Abstract This paper examines the effects of different types of private debt on firm cash balances, equity risk, and investment. Firms with more bank loans have more cash and investment, but lower equity risk. Firms with more nonbank private debt have more cash, lower equity risk, and less investment. Firms with more unused credit lines have less cash and lower equity risk, but greater investment. Results suggest that financial intermediaries' monitoring intensity increases with loan size. Depending on type, private debt mitigates information asymmetry or asset substitution, or both. Deposit relations associated with bank borrowing also contribute to banks' information advantage.

Abstract We propose a novel trend factor for the Chinese stock market that incorporates both price and volume information to capture dominant individual trading, momentum, and liquidity. We find that volume plays a more significant role in the trend factor for China than for the United States, reflecting the greater retail participation in China. By incorporating this trend factor into the 3-factor model of Liu et al. (2019), we propose a 4-factor model that explains a wide range of stylized facts and 60 representative anomalies. Our study highlights the important role of individual trading in asset pricing, especially in China. (JEL G12, G14, G15)

Measures of US government policy approval are strongly related to persistent fluctuations in the dollar value. Contemporaneous correlations between approval ratings and the dollar approach 50% against advanced economy currencies. High approval ratings further forecast a decline in the dollar risk premium several years ahead and are associated with a persistent increase in economic growth and a reduction in economic volatility. We provide an illustrative model to interpret our empirical evidence. In the model, policy valuations (approvals) are forward-looking and increase at times of high expected policy-related growth and low policy-related uncertainty, which are times of a strong dollar and low dollar risk premium.

We propose and estimate a quantitative model of exchange rates in which participants in the foreign exchange market are intermediaries subject to value-at-risk (VaR) constraints. Higher volatility translates into tighter VaR constraints, and intermediaries require higher returns to hold foreign assets. Therefore, the foreign currency is expected to appreciate. The model quantitatively resolves the Backus–Smith puzzle, the forward premium puzzle, and the exchange rate volatility puzzle and explains deviations from the covered interest rate parity. Moreover, the model implies both contemporaneous and predictive relations between proxies of leverage constraint tightness and exchange rates. These implications are supported in the data.

Abstract Do real assets protect against inflation? Stocks’ core inflation betas are negative, while their energy betas are positive. Currencies, commodities, and real estate mostly hedge against energy inflation, but not core inflation. These hedging properties are reflected in the prices of inflation risks: only core inflation carries a negative risk premium, and its magnitude is consistent within and across asset classes, uniquely among macroeconomic risk factors. Energy inflation has become more procyclical and volatile since the 1990s, which helps explain the time-varying correlation between stock and bond returns. A two-sector New Keynesian asset pricing model accounts for these facts quantitatively.

Abstract We develop a novel measure of volatility pass-through to assess international propagation of output volatility shocks to macroeconomic aggregates, equity prices, and currencies. An increase in country’s output volatility is associated with a decrease in its output, consumption, and net exports. The average consumption pass-through is 50% (a 1% increase in output volatility increases consumption volatility by 0.5%) and it increases to 70% for shocks originating in smaller countries. The equity volatility pass-through is larger and in the order of 90%. A novel channel of risk sharing of volatility risks can explain our empirical findings.