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Korea Institute for International Economic Policy (KIEP), 08/2023–
- Research Fellow, International Finance Team, 01/2025–
- Associate Research Fellow, International Finance Team, 08/2023–12/2024
City University of Hong Kong, 05/2016–06/2023
- Assistant Professor (Finance), Department of Economics and Finance
Ph.D. in Economics, University of Pennsylvania, 2012
M.A. in Economics, University of Pennsylvania, 2011
B.Sc. in Physics and B.A. in Economics, Summa cum Laude, Seoul National University, 2006
Working Papers
Revise and Resubmit at the Journal of Economic Theory
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.
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). The model of learning is also applied to portfolio choice.
Publication
On the Timing Premium Puzzle, 2025, Economics Letters
The long-run risk model has been criticized for implying an excessive timing premium (excessive willingness to pay 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 not infinite and the timing premium turns out to be sensitive to the choice of the horizon. 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.
Work in Progress
Can Learning Reduce the Dark Matter in the Long-Run Risk Model?
General Research Fund, Hong Kong Research Grants Council, 01/2022–12/2023
Retirement Pension Portfolio Choice with AI Assistance: An Experimental Study, with Jeongbin Kim, Matthew Kovach, Kyu-Min Lee, Euncheol Shin, and Hector Tzavellas
Research Interests
Theoretical Asset Pricing, Portfolio Choice, Decision Making under Ambiguity
Teaching
City University of Hong Kong, 2016–2023
Derivatives and Risk Management
Fixed Income Securities
Stochastic Calculus for Finance
Theoretical Asset Pricing (Part 2/4)
Financial Management
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