Gerardo Manzo

Quant Researcher and Portfolio Manager
Systematic Credit and Equity

Personal Biography

I have 10+ years of experience in applied quantitative research in financial markets. I work in the hedge fund industry as a quantitative researcher and portfolio manager.


Over the years, I have worked on different projects, including developing trading systems for systematic credit and equity strategies, alpha and risk modeling, portfolio construction, implementation, and management, market analyses from a macro standpoint, and many more.


Data is what's behind my work and I use any tool to explore it, from statistics to machine learning to econometrics.


I worked as a quant researcher and PM at top-tier hedge funds like Kepos Capital, AQR Capital Management, and Two Sigma Investments. I started my research work in finance in 2012 when I was at the University of Chicago Booth School of Business, 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 and machine learning. 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 my Ph.D. in Finance at the University of Rome 'Tor Vergata' in 2013, and my master's 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.


Enjoy my website!

Download Short Resume (latest update: Jan 2022)

Latest publications:

DEEP LEARNING CREDIT RISK MODELING (with Xiao Qiao), The Journal of Fixed Income, 2021

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

Latest working paper:

CREDIT-IMPLIED VOLATILITY (with Bryan Kelly and Diogo Palhares), 2020

Work Experience.

  • March 2020
    April 2022

    Kepos Capital, New York (NY)

    Quant Researcher and Portfolio Manager in Systematic Credit and Equity

  • Sept 2018
    March 2020

    AQR Capital Management, Greenwich (CT)

    Vice President in Credit and Fixed-Income Research

  • Feb 2016
    May 2018

    Two Sigma Investments, New York (NY)

    Global macro and asset-management research

  • Jan 2014
    Aug 2016

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

    Post-doctoral fellow

Education.

Publications.

DNNplot

Deep Learning Credit Risk Modeling

The Journal of Fixed Income, 2021 (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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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.

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/GitHub.

GitHub Repository

A collection of Python codes I created over the years, including published papers and fun projects

----> GitHub Website

I conduct research on asset pricing and rely on statistics and machine learning techniques.

Feedback is welcome!

Contact.