Gerardo Manzo

Quant Researcher at Kepos Capital

Personal Biography

I am a quant researcher at Kepos Capital.

Before joining Kepos Capital I was at AQR Capital Management in the systematic credit research team and at Two Sigma Investments in the asset management team. I started my research work in finance in 2012 when I was at the University of Chicago Booth SoB, initially as a visiting scholar and then as a post-doctoral fellow at the Fama-Miller Center for Research in Finance.

My research interest spans several topics, including asset pricing, credit risk, systemic risk and macro-finance. I am the recipient of several academic awards, including: the 2016 Jack Treynor Prize, the 2014 UniCredit & Universities Best Ph.D. Thesis Award, the 2014 John A. Doukas Best Ph.D. Paper Award and the 2011 Orazio Ruggeri Best Master Thesis Award.

I earned the Ph.D. in Finance at the University of Rome 'Tor Vergata' in 2013, and the master's degree with summa cum laude and special mention (“dignità di stampa”) in economics and finance at LUISS 'Guido Carli' University in Rome in 2010. My undergraduate work was in economics and finance at the University of Salerno where I graduated in 2007.

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Latest working paper:

DEEP LEARNING CREDIT RISK MODELING (with Xiao Qiao), July 2020

Latest publications:

THE IMPACT OF SOVEREIGN SHOCKS (with Antonio Picca), Management Science, 2020

SOVEREIGN CREDIT RISK (with Pietro Veronesi), in Handbook of Fixed-Income Securities, 2016 Wiley

Work Experience.

  • 2020-date

    Kepos Capital, New York (NY)

    Quant Researcher

  • 2018-2020

    AQR Capital Management, Greenwich (CT)

    Vice President in Credit and Fixed-Income Research

  • 2016-2018

    Two Sigma Investments, New York (NY)

    Global macro and asset-management research

  • 2014-2016

    Fama-Miller Center for Research in Finance at University of Chicago Booth School of Business, Chicago (IL)

    Post-doctoral fellow

  • 2012-2014

    University of Chicago Booth School of Business, Chicago (IL)

    Visiting Ph.D. scholar

Education.

Publications.

tDIP

The Impact of Sovereign Shocks

Management Science, 2020 (with A. Picca)

This paper studies the dynamic propagation mechanisms of systemic risk shocks within and across macro-systems of governments and financial institutions. We propose a novel approach to identify relevant systemic shocks and to classify them into sovereign or banking categories. We find that sovereign shocks have a significant and persistent impact on the probability of a collective banking default. We also explore channels through which these shocks propagate and identify how sovereign fiscal fragility and banking exposure are relevant mechanisms of shock transmission.

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Sov

Sovereign Credit Risk

in Handbook of Fixed-Income Securities, Wiley 2016 (with P. Veronesi)

This chapter reviews recent techniques to model sovereign credit risk and applies them to the credit markets of both emerging and European economies. It shows how to model the sovereign credit risk in a reduced‐form setting and how to price credit default swap (CDS) contracts written on sovereign debt. The modeling framework enables to decompose the credit spread into two components: the credit risk premium and the default risk. The former captures the compensation investors demand for bearing the risk due to unexpected variations in the default intensity, whereas the upon default the default risk captures the real probability of default of a country or of an institution. The chapter then describes how to estimate the pricing model with market data using the Quasi-maximum likelihood estimator (MLE) that exploits the features of the probability distribution of the default intensity to match actual CDS spreads.

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Working Papers.

DNNplot

Deep Learning Credit Risk Modeling

Working Paper, July 2020 (with Xiao Qiao)

This paper demonstrates how deep learning can be used to price and calibrate models of credit risk. Deep neural networks can learn structural and reduced-form models with high degrees of accuracy. For complex credit risk models, whose closed-form solutions are not available, deep learning offers a conceptually simple and more efficient alternative solution. We propose an approach that combines deep learning with the unscented Kalman filter to calibrate credit risk models on historical data, which attains an in-sample R-squared of 98.5 percent for the reduced-form model and 95 percent for the structural model.

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CIVplot

Credit Implied Volatility

Chicago Booth Research Paper No. 17-22, June 2019 (with B. Kelly and D. Palhares)

We define and construct a credit-implied volatility (CIV) surface from the firm-by-maturity panel of CDS spreads. We use this framework to organize the behavior of corporate credit markets into three stylized facts. First, CIV exhibits a steep moneyness smirk. Second, the joint dynamics of credit spreads on all firms are captured by three interpretable factors in the CIV surface. Third, the cross section of CDS risk premia is fully explained by exposures to CIV surface shocks. We propose a structural model for joint asset behavior of all firms that is characterized by stochastic volatility and time-varying downside tail risk in aggregate asset growth.

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regionalCDS

Political Uncertainty, Credit Risk Premium and Default Risk

2013 working paper

I empirically decompose sovereign credit spreads into a default-risk component and its associated (credit) risk premium and study the effect of political uncertainty on them. On average, credit risk premia account for 42 percent of the observed spreads in the European sovereign credit market. I find that a 10 percent increase in political uncertainty leads to a 3 percent increase in both components after a month. A regional-level analysis reveals heterogeneity in the response of sovereign risk to variations in political uncertainty. This work enriches the understanding of how macroeconomic forces drive variations in sovereign risk and introduces political uncertainty as a significant factor driving the European credit market.

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SystemicRisk

What Drives Systemic Risk? Political Uncertainty and Contagion in Credit Markets

Unicredit & Universities Working Paper Series, 2013

Ph.D. Dissertation: Three essays in empirical asset pricing with a focus on sovereign credit and systematic risk.

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Research.

Code Library

A collection of the Python Codes I created for my academic research

I have split the codes by topic. Use them at your own risk.

ASSET PRICING

tDIP: t-Student Distress Insurance Premium tDIP Input Computation CDS pricing model of Pan & Singleton (2008)

ECONOMETRICS

Identification through Heteroskedasticity (Rigobon, 2003) VARX (Vector Autoregression with Exogenous variables) FDA Regression for Curve Estimation and Inference (Functional Data Analysis) Block Bootstrap

I conduct research on asset pricing with a focus on credit risk, systemic risk, and macro-finance.

Data Library

A collection of some data I used in my academic research.

Data & Code Library - The Impact of Sovereign Shocks (Manzo & Picca, 2019)

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