Andrea Deghi
Work Experience
International Monetary Fund (IMF), Monetary and Capital Markets Department
(August 2018-)
Financial Sector Expert
Macroeconomic and Asset Price Forecasts
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· Lead forecasting of asset prices and macroeconomic activity
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· Designed factor-models to assess overvaluation in global equity and bond markets
Bank and Non-bank Financial Institutions
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· Lead stress test and scenario design to assess fragility in banks, mutual funds, and insurances
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· Monitored bank and non-bank fragility for IMF country missions in Hong Kong SAR and Japan
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· Selected as team lead for the IMF’s Global Financial Stability Report on the Sovereign-Bank Nexus in Emerging Markets (Chapter 2 April 2022)
Real Estate
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· Lead regular monitoring and analysis of commercial real estate valuation and fragility
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· Selected as team lead for the IMF’s Global Financial Stability Report on Commercial Real Estate Financial Stability Risks During the Covid-19 Crisis and Beyond (Chapter 3 April 2021)
European Central Bank (ECB), Systemic Risk and Financial Institutions Division,
(August 2017-July 2018)
Research Analyst / Expert
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Proposed and constructed a composite index of systemic risk to predict negative GDP shocks using time-varying factor models and quantile regressions
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Measured interbank contagion risk by applying agent-based network models to bilateral exposures between euro area banks
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Modeled the term structure implications of negative interest rate policy
European Central Bank (ECB), Financial Stability Surveillance Division
(2016 – 2017)
PhD Trainee
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Quantified the effects of negative interest rates on bank profitability and systemic risk
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Identified the determinants of bank mergers through an endogenous matching model
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Analyzed the effects of loan market concentration on firm loan rates
Deutsche Bundesbank, Research Department
(2015 – 2016)
PhD Research Assistant
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Modeled and estimated the impact of interbank market frictions on firm loan supply
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Conducted policy analysis on banks’ strategic choice of network position
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Designed machine learning algorithms for the harmonization of supervisory data