Volatility-of-Volatility Risk in Asset Pricing

Abstract

This paper develops a general equilibrium model and provides empirical support that the market volatility-of-volatility (VOV) predicts market returns and drives the time-varying volatility risk. In asset pricing tests with the market, volatility, and VOV as factors, the risk premium on VOV is statistically and economically significant and robust. Market and volatility risks are not priced in unconditional models, but, consistent with theory, their factor loadings, conditional on VOV, are priced. The pricing impact of VOV strengthens during market crashes, suggesting that VOV is particularly relevant during market turmoil, when investors demand increased compensation for VOV risk.

Unknown Unknowns: Knightian Uncertainty and Corporate Opportunistic Earnings Management

“…Different from the traditional risk framework with known probability distributions (‘risk’), we focus on the analysis of corporate decision‐making under the unknown unknowns. Meanwhile, different from the existing studies focusing on the impact of Knightian uncertainty on asset pricing, forecasting and financial risk management (Chen, Chung and Lin, 2014; Corsi et al. , 2008; Drimus and Farkas, 2013), as well as relatively few studies concerning Knightian uncertainty on corporate R&D investment decisions (Amoroso, Moncada‐Paternò‐Castello and Vezzani, 2017) and cash holdings (Goodell, Goyal and Urquhart, 2021), we mainly explore corporate opportunistic earnings management under Knightian uncertainty.…”

“…Different from the traditional risk framework with known probability distributions ('risk'), we focus on the analysis of corporate decision-making under the unknown unknowns. Meanwhile, different from the existing studies focusing on the impact of Knightian uncertainty on asset pricing, forecasting and financial risk management (Chen, Chung and Lin, 2014;Corsi et al, 2008;Drimus and Farkas, 2013), as well as relatively few studies concerning Knightian uncertainty on corporate R&D investment decisions (Amoroso, Moncada-Paternò-Castello and We used the attention analysed, the Herfindahl index and the proportion of the top five suppliers' purchase amounts in the total amount, respectively, to measure the external supervision, the industry competition and the supplier concentration. We group the entire sample according to the sample median of these three variables.…”

Volatility of Volatility and Leverage Effect from Options

“…Volatility of volatility risk is a major source of risk for volatility derivatives traders. In addition, there is growing literature in finance that documents the emergence of volatility of volatility as a separate risk factor and its weak correlation with volatility risk, see e.g., Agarwal et al (2017), Baltussen et al (2018), Hollstein and Prokopczuk (2018), Huang et al (2019) and Chen et al (2022) among others. In this paper we propose nonparametric estimators of volatility of volatility and covariance between price and volatility (leverage effect) using high-frequency data of short-dated options written on a financial asset.…”

Further Tests of the ZCAPM Asset Pricing Model

“…They used daily returns in a one-month period to compute monthly time-series standard deviations of returns for individual stocks and then averaged this idiosyncratic risk metric for N firms in the market to compute an aggregate idiosyncratic variance measure. Numerous studies have utilized a time-series market volatility factor, including those of Ang et al (2006bAng et al ( , 2009, Adrian and Rosenberg (2008), Da and Schaumburg (2011), Chang et al (2013), Bansal et al (2014), Bollerslev et al (2016), andChen et al (2021), among others. 3 Relevant to the ZCAPM, cross-sectional return dispersion is quite different from time-series dispersion.…”