A long-term investor who ignores variation in volatility gives up the equivalent of 2.4% of wealth per year. This result holds for a wide range of parameters that are consistent with US stock market data, and it is robust to estimation uncertainty. We propose and test a new channel, the volatility composition channel, for how investment horizon interacts with volatility timing. Investors respond substantially less to volatility variation if the amount of mean reversion in returns disproportionally increases with volatility and also if mean reversion happens quickly. We find that these conditions are unlikely to hold in the data.

We build a macrofinance model of shadow banking—the transformation of risky assets into securities that are money‐like in quiet times but become illiquid when uncertainty spikes. Shadow banking economizes on scarce collateral, expanding liquidity provision, boosting asset prices and growth, but also building up fragility. A rise in uncertainty raises shadow banking spreads, forcing financial institutions to switch to collateral‐intensive funding. Shadow banking collapses, liquidity provision shrinks, liquidity premia and discount rates rise, asset prices and investment fall. The model generates slow recoveries, collateral runs, and flight‐to‐quality effects, and it sheds light on Large‐Scale Asset Purchases, Operation Twist, and other interventions.

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean‐variance investors. We document this for the market, value, momentum, profitability, return on equity, investment, and betting‐against‐beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk‐based explanations and is a challenge to structural models of time‐varying expected returns.

We document extreme disruption in debt markets during the COVID-19 crisis: a severe price crash accompanied by significant dislocations at the safer end of the credit spectrum. Investment-grade corporate bonds traded at a discount to credit default swaps; exchange-traded funds traded at a discount to net asset value, more so for safer bonds. The Federal Reserve’s announcement of corporate bond purchases caused these dislocations to disappear and prices to recover. These facts inform potential theories of the disruption. The best explanation is an acute liquidity need for specific bond investors, such as mutual funds, leading them to liquidate large positions.

At the announcement of a new policy, agents form a view of state-contingent policy actions and impact. We develop a method to estimate this state-contingent perception and implement it for many asset-purchase interventions worldwide. Expectations of larger support in bad states—“policy puts”—explain a large fraction of the announcements' impact. For example, when the Fed introduced purchases of corporate bonds in March 2020, markets expected five times more price support had conditions worsened relative to the median scenario. Perceived promises of additional support in bad states alter asset prices, risk, and the response to future announcements.

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 postwar 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. Over the longer 1890–2009 sample that includes the Great Depression and two world wars, high NVIX predicts high future returns in normal times and rises just before transitions into economic disasters. The evidence is consistent with recent theories emphasizing time variation in rare disaster risk as a source of aggregate asset prices fluctuations.