Simon Potter keynote speaker

Former Nonresident Senior Fellow at the Peterson Institute for International Economics
SERVICE
STANDARD
PRO
VIP
TOPICS
  • Central Banking
  • Dollar
  • Financial Crisis
  • Monetary Policy
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ABOUT SPEAKER

Simon Potter was nonresident senior fellow at the Peterson Institute for International Economics from September 2019 to June 2020, with a research focus on central bank operations, monetary policy, digital currencies, reference rates, the role of the dollar, and economic forecasting.

Prior to joining the Institute, he was head of the Markets Group and System Open Market Account (SOMA) at the Federal Reserve Bank of New York. In this role, he oversaw the implementation of domestic open market and foreign exchange trading operations on behalf of the Federal Open Market Committee (FOMC), the execution of fiscal agent support for the US Treasury, the provision of account services to foreign and international monetary authorities, and the administration and production of reference interest rates for US money markets.

He started at the New York Fed in June 1998 and served as director of economic research and co-head of the Research and Statistics Group at the New York Fed, prior to becoming head of the Markets Group in June 2012 where he was responsible for monetary policy advice. He played a prominent role in the Federal Reserve’s financial stability efforts, including by contributing to the design of the 2009 US bank stress tests, as a member of the international Macroeconomic Assessment Group that supported the Basel Committee’s work to strengthen bank capital standards and, most recently, as chair of the Global Foreign Exchange Committee. In addition, he worked for the Financial Stability Oversight Council in 2011 to produce its first annual report.

Prior to working at the New York Fed, he was an assistant professor at the University of California Los Angeles. He has also taught at Johns Hopkins University, New York University, and Princeton. His academic research focused on applying Bayesian methods to time series models.


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