Chris Martin is a Senior Financial Economist at the Center for Financial Research in the FDIC's Division of Insurance and Research.
His research focuses on policy-relevant topics in applied banking and macro-finance. His dissertation examined the impacts of foreign purchases of US Treasury securities on Treasury yields. Since joining the FDIC in October 2014, Chris' research has focused on the stress testing of banks, bank depositor behavior, and bank lending. Chris’ current research leverages highly granular data from failed banks to study policy-relevant banking topics. Chris obtained a PhD and MA in Economics at Johns Hopkins University. He received a BA in Economics from McDaniel College.
Publications
Martin, Christopher, Manju Puri, and Alexander Ufier (2022), “Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance,” Journal of Finance, forthcoming.
Heitz, Amanda Rae, Christopher Martin, and Alexander Ufier (2023), “Bank Loan Monitoring, Distance, and Delegation,” AEA Papers and Proceedings, Vol. 113, pp. 177-181.
Kapinos, Pavel, Christopher Martin, and Oscar Mitnik (2018), “Stress Testing Banks: Whence and Whither?” Journal of Financial Perspectives, Vol. 5(1), pp. 68-87.
McIntyre, Kevin H. and Christopher A. Martin. (2013), "Estimates and Determinants of Firm Efficiency in Eastern Europe: Evidence from Romanian Microdata," Eastern European Economics, Vol. 51(2), pp. 58-89.
Working Papers
Heitz, Amanda Rae, Christopher Martin, and Alexander Ufier (2023), “Bank Monitoring with On-Site Inspections," FDIC Center for Financial Research Working Paper No. 2022-09.
Martin, Christopher, Manju Puri, and Alexander Ufier (2022), "Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance." FDIC Center for Financial Research Working Paper No. 2018-02.
Also published as National Bureau of Economic Research Working Paper No. 24589, May 2018.
Kapinos, Pavel, Christopher Martin and Oscar Mitnik. (2015), " Stress Testing Banks: Whence and Whither?" FDIC Center for Financial Research Working Papers. 2015-07.
Martin, Christopher A. (2015), "Identifying the Effects of Chinese Treasury Purchases Using High Frequency Data."
Martin, Christopher A. (2015), "Treasury Purchases and the Yield Curve: Evidence from a Sign-Identified Vector Autoregression."
Martin, Christopher A. (2014), "Extracting Treasury Flows as a Latent Variable."
Presentations
CONFERENCE
Mar-2023
Eastern Finance Association Conference
Bank Monitoring with On-Site Inspections
CONFERENCE
Oct-2022
Financial Management Association Annual Meeting
Bank Monitoring with On-Site Inspections
SEMINAR
Oct-2018
Johns Hopkins University
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Sept-2018
Interagency Early Warning Model Workshop
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Apr-2018
NBER Corporate Finance Conference
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Jan-2018
American Economic Association Conference
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Aug-2017
Federal Financial Institutions Examination Council International Banking Conference
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Apr-2017
Avoiding and Resolving Banking Crises Conference
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Feb-2017
Eastern Economic Association Conference
Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance
CONFERENCE
Oct-2015
International Atlantic Economic Society Conference
Treasury Purchases and the Yield Curve: Evidence from
a Sign-Identified Vector Autoregression
SEMINAR
Jul-2015
FDIC Center for Financial Research
Stress Testing Banks: Whence and Whither?
SEMINAR
Jun-2015
Federal Reserve Board
Identifying the Effects of Chinese Treasury Purchases
Using High-Frequency Data
CONFERENCE
Feb-2015
Eastern Economic Association Conference
Identifying the Effects of Chinese Treasury Purchases
Using High-Frequency Data