Publications
News Implied Volatility and Disasters Concerns (with Asaf Manela)
[NVIX interactive chart] [Slides] Journal of Financial Economics, forthcoming
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.
Working Papers
Volatility Managed Portfolios (with Tyler Muir)
[Local copy] [Slides]
Managed portfolios that take less risk when volatility is high produce large, positive
alphas and increase factor Sharpe ratios by substantial amounts. We document
this fact for the market, value, momentum, profitability, return on equity, and investment
factors in equities, as well as the currency carry trade. Our portfolio timing
strategies are simple to implement in real time and are contrary to conventional wisdom
because volatility tends to be high after the onset of recessions and crises when
selling is typically viewed as a mistake. Instead, our strategy earns high average returns
while taking less risk in recessions. We study the portfolio choice implications of
these results. We find volatility timing provides large utility gains to a mean variance
investor, with increases in lifetime utility ranging from 50-90%. We then study the
problem of a long horizon investor and show that, perhaps surprisingly, long horizon
investors can benefit from volatility timing even when time-variation in volatility is
completely driven by time-varying discount rate volatility.
Capital Immobility and the Reach for Yield
[Local copy]
In this paper, I build a model where financial intermediation slows the flow of capital. Investors optimally learn from intermediary performance to allocate capital toward profitable intermediaries. Intermediaries reach for yield, i.e invest in high tail risk assets, in an attempt to drive flows and reduce liquidation risk. Reaching for yield is stronger among intermediaries with weak opportunities, resulting in a reduction in the informativeness of performance; investors take longer to learn and capital flows become less responsive to performance. Capital becomes slow moving because the reach for yield dampens learning. The model predicts capital immobility to be stronger when tail risk is high; when tail risk is underpriced; and in asset classes with large cross-sectional variation in tail risk exposures.
The Macroeconomics of Shadow Banking (with Alexi Savov)
[Local copy] [Slides] [VoxEU column] , Revise and Resubmit, Journal of Finance
Managed portfolios that take less risk when volatility is high produce large, positive alphas and increase factor Sharpe ratios by substantial amounts. We document this fact for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Our portfolio timing strategies are simple to implement in real time and are contrary to conventional wisdom because volatility tends to be high after the onset of recessions and crises when selling is typically viewed as a mistake. Instead, our strategy earns high average returns while taking less risk in recessions. We study the portfolio choice implications of these results. We find volatility timing provides large utility gains to a mean variance investor, with increases in lifetime utility ranging from 50-90%. We then study the problem of a long horizon investor and show that, perhaps surprisingly, long horizon investors can benefit from volatility timing even when time-variation in volatility is completely driven by time-varying discount rate volatility.
In this paper, I build a model where financial intermediation slows the flow of capital. Investors optimally learn from intermediary performance to allocate capital toward profitable intermediaries. Intermediaries reach for yield, i.e invest in high tail risk assets, in an attempt to drive flows and reduce liquidation risk. Reaching for yield is stronger among intermediaries with weak opportunities, resulting in a reduction in the informativeness of performance; investors take longer to learn and capital flows become less responsive to performance. Capital becomes slow moving because the reach for yield dampens learning. The model predicts capital immobility to be stronger when tail risk is high; when tail risk is underpriced; and in asset classes with large cross-sectional variation in tail risk exposures.
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.
The Analyst Coverage Network (with Armando Gomes and David Sovich)
[Local copy]
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.
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. |
Limits to Arbitrage and Lockup Maturities
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.
A Reputation Based Model of Limited Arbitrage
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.