The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We propose new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets.
G12: Asset Pricing; Trading volume; Bond Interest Rates
We construct a slope factor from changes in federal funds futures of different horizons. Slope predicts stock returns at the weekly frequency: faster monetary policy easing positively predicts excess returns. Investors can achieve increases in weekly Sharpe ratios of 20% conditioning on the slope factor. The tone of speeches by the FOMC chair correlates with the slope factor. Slope predicts changes in future interest rates and forecast revisions of professional forecasters.
We develop an asset pricing model with rich heterogeneity in asset demand across investors, designed to match institutional holdings. The equilibrium price vector is uniquely determined by market clearing for each asset. We relate our model to traditional frameworks including Euler equations, mean-variance portfolio choice, factor models, and cross-sectional regressions on characteristics.
We construct a text-based measure of uncertainty starting in 1890 using front-page articles of the Wall Street Journal. News implied volatility (NVIX) peaks during stock market crashes, times of policy-related uncertainty, world wars and financial crises. In US post-war data, periods when NVIX is high are followed by periods of above average stock returns, even after controlling for contemporaneous and forward-looking measures of stock market volatility. News coverage related to wars and government policy explains most of the time variation in risk premia our measure identifies.
Financial innovations that change how promises are collateralized affect prices and investment, even in the absence of any change in fundamentals. In C-models, the ability to leverage an asset always generates over-investment compared to Arrow Debreu. Credit Default Swaps always leads to under-investment with respect to Arrow Debreu, and in some cases even robustly destroy competitive equilibrium. The need for collateral would seem to cause under-investment.
The optimal investment to mitigate climate change crucially depends on the discount rate used to evaluate the investment’s uncertain future beneﬁts. The appropriate discount rate is a function of the horizon over which these beneﬁts accrue and the riskiness of the investment. In this paper, we estimate the term structure of discount rates for an important risky asset class, real estate, up to the very long horizons relevant for investments in climate change abatement. We show that this term structure is steeply downward-sloping, reaching 2.6% at horizons beyond 100 years.
When a risk factor has small covariance with asset returns, risk premia in the linear asset pricing models are no longer identified. Weak factors, similar to weak instruments, make the usual estimation techniques unreliable. When included in the model, they generate spuriously high significance levels of their own risk premia estimates, overall measures of fit and may crowd out the impact of the true sources of risk.
We study structural models of stochastic discount factors and explore alternative methods of estimating such models using data on macroeconomic risk and asset returns. Particular attention is devoted to recursive utility models in which risk aversion can be modified without altering intertemporal substitution. We characterize the impact of changing the intertemporal substitution and risk aversion parameters on equilibrium short-run and long-run risk prices and on equilibrium wealth.
We characterize the compensation demanded by investors in equilibrium for incremental exposure to growth-rate risk. Given an underlying Markov diffusion that governs the state variables in the economy, the economic model implies a stochastic discount factor process S. We also consider a reference growth process G that may represent the growth in the payoff of a single asset or of the macroeconomy. Both S and G are modeled conveniently as multiplicative functionals of a multidimensional Brownian motion.
A representative agent fears that his model, a continuous time Markov process with jump and diffusion components, is misspecified and therefore uses robust control theory to make decisions. Under the decision maker's approximating model, cautious behavior puts adjustments for model misspecification into market prices for risk factors. We use a statistical theory of detection to quantify how much model misspecification the decision maker should fear, given his historical data record. A semigroup is a collection of objects connected by something like the law of iterated expectations.