The pricing of jump risk in the cross-section

Lin, Huidi

doi:https://doi.org/10.21985/n2-sd9k-xe28

Work

The pricing of jump risk in the cross-section

Public

Download PDF

This dissertation studies the pricing of jump risk in the cross-section, which builds on understanding the cross-sectional structure of jumps at market jump times, develops nonparametric measures of idiosyncratic jump risk, and investigates its asset pricing implications. Chapter 1 is joint work with Professor Viktor Todorov, in which we study the structure and pricing of idiosyncratic jumps, i.e., jumps in asset prices that occur outside market-wide jump events. Using options on individual stocks and the market index that are close to expiration as well as local estimates of market betas from returns on the underlying assets, we estimate nonparametrically the asymmetry in the risk-neutral expected idiosyncratic variation, i.e., the difference in variation due to negative and positive returns, which asymptotically is solely attributed to jumps. We derive a feasible Central Limit Theorem that allows to quantify precision in the estimation, with the limiting distribution being mixed Gaussian. We find strong empirical evidence for aggregate asymmetry in idiosyncratic risk which shows that such risk clusters cross-sectionally. Our results reveal the existence and non-trivial pricing of aggregate downside tail risk in stocks during market-neutral systematic events as well as a negative skew in the cross-sectional return distribution during such episodes. Chapter 2 builds on the econometric theory from chapter 1 and studies the structure of information arrival on the individual stock level and its implication for the cross-section of asset prices. Firm-level information flow can be smooth or lumpy. In the latter case the valuation of the firm tend to have large adjustments happening in a short time window. The arrival of lumpy information is reflected in the price of various financial instruments -- stock prices react with discrete jumps at the moment of news arrival and forward-looking option prices react with changes in the term structure ahead of the arrival of pre-scheduled news. I design nonparametric measures of the structure of information arrival for individual stocks under both risk-neutral measure and physical measure, and show that this feature helps explain the cross-sectional variation of expected stock returns. Further empirical evidence and an illustrative theoretical model suggest that lumpy information arrival carries a type of risk that investors are averse to, which requires premia that cannot be explained by existing risk factors.

Creator