Dapeng Shang

I am a Principal Quantitative Analyst at Capital One with a Ph.D. in Mathematical Finance from Boston University. My research focuses on macro-finance, empirical asset pricing, market microstructure, and robust optimization.

Research

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Robust Portfolio and Dynamic Disaster Risk

Dapeng Shang, H. Xing, and P. Maenhout

Working Paper, 2025

This paper explores the effect of disaster risk on the beliefs and portfolio choices of ambiguity-averse agents. With the introduction of Cressie-Read discrepancies, a time-varying pessimism state variable arises endogenously, generating time-varying disaster risk. In the event of a disaster, agents heighten their pessimism, anticipating subsequent disasters to arrive sooner. Within this framework, we deduce optimal consumption and portfolio choices that are robust to model misspecification. Additionally, our measure of pessimism aids in understanding the stylized facts derived from Vanguard's retail investor survey data, as reported in Giglio et al. (2021).

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Option Implied Risk Information on Macroeconomic Announcements

Dapeng Shang

Working Paper, 2024

This paper constructs a novel measure to assess the impact of macro announcements on investors' risk expectations using S&P500 index and Treasury future options. This measure corrects the systematic downward bias in the option-implied variance measure and isolates innovations of investors' risk expectations after macro-announcements. Applied to key economic releases, including FOMC meetings, GDP, PPI, and Employment data announcements, this measure reveals that macro announcements significantly increase investors' risk expectations compared to pre-announcement levels. Furthermore, I show that investor sentiment significantly declines following macro-announcements with heightened risk expectations.

SSRN
SIAM

Inventory Management for High-Frequency Trading With Imperfect Competition

Dapeng Shang, S. Herrmann, J. Muhle-Karbe, and C. Yang

SIAM Journal on Financial Mathematics, Vol. 11 (2020), No. 1, pp. 1-26

We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions are obtained around the high-frequency limit. Unlike in the risk-neutral case, the optimal inventories become mean-reverting and vanish as the number of trading rounds becomes large. In contrast, the HFTs' risk-adjusted profits and the equilibrium price impact converge to their risk-neutral counterparts. Compared to cooperative HFTs, Nash competition leads to excess trading, so that marginal transaction taxes in fact decrease market liquidity.

DOI SSRN