Volatility, the Macroeconomy, and Asset Prices

Abstract

How important are volatility fluctuations for asset prices and the macroeconomy? We find that an increase in macroeconomic volatility is associated with an increase in discount rates and a decline in consumption. We develop a framework in which cash flow, discount rate, and volatility risks determine risk premia and show that volatility plays a significant role in explaining the joint dynamics of returns to human capital and equity. Volatility risk carries a sizable positive risk premium and helps account for the cross section of expected returns. Our evidence demonstrates that volatility is important for understanding expected returns and macroeconomic fluctuations. ABSTRACTWe show that volatility movements have first-order implications for consumption dynamics and asset prices. Volatility news affects the stochastic discount factor and carries a separate risk premium. In the data, volatility risks are persistent and are strongly correlated with discount-rate news. This evidence has important implications for the return on aggregate wealth and the cross-sectional differences in risk premia. Estimation of our volatility risks based model yields an economically plausible positive correlation between the return to human capital and equity, while this correlation is implausibly negative when volatility risk is ignored. Our model setup implies a dynamics capital asset pricing model (DCAPM) which underscores the importance of volatility risk in addition to cash-flow and discount-rate risks. We show that our DCAPM accounts for the level and dispersion of risk premia across book-to-market and size sorted portfolios, and that equity portfolios carry positive volatility-risk premia.

Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach

“…Third, it is worth noting that the median posterior estimate for risk aversion is around 9, while it is around 2 for the intertemporal elasticity of substitution (IES). These estimates are broadly consistent with the parameter values highlighted in the LRR literature (see Bansal, Kiku, and Yaron (2012), and Bansal, Kiku, and Yaron (2014)). Fourth, at the estimated preference parameters and those characterizing the consumption and dividend dynamics, the model is able to successfully generate many key asset pricing moments, and improve model performance relative to previous LRR models along several dimensions.…”

“…Based on multiple variables, consumption growth is highly predictable in the data. At the one-year horizon, the R 2 is about 52% (see also Bansal et al (2014)). While the predictability diminishes over time, it is still nontrivial with an R 2 of 12% at the 10-year horizon.…”

“…Our paper is connected to several strands of the literature. In terms of the LRR literature, Bansal, Kiku, and Yaron (2014) utilized data that are time-aggregated to annual frequency to estimate the LRR model by GMM, and Bansal, Gallant, and Tauchen (2007) pursued an approach based on the efficient method of moments (EMM). Both papers use cash-flow and asset price data jointly for the estimation of the parameters of the cash-flow process.…”

“…The use of state-space systems to account for missing monthly observations dates back to at least Harvey (1989) and has more recently been used in the context of dynamic factor models (see, e.g., Mariano and Murasawa (2003) and Aruoba, Diebold, and Scotti (2009)) and VARs (see, e.g., Schorfheide and Song (2015)). Finally, there is a growing and voluminous literature in macro and finance that highlights the importance of volatility for understanding the macroeconomy and financial markets (see, e.g., Bansal, Khatacharian, and Yaron (2005), Bloom (2009), Fernández-Villaverde and Rubio-Ramírez (2011), Bansal, Kiku, and Yaron (2012), and Bansal, Kiku, Shaliastovich, and Yaron (2014)). Our volatility specification that accommodates three processes further contributes to identifying the different uncertainty shocks in the economy.…”

Robust Test of Long Run Risk and Valuation Risk Model

“…The bias is a function of utility parameters (risk aversion γ, inter-temporal elasticity of substitution ψ), discount rate (δ), time preference shock λt+1 λt , the aggregate wealth portfolio return (R a,t+1 ), the asset return (R i,t+1 ), and the variance of measurement error (σ 2 ). 4 This assumption is reasonable since consumption data has a trend 5 Given the highly non-linear nature of the Euler equation, I expand up to two terms. I assume that the higher order terms have a negligible impact.…”