[Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate—that is, the rent-price ratio in commercial real estate—captures fluctuations in expected returns for apartments and retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on offices are also time-varying. As implied by the present value relation, cap rates marginally forecast office rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data-generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land-use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market and Treasury bills, as well as commercial real estate.]

Commercial real estate expected returns and expected rent growth rates are time-varying. Relying on transactions data from a cross-section of U.S. metropolitan areas, we find that up to 30% of the variability of realized returns to commercial real estate can be accounted for by expected return variability, while expected rent growth rate variability explains up to 45% of the variability of realized rent growth rates. The cap rate—that is, the rent-price ratio in commercial real estate—captures fluctuations in expected returns for apartments and retail properties, as well as industrial properties. For offices, by contrast, cap rates do not forecast (in-sample) returns even though expected returns on offices are also time-varying. As implied by the present value relation, cap rates marginally forecast office rent growth but not rent growth of apartments, retail properties, and industrial properties. We link these differences in in-sample predictability to differences in the stochastic properties of the underlying commercial real estate data-generating processes. Also, rent growth predictability is observed mostly in locations characterized by higher population density and stringent land-use restrictions. The opposite is true for return predictability. The dynamic portfolio implications of time-varying commercial real estate returns are also explored in the context of a portfolio manager investing in the aggregate stock market and Treasury bills, as well as commercial real estate.

We propose a quantile‐based measure of conditional skewness, particularly suitable for handling recalcitrant emerging market (EM) returns. The skewness of international stock market returns varies significantly across countries over time, and persists at long horizons. In EMs, skewness is mostly positive and idiosyncratic, and significantly relates to a country's financial and trade openness and balance of payments. In an international portfolio setting, return asymmetry leads to sizeable certainty‐equivalent gains and increases the weight on emerging countries to about 30%. Investing in EMs seems to be about expectations of a higher upside than downside, consistent with recent theories.

We develop a term structure model that decomposes nominal yields into the sum of an expectation, term premium, and convexity term and in turn of their real and inflation counterparts. The model explicitly captures the interrelation between yield-only and macroeconomic factors while allowing for aggregate stochastic volatility. We extract the components from the nominal and real yield curve of the United States, the Euro Area, the United Kingdom, and Japan. We find that short-rate expectations have steadily declined over the last two decades and account for the bulk of yield dynamics. Term premia increase with maturity but explain a smaller fraction of yield forecast error variance than previously documented. With regard to yield comovement, the United States generates the strongest spillovers at the long end of the yield curve, whereas the Japanese market is the top importer of shocks.

Across a wide panel of countries, the top-10% of financial stocks on average account for over 20% of a country’s market capitalization but earn on average significantly lower returns than do nonfinancial firms of the same size and risk exposures. In a bailout-augmented, rare disasters asset pricing model, the spread in risk-adjusted returns between large and small institutions depends on country characteristics that determine the likelihood of bailouts. Consistent with this model, we find larger spreads in countries with large and interconnected financial sectors, weaker capital regulation and corporate governance, and fiscally stronger governments. Valuation gaps increase in anticipation of financial crises.

The article studies liquidity provision by institutional investors using trade-level data. We find that hedge fund trades are a more important predictor of stock-level liquidity than mutual fund trades. However, hedge funds’ liquidity provision is more exposed to financial conditions than that of mutual funds. Hedge funds that are more constrained in terms of leverage, age, asset illiquidity, and past performance exhibit a stronger shift toward liquidity consumption when funding condition tighten. Stocks with more exposure to constrained liquidity providing hedge funds suffered more during the financial crisis.