Asset Pricing Program Meeting
April 13, 2012
Leonid Kogan, MIT and NBER; Dimitris Papanikolaou, Northwestern University; Amit Seru, University of Chicago and NBER; and Noah Stoffman, Indiana University
Kogan, Papanikolaou, Seru, and Stoffman explore the role of technological innovation as a source of economic growth by constructing direct measures of innovation at the firm level. They combine patent data for U.S. firms from 1926 to 2010 with the stock market response to news about patents in order to assess the economic importance of each innovation. Their innovation measure predicts productivity and output at the firm, industry, and aggregate level. Furthermore, capital and labor flow away from non-innovating firms towards innovating firms within an industry. There exists a similar, though weaker, pattern across industries. Cross-industry differences in technological innovation are strongly related to subsequent differences in industry output growth.
Howard Kung and Lukas Schmid, Duke University
Asset prices reflect anticipations of future growth. Kung and Schmid examine the asset pricing implications of a production economy whose long-term growth prospects are endogenously determined by innovation and R and D. In equilibrium, R and D endogenously drives a small, persistent component in productivity that generates long-run uncertainty about economic growth. With recursive preferences, households fear that persistent slowdowns in economic growth are accompanied by low asset valuations and command high risk premiums in asset markets. The authors find substantial evidence for innovation-driven low-frequency movements in aggregate growth rates and asset market valuations. In short, equilibrium growth is risky.
John Y. Campbell, Harvard University and NBER; Stefano Giglio, University of Chicago; Christopher Polk, London School of Economics; and Robert Turley, Harvard University
Campbell, Giglio, Polk, and Turley extend the approximate closed-form intertemporal capital asset pricing model of Campbell (1993) to allow for stochastic volatility. They model the return on the aggregate stock market as one element of a vector autoregressive (VAR) system, and the volatility of all shocks to the VAR as another element of the system. They then present evidence that growth stocks underperform value stocks because they hedge two types of deterioration in investment opportunities: declining expected stock returns, and increasing volatility. Volatility hedging is also relevant for pricing risk-sorted portfolios and non-equity assets, such as equity index options and corporate bonds.
Ravi Bansal, Duke University and NBER; Dana Kiku and Ivan Shaliastovich, University of Pennsylvania; and Amir Yaron, University of Pennsylvania and NBER
Bansal, Kiku, Shaliastovich, and Yaron show that volatility movements have first-order implications for consumption, the stochastic discount factor, and asset prices. Volatility shocks carry a risk-premium in this model. Accounting for volatility risks leads to a positive correlation between the return to human capital and the market return, but to a negative correlation when volatility risk is ignored. The volatility-risk- based asset pricing model can account for the levels and differences in the risk premiums across value and size portfolios in the data.
Andrea Frazzini, AQR Capital Management, and Lasse H. Pedersen, New York University and NBER
Many financial instruments are designed with embedded leverage such as options and leveraged exchange traded funds (ETFs). Embedded leverage alleviates investors' leverage constraints and, therefore, Frazzini and Pedersen hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, they find that asset classes with embedded leverage offer low risk-adjusted returns and, in the cross-section, higher embedded leverage is associated with lower returns. A portfolio which is long low-embedded-leverage securities and short high-embedded-leverage securities earns large abnormal returns, with t-statistics of 8.6 for equity options, 6.3 for index options, and 2.5 for ETFs. The authors provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics.
Ji Shen, London School of Economics, and Hongjun Yan and Jinfan Zhang, Yale University
Shen, Yan, and Zhang propose a collateral view of financial innovation: many innovations are motivated by alleviating collateral/margin constraints for trading (speculation or hedging). They analyze a model of investors with heterogeneous beliefs. The need for trading motivates investors to introduce derivatives, which are endogenously determined in equilibrium. In the presence of a collateral friction in cross-netting, the "optimal" security is one that isolates the variable with disagreement: it is optimal in the sense that alternative derivatives cannot generate any trading. With an arbitrarily small trading cost, the optimal security is "unfunded", that is, it has a zero initial value. The endogenous difference in collateral requirements leads to a basis:, the spread between the prices of an underlying asset and its replicating portfolio. This basis reflects the shadow value of collateral, leading to a number of time-series and cross-sectional implications. More broadly, this analysis highlights the common theme behind a variety of financial innovations: the inventions of securities (for example, futures, or swaps); legal practice (such as the super-seniority of repos and derivatives); legal entities (including special purpose vehicles); and to the efforts in improving the margin procedure.
Hanno Lustig, University of California at Los Angeles and NBER; Nikolai Roussanov, University of Pennsylvania and NBER; and Adrien Verdelhan, MIT and NBER
Lustig, Roussanov, and Verdelhan describe a novel currency investment strategy, the 'dollar carry trade,' that delivers large excess returns, uncorrelated with the returns on well-known carry trade strategies. Using a no-arbitrage model of exchange rates, they show that these excess returns compensate U.S. investors for taking on aggregate risk by shorting the dollar in bad times, when the price of risk is high. The counter-cyclical variation in risk premiums leads to strong return predictability: the average forward discount and U.S. industrial production growth rates forecast up to 25 percent of the dollar return variation at the one-year horizon.