Asset Prices and Wealth Inequality (2017)
I examine recently available data on the top of the wealth distribution through the lens of asset pricing models with heterogeneous agents. A standard model where agents have heterogeneous preferences matches three key facts about the relation between asset prices and wealth inequality: (1) the wealth distribution is fat-tailed, due to the high wealth growth of households in top percentiles (2) when stock market returns are high, wealth inequality increases (3) higher wealth inequality predicts lower future excess stock returns. Quantitatively, however, a standard model that matches the wealth distribution cannot fully account for the volatility of asset prices in equilibrium: to match the high volatility of asset prices, the model would require such a large degree of preference heterogeneity that it would give rise to a wealth distribution close to Zipf’s law, i.e. with a right tail much thicker than the data.
Video of the Wealth Distribution with Regime Switches
Ups and Downs: How Idiosyncratic Volatility Drives Top Wealth Inequality (2018)
This paper examines the role of idiosyncratic returns in driving the recent rise in top wealth inequality. The growth of top wealth shares can be decomposed into two terms: (i) a within term, driven by the average wealth growth of households in top percentiles and (ii) a displacement term, driven by all higher-order moments of their wealth growth. Using panel data, I document that the displacement term accounts for more than half the rise in top wealth shares in the United States since 1983. This comes from a sharp rise in the dispersion of firm-level returns during this period. These findings have important implications for the relationship between wealth inequality and economic growth, as well as wealth mobility.
Bank Exposure to Interest-Rate Risk and the Transmission of Monetary Policy with A. Landier, D. Sraer and D. Thesmar (2016)
We show that the cash-flow exposure of banks to interest rate risk, or income gap, affects the transmission of monetary policy to bank lending and real activity. In the cross-section of banks, income gap predicts the sensitivity of cash-flows and lending to interest rates. The effect of income gap is larger or similar in magnitude to that of previously identified factors, such as leverage, bank size or asset liquidity. To alleviate endogeneity concerns, we build loan-level data to control for firm-level demand shocks. This analysis also allows us to link banks' interest risk exposure to firm investment and employment.