Andrea Deghi, PhD

Work Experience

International Monetary Fund (IMF), Monetary and Capital Markets Department

(August 2018-)

Financial Sector Expert

Macroeconomic and Asset Price Forecasts

· Lead forecasting of asset prices and macroeconomic activity
· Designed factor-models to assess overvaluation in global equity and bond markets

Bank and Non-bank Financial Institutions

· Lead stress test and scenario design to assess fragility in banks, mutual funds, and insurances
· Monitored bank and non-bank fragility for IMF country missions in Hong Kong SAR and Japan
· 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

· Lead regular monitoring and analysis of commercial real estate valuation and fragility
· 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

Proposed and constructed a composite index of systemic risk to predict negative GDP shocks using time-varying factor models and quantile regressions
Measured interbank contagion risk by applying agent-based network models to bilateral exposures between euro area banks
Modeled the term structure implications of negative interest rate policy

European Central Bank (ECB), Financial Stability Surveillance Division

(2016 – 2017)

PhD Trainee

Quantified the effects of negative interest rates on bank profitability and systemic risk
Identified the determinants of bank mergers through an endogenous matching model
Analyzed the effects of loan market concentration on firm loan rates

Deutsche Bundesbank, Research Department

(2015 – 2016)

PhD Research Assistant

Modeled and estimated the impact of interbank market frictions on firm loan supply
Conducted policy analysis on banks’ strategic choice of network position
Designed machine learning algorithms for the harmonization of supervisory data