Knowledge that Transforms

We estimate total returns to rental housing by studying over 170,000 hand-collected archival observations of prices and rents for individual houses in Paris (1809–1943) and Amsterdam (1900–1979). The annualized real total return, net of costs and taxes, is 4.0% for Paris and 4.8% for Amsterdam and entirely comes from rental yields. Our returns weakly correlate with the implied returns in Jordà et al. (2019) and are substantially lower. We decompose total return risk at the individual asset level and find that yield risk becomes an increasingly important component of property-level risk for longer investment horizons.

We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.

We analyze government interventions in the eurozone banking sector during the 2008–2009 financial crisis. Using a novel data set, we document that fiscally constrained governments “kicked the can down the road” by providing banks with guarantees instead of fully-fledged recapitalizations. We econometrically address the endogeneity associated with bailout decisions in identifying their consequences. We find that forbearance prompted undercapitalized banks to shift their assets from loans to risky sovereign debt and engage in zombie lending, resulting in weaker credit supply, elevated risk in the banking sector, and, eventually, a greater reliance on liquidity support from the European Central Bank.

We construct a new data set tracking the daily value of life insurers’ assets at the security level. Outside of the 2008–2009 crisis, a $ 1 drop in the market value of assets reduces an insurer’s market equity by $ 0.10. During the ?nancial crisis, this pass-through rises to $ 1. We explain this pattern by viewing insurance companies as asset insulators, institutions with stable, long-term liabilities that can ride out transitory dislocations in market prices. Illustrating the macroeconomic importance of insulation, insurers’ market equity declined by $50 billion less than the duration-adjusted value of their securities during the crisis.

Since the mid-1990s, negative stock returns comove with downgrades to the Fed’s growth expectations and predict policy accommodations. Textual analysis of FOMC documents reveals that policy makers pay attention to the stock market. The primary mechanism is their concern with the consumption wealth effect, with a secondary role for the market predicting the economy. We find little evidence of the Fed overreacting to the market in an ex post sense (reacting beyond the market’s effect on growth expectations). Although policy makers are aware that the Fed put could induce risk-taking, moral hazard considerations appear not to significantly affect their decision-making ex ante.

We theoretically investigate the interaction of endogenous risk intolerance and monetary policy following a large recessionary shock. As asset prices dip, risk-tolerant agents’ wealth share declines. This decline reduces the market’s risk tolerance and triggers a downward loop in asset prices and aggregate demand when the interest rate policy is constrained. In this context, large-scale asset purchases are effective because they transfer unwanted risk to the government’s balance sheet. These effects are sizable when the model is calibrated to match the estimates of aggregate asset demand inelasticity. The COVID-19 shock illustrates the environment we seek to capture.

We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users’ transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an -curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users’ transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility.

This paper proposes “implied stochastic volatility models” designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.

We analyze the out-of-sample performance of variables shown to forecast future mutual fund alphas. The degree of predictability, as measured by alpha spreads from quintile sorts or cross-sectional regression slopes, falls by at least half post-sample. These declines appear to be primarily the result of changes in the level of arbitrage activity in the market, with mutual fund competition appearing to play a secondary role. We find no evidence that the declines are the result of data snooping or learning by investors or fund managers. Finally, we show that corporate bond fund performance exhibits similar dependence on measures of bond market arbitrage activity.

This paper studies the idiosyncratic risk component of individual house capital gains using data on resales and intermediate capital investments. The idiosyncratic component is large; its dynamics do not follow a random walk; and its magnitude is associated with proxies of information quality and market liquidity at the level of individual properties. Accounting for idiosyncratic risk substantially changes the assessment of the risk-return trade-off for housing: it reduces Sharpe ratios and makes them holding period dependent. I use a simple quantitative portfolio model to show that homeowners may be willing to make significant payments to insure against idiosyncratic housing risk.