agents borrowing more and with recent evidence on intermediaries' leverage being a priced factor of asset returns. In crisis times, levered agents strongly deleverage by "fire selling" their risky assets as asset prices drop. Yet, consistent with the data, their debt-to-wealth ratios increase because their wealth decline faster due to higher discount rates.

Peter Diep, AQR; Andrea L. Eisfeldt, University of California, Los Angeles and NBER; and Scott Richardson, AQR Capital Management

Prepayment Risk and Expected MBS Returns (NBER Working Paper No. 21851)

Diep, Eisfeldt, and Richardson present a simple, linear asset pricing model of the cross section of Mortgage-Backed Security (MBS) returns in which MBS earn risk premia as compensation for their exposure to prepayment risk. The researchers measure prepayment risk and estimate security risk loadings using real data on prepayment forecasts vs. realizations. Estimated loadings are monotonic in securities' coupons relative to the par coupon, as predicted by the model. Prepayment risks appear to be priced by specialized MBS investors. In particular, the researchers find convincing evidence that prepayment risk prices change sign over time with the sign of a representative MBS investor's exposure to prepayment risk.


Hui Chen and Jiang Wang, MIT and NBER, and Anton Petukhov, MIT

The Dark Side of Circuit Breakers

Market-wide trading halts, also called circuit breakers, have been proposed and widely adopted as a measure to stabilize the stock market when experiencing large price movements. Chen, Petukhov, and Wang develop an intertemporal equilibrium model to examine how circuit breakers impact the market when investors trade to share risk. The researchers show that a downside circuit breaker tends to lower the stock price and increase its volatility, both conditional and realized. Due to this increase in volatility, the circuit breaker's own presence actually raises the likelihood of reaching the triggering price. In addition, the circuit breaker also increases the probability of hitting the triggering price as the stock price approaches it—the so-called "magnet effect." Surprisingly, the volatility amplification effect becomes stronger when the wealth share of the relatively pessimistic agent is small.


Serhiy Kozak and Shrihari Santosh, University of Maryland, and Stefan Nagel, University of Michigan and NBER

Shrinking the Cross-Section

Kozak, Nagel, and Santosh propose a new method of tackling the "multi-dimensionality challenge" in the cross section of equity returns. Their approach relies on exploiting economically-driven regularization to construct a robust stochastic discount factor (SDF) using individual stock returns and a vast array of characteristics. They impose penalties on estimated SDF coefficients in L2 and L1 norms, similar to the elastic nets technique in machine learning. The penalties are motivated by the need to down-weight contributions of small principal components to the total squared Sharpe ratio and sparsity of SDFs implied by most economic models, respectively. The researchers' economically-motivated estimator delivers robust, sparse SDF representations that perform well out of sample.


 
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