My research interests include studying financial markets in the large and in the small.
- Asset Pricing
- Tail Risk
- Algorithmic Trading
On Sources of Risk Premia in Representative Agent Models
with David Schreindorfer. SSRN
We use options and return data to decompose unconditional risk premia
into different parts of the return state space. In the data, the entire equity
premium is attributable to monthly returns below -11.3%, but returns in the
extreme left tail matter very little. In contrast, leading asset pricing models
based on habits, long-run risks, and rare disasters attribute the premium almost
exclusively to returns above -11.3%, or to the extreme left tail. We find
that model extensions with a larger quantity of tail risk cannot account for
the data, while models with a higher price of tail risk can.
The Anatomy of Trading Algorithms
with Sunil Wahal. SSRN
We study the anatomy of four widely used standardized institutional trading algorithms
representing $675 billion in demand from 961 institutions between 2012 and 2016. The central tradeoff in these algorithms is between the desire to trade and transaction costs. Large parent orders generate hundreds of child orders which strategically employ the price, time, and display priority rules embodied in market structure to navigate this tradeoff. The distribution of child orders is non-random, generating strategic runs which oscillate between providing and taking liquidity. Price impact occurs both at the time an order is submitted to the book (regardless of whether it is filled), and at the time of execution. Passive child orders have much lower likelihood of execution but still incur substantial price impact. Conversely, marketable orders, even though immediately executable, do not necessarily guarantee execution and generate even larger price impact.
Heterogeneity and Household Portfolio Choice
Simulation of a Financial Market: The Possibility of Catastrophic Disequilibrium
with Amit Sinha, Kelly Roos, & Philip Horvath. Chaos, Solitons, & Fractals, 2019, 125, 13-16. Link
We use kinetic Monte Carlo simulations to produce solutions of an agent-based, rate equation model of an informationally efficient, closed financial market. The simulations produce a crash in the market that is forewarned through the observation of a market instability from which the market temporarily recovers. The market remained in a quasi-stable state for a relatively large amount of time between the warning and the crash, raising the prospect that some mitigating action can be taken in time to avert the impending crash. This result has strong ramifications for the future of predicting calamitous market events, especially if some observable aspect of financial markets can be positively identified and associated with simulation parameters.