Rustam Jamilov

I am a PhD candidate in Economics at London Business School.

My research fields are macroeconomics and finance. I am also interested in spatial economics and cybersecurity.

Contact: rjamilov[at]


I received the AQR Asset Management Institute Fellowship Award (link)


Credit Market Power (Preliminary. First Version: March 2020)

I offer new estimates of spatial credit demand elasticities (CDE) in the U.S. by exploiting within-bank cross-regional variation in weekly changes in interest rates on home equity loans in response to the 2006-2009 housing net worth shock. The mean structural elasticity of substitution across bank branches is 1.18, a low value that is consistent with localized monopolistic banking. An index of local credit market power is made available for 48 U.S. States. Results are not driven by bank lending opportunities, local demographic characteristics, or measures of deposit market concentration. Instead, spatial variation in CDEs can be explained by the cross-state differential in the timing of bank deregulation laws over 1960-2000. In order to study the positive and normative implications of local credit market power, I develop a tractable quantitative model with monopolistic financial intermediation, endogenous bank default, and incomplete markets. Banks have market power in the asset market and charge markups over the cost of funds. I characterize an aggregate credit supply externality: individual banks do not internalize that raising private credit margins lowers collective returns and depresses total investment, output, and welfare. The externality yields a spatial distribution of relative welfare effects from local credit market power. States with a CDE that is one standard deviation below the median experience excess welfare losses amounting to 0.54% of lifetime consumption. Second, the externality decreases monetary policy effectiveness. I find that for each one-standard-deviation decline in local CDE, total responsiveness to monetary shocks falls by 7.22%. Finally, design of optimal credit markup-correcting policies is not obvious because of the endogenous financial competition-stability trade-off.


Granular Credit Risk, with S. Galaasen, R. Juelsrud, and H. Rey

We show that idiosyncratic shocks to banks' granular borrowers have a large impact on loan outcomes at all levels of aggregation. The effect is strongly concave, consistent with the payoff structure of the debt contract. Using detailed data on banks' non-interest income, we find that such risks largely go unhedged. Affected banks pass on portfolio-level granular credit shocks to their non-granular corporate clients via reduced loan supply. This leads to a decline in firm investment and an increased probability of bankruptcy. Our empirical strategy employs a novel administrative matched bank-firm dataset from Norway and exploits the heavy tail of the loan size distribution by constructing a Gabaix and Koijen (2020) granular instrumental variable. Our results are useful for calibrating the granularity adjustment in the regulation of credit concentration risk.

The distribution of institutional investor risk-taking carries significant explanatory power for the cross-section of asset returns. We compute an investor-level Value-at-Risk (VaR) measure - our proxy for ex-ante riskiness - from a structural model with stochastic volatility that we estimate with a particle filter. Our pricing factor - CrossRisk - is then constructed from shocks to the procyclical dispersion of the time-varying VaR distribution. CrossRisk is able to price equity, bond, CDS, options, currency, and commodity market portfolios comparably to numerous single and multi-factor benchmarks. We show that the mechanism behind our results is the extensive margin - dynamic entry and exit of investors into the risky market. A synthetic high minus-low CrossRisk beta pre-sorted equity portfolio built on the full universe of CRSP firms has an annualized returns spread of 5.8%.


Systemic Cyber Risk, with A. Tahoun [Draft Coming Soon]

Surveys consistently rank cyber security among biggest challenges for firms, behind only political and climate change risk. The European Systemic Risk Board recently identified cyber attacks as a new source of systemic risk to the financial system. Despite continuous interest from both industry participants and policy makers, empirical and theoretical research on the economics of cyber security is lacking. This paper fills the gap by constructing a novel index of firm-level cyber security risk and uncertainty. We utilize machine learning techniques and earnings conference calls data to extract textual references to cyber threats.

Targeted Bank Runs [Draft Coming Soon]

This paper incorporates targeted financial panics into a canonical macroeconomic framework with a financial sector ala Gertler and Kiyotaki (2010). Monopolistically competitive banks intermediate funds between households and productive capital and face uninsurable idiosyncratic rate of return shocks. The nonlinear model yields a stationary distribution of bank net worth. Households can decide to run on any individual bank in this distribution. Probability of each run is bank-specific and depends on fundamental balance sheet characteristics. Unlike Diamond and Dybvig (1983), targeted bank runs have quantitatively significant macroeconomic effects because the endogenous bank size distribution is very skewed.

Dividend Taxation and Aggregate Demand, with P. Nenov and A. Simsek

Bank Bewley Problems, with T. Monacelli