Associate Research Fellow, Korea Institute for International Economic Policy (KIEP), 08/2023–

Assistant Professor of Finance, City University of Hong Kong, 05/2016–06/2023

Ph.D. in Economics, University of Pennsylvania, 2012

B.Sc. in Physics and B.A. in Economics, Summa cum Laude, Seoul National University, 2006

Curriculum Vitae

Working Papers


This paper considers learning about unobservable state variables when their dynamics are ambiguous. The drift of the state process is perturbed and set-estimated by inverting a test. The evolution of the set estimate is explicitly characterized up to a system of differential equations extending the conditionally Gaussian filter and is embedded in recursive maxmin expected utility. Despite the fact that the agent is unconfident only about the drift of the state process, learning under ambiguity makes her behave as if she assumed excessive volatility for the state process. This helps explain why the long-run risk model elicits seemingly excessive long-run risk from returns data.

Online Appendix


Revise and Resubmit at the Journal of Economic Theory

This paper investigates whether ambiguity afflicting the long-run rate of growth fades away in a nonexchangeable environment (time-varying instantaneous expected growth rate). Two types of ambiguity are considered: static (multiple priors) and dynamic (multiple laws of motion). In the absence of dynamic ambiguity, likelihood-based learning resolves static ambiguity. In the presence of dynamic ambiguity, on the other hand, likelihood-based learning fails. In this case, static ambiguity fades away if the agent incorporates into the objective criteria (likelihood) her subjective criteria (penalty proportional to the Kullback–Leibler divergence).

Supplementary Appendix


The long-run risk model has been criticized for implying an excessive timing premium: The agent of the model is willing to pay too much for resolving consumption uncertainty early. In this paper, I argue that the criticism is misplaced: The agent's timing premium is not to be compared with our own, because our horizons are in fact not infinite and the timing premium turns out to be quite sensitive to the choice of the horizon—more than half of the large timing premium can be viewed as a cost of simplifying the asset-pricing model in terms of time. Furthermore, the agent's preference for early resolution of uncertainty is neither essential nor necessary, in the first place, in explaining the equity premium of the long-run risk model. With these observations, the timing premium puzzle loses much of its force.

Work in Progress

Funded by the General Research Fund of the Hong Kong Research Grants Council, 2021

Research Interests

Theoretical Asset Pricing, Portfolio Choice, Decision Making under Ambiguity

Teaching

City University of Hong Kong, 2016–2023