We show that the price of risk and quantity of risk are negatively correlated in the time-series for benchmark factors in equities and currencies. Managed portfolios that increase factor exposures when volatility is low and decrease exposure when volatility is high thus produce positive alphas and increase factor Sharpe ratios. We also .nd volatility timing to be more bene.cial to a mean variance investor than expected return timing by a fairly wide margin. These portfolio timing strategies are simple to implement in real time and are contrary to conventional wisdom because volatility tends to be high at the beginning of recessions and crises when selling is often viewed as a mistake. The facts are potentially puzzling because they imply that e.ective risk aversion would have to be low when volatility is high, and vice versa.
We study the problem of optimally aggregating potentially biased information pro- duced by sell-side analysts. In a Bayesian framework, we obtain closed-form expressions for the posterior mean and variance of excess returns and we derive the optimal port- folio policy for a utility maximizing mean-variance investor. We show that the optimal Bayesian portfolio choice is intricately linked to the analyst coverage network - the graph where the vertices are the .rms and the edges are all the pairs of distinct .rms that are covered by at least one common analyst. The connectedness of the analyst coverage network determines how wealth is reallocated among stock components such as industry groups. Moreover, changes in portfolio depend not only on the value of rel- ative stock recommendations, but also the strength of the connections between stocks in the network, and the sensitivity of returns to stock recommendations depends on the relationship between the network structure and the risk-premium. Our model also admits a novel estimator for the consensus stock recommendation measure commonly used in the empirical evaluation of analyst forecasts.
This paper develops a model of financial intermediation that highlights how reputation shapes the allocation of capital, and study the role of tail-risks in reducing the speed of capital flows. An intermediary builds reputation and attracts capital as investors learn from his investment performance. The tension between reputation and performance incentives produces a feedback loop between intermediaries and investors reach-for-yield behavior, which distorts capital allocation decisions and asset prices. Strategic behavior slows down the flow of capital overall, and into low-tail-risk assets in particular. Tail-risk taking opportunities render capital endogenously slow moving.
We build a macro-finance model in which intermediaries issue equity without friction. In normal times, they maximize liquidity creation by levering up the collateral value of their assets, a process we call shadow banking. A rise in uncertainty causes investors to demand liquidity in bad states, which forces intermediaries to delever and substitute toward safe liabilities; shadow banking shuts down, prices and investment fall. The model is consistent with a slow economic recovery especially when intermediary capital is high. It features collateral runs and flight to quality, and it provides a framework for analyzing unconventional monetary policy and regulatory reform proposals.
We extend back to 1890 the volatility implied by options index (VIX), available only since 1986, using the frequency of words on the front-page of the Wall Street Journal. News implied volatility (NVIX) captures well the disaster concerns of the average investor over this longer history. NVIX is particularly high during stock market crashes, times of policy-related uncertainty, world wars and financial crises. We find that periods when people are more concerned with a rare disaster, as proxied by news, are either followed by periods of above average stock returns, or followed by periods of large economic disasters. We estimate that the disaster probability has a half-life of four to eight months and annual volatility of 4% to 6%. Our findings are consistent with the view that hard to measure time-varying rare disaster risk is an important driver behind asset prices.
This paper studies the interaction between a fund manager who has information regarding a long-term opportunity and investors who are uncertain about their manager. Investor behavior determines the fund liquidation risk. Manager portfolio decisions interact with investor behavior through the learning channel. A loop between limits to arbitrage and liquidation arises: higher liquidation risk pushes the manager to invest less in the long-term opportunity, which leads investors to liquidate the manager earlier, which feeds back into higher liquidation risk. The introduction of a lockup reduces limits to arbitrage, but also leads to managerial entrenchment. The optimal lockup maturity balances these two forces. For a calibration that matches moments of a hedge fund database, the model produces quantitatively large limits to arbitrage distortions and lockup maturities consistent with the data.
My model shows how limits to arbitrage arises endogenously from a positive self-enforcing feed-back between fund investors' liquidation decisions and manager's portfolio choice. A higher risk of fund liquidation leads managers to favor strategies that pay out quickly. Rational investors anticipate the managers' incentives, learn more from short-term performance and liquidate funds earlier. Investor's decisions feed back into the manager's portfolio through an additional reduction in the manager horizon, further amplifying the initial distortion. Equilibrium pricing reflects this fundamental delegation friction with mispricing becoming more severe as reputational capital becomes scarce.