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BEGIN:VEVENT
DTSTAMP:20261020T001710Z
LAST-MODIFIED:20180723T135656Z
DTSTART:20180727T150000Z
DTEND:20180727T160000Z
UID:event1956@bu.edu
URL:http://physics.bu.edu/internal/events/show/1956
SUMMARY:MAXIMUM ENTROPY AND NETWORK APPROACHES TO SYSTEMIC RISK AND FOREIGN
	 EXCHANGE
DESCRIPTION:Featuring Alex Becker\, Boston University\, Physics Department\
	n\nPart of the PhD Final Oral Exams.\n\nThe global financial system is an i
	ntricate network of networks\, and recent financial crises have laid bare o
	ur insufficient understanding of its complexity. In response\, we study how
	 interconnectedness\, interdependency and mutual influence impact financial
	 markets and systemic risk. \n\nWe begin by investigating the community for
	mation of global equity and currency markets. We find remarkable changes to
	 correlation structure and lead-lag relationships in times of economic turm
	oil\, implying significant risks to diversification based on historical dat
	a. \n\nWe then turn our attention to banks as creators of credit. Bank port
	folios generally share some overlap\, and this may introduce systemic risk.
	 We model this using European stress test data\, finding that the system is
	 stable across a broad range of asset liquidity and risk tolerance. However
	\, there exists a phase transition: If banks become sufficiently risk avers
	e\, even small shocks may inflict great losses. Failure to address portfoli
	o overlap thus may leave the banking system ill-prepared.\n\nComplete knowl
	edge of the financial network is prerequisite to such systemic risk analyse
	s. When lacking this knowledge\, maximum entropy methods allow a probabilis
	tic reconstruction. To test the success of such methods\, we consider Japan
	ese firm-bank data and find that reconstruction methods fail to generate a 
	connected network. Deriving an analytical expression for connection probabi
	lities\, we show that this is a general problem of sparse graphs with inhom
	ogeneous layers. Our results yield confidence intervals for the connectivit
	y of a reconstruction.\n\nThe maximum entropy approach also proves useful f
	or studying dependencies in financial markets: On its basis\, we develop a 
	new measure for the information content in foreign exchange rates  and use 
	it to study the impact of macroeconomic variables on the strength of curren
	cy co movements. While macroeconomic data and the law of supply and demand 
	drive financial markets\, foreign exchange rates are also subject to policy
	 interventions. Finally\, we classify the roles of currencies within the ma
	rket with a clustering algorithm and study changes after political and mone
	tary shocks. This methodology may further provide a quantitative underpinni
	ng to existing qualitative classifications.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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