Sergei Glebkin

Assistant Professor of Finance,

Working Papers

Liquidity versus Information Efficiency

I analyse liquidity, information efficiency and welfare in a market with large and small traders. Large traders create noise in the price for small traders, and vice versa, due to private value differences across the two groups. More liquidity induces large traders to trade more aggressively, creating more noise for small traders; less informative prices, in turn, incite small traders to provide more liquidity. Implications of this interaction are twofold: (i) an increase in competition between large traders may make all traders worse-off, (ii) an increase in the quality of private information may reduce information efficiency.

Funding Constraints and Informational Efficiency (with Naveen Gondhi & John Kuong), R&R RFS

We develop a tractable rational expectations model that allows for general price-dependent portfolio constraints and study a setting where constraints arise because of margin requirements. We argue that constraints affect and are affected by informational efficiency, leading to a novel amplification mechanism. A decline in wealth tightens constraints and reduces investors' incentive to acquire information, lowering price informativeness. Lower informativeness, in turn, increases the risk borne by financiers who fund trades, leading them to further tighten constraints. This information spiral implies that risk premium, return volatility, and Sharpe ratio may rise significantly as investors' wealth declines.

Strategic Trading without Normality

I present a model of strategic trading a la Kyle (1989) that does not require the assumption of normally distributed asset payoffs. I propose a constructive solution method: finding the equilibrium reduces to solving a linear ordinary differential equation. With non-normal payoffs, the price response becomes an asymmetric, non-linear function of order size: greater for buys than sells and concave (convex) for small sell (buy) orders when asset payoffs are positively skewed; concave for large sell (buy) orders when payoffs are bounded below (above). The model can speak to key empirical findings and provides new predictions concerning the shape of price impact.

A Model of Request for Quote Systems in OTC Markets (with Ji Shen and Bart Zhou Yueshen)

The “Request-for-Quote” (RFQ) system has become a major trading protocol in over-the-counter markets. It allows traders to contact multiple counterparties for prices at the same time. Through a dynamic model, this paper examines the features and implications of RFQ. In equilibrium, the contacted traders strategically quote prices via a mixed-strategy, due to the endogenous uncertainty of competitor types. The model yields testable predictions about price distribution, quote response rate, and their dynamics both in steady state and in transition. Compared to a bilateral-bargain setting, RFQ leads to more efficient allocation but, with low search friction, traders may refrain from using it.

Old Working Papers

Capital Market Equilibrium with Competition Among Institutional Investors (with Dmitry Makarov)

We develop a dynamic general equilibrium model to study how competition among institutional investors affects the stock market characteristics - level, expected return, and volatility. We consider an economy in which multiple fund managers strategically interact with each other, as each manager tries to increase her performance relative to the others. We fully characterize an equilibrium in this economy, and find that a more intense competition is associated with a higher level of the market, lower expected market return, while market volatility is not affected by competition. These findings are broadly consistent with the data.