NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH
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Research Conference on Quantifying Systemic Risk

November 6, 2009
Joseph G. Haubrich and Andrew W. Lo, Organizers

Sujit Kapadia, Bank of England; Mathias Drehmann, Bank for International Settlements; John Elliott and Gabriel Sterne, Bank of England
Liquidity Risk, Cash Flow Constraints, and Systemic Feedbacks

Kapadia,Drehmann, Elliott, and Sterne analyze liquidity feedbacks in a quantitative model of systemic risk. Previous related work incorporated the outright closure of funding markets to institutions in a balance sheet based model for U.K. banks which itself included macro-credit risk, income risk, and network interactions. The model is known as RAMSI and is still in the development phase. The focus here is on the systemic implications of banks' defensive actions when suffering funding liquidity problems and trying to meet a cashflow constraint. The model incorporates a number of channels important in the recent financial crisis. As the bank loses access to longer-term funding markets, its liabilities may become increasingly short term, further undermining confidence. Stressed banksÂ’ defensive actions include liquidity hoarding and asset fire sales. This behavior affects the rest of the financial system and can trigger funding problems at other banks. In presenting results, the researchers focus on a scenario in which distress at some banks can trigger liquidity feedbacks which may cause contagion and lead to the failure of some institutions.


Jon Danielsson, London School of Economics; Hyun Song Shin, Princeton University; and Jean-Pierre Zigrand, London School of Economics
Risk Appetite and Endogenous Risk

Risk is endogenous. Equilibrium risk is the fixed point of the mapping that takes perceived risk to actual risk. When risk-neutral traders operate under Value-at-Risk constraints, market conditions exhibit signs of fluctuating risk appetite and amplification of shocks through feedback effects. Correlations in returns emerge even when underlying fundamental shocks are independent. Danielsson,Shin, and Zigrand derive a closed-form solution of equilibrium returns, correlation, and volatility by solving the fixed point problem in closed form. They apply their results to stochastic volatility and option pricing.

Tobias Adrian, Federal Reserve Bank of New York; and Markus K. Brunnermeier, Princeton University and NBER
CoVaR

Adrian and Brunnermeier propose a measure for systemic risk: CoVaR, the value at risk (VaR) of the financial system conditional on institutions being under distress. They define an institution's (marginal) contribution to systemic risk as the difference between CoVaR and the financial system VaR. From their estimates of CoVaR for characteristic sorted portfolios of publicly traded financial institutions, they quantify the extent to which characteristics such as leverage, size, and maturity mismatch predict systemic risk contribution. They argue for macro-prudential regulation based on the degree to which such characteristics forecast systemic risk contribution.


Romney Duffey, Atomic Energy of Canada Limited
The Quantification of Systemic Risk and Stability: New Methods and Measures


Gianni De Nicolo, Internatonal Monetary Fund; and Marcella Lucchetta, University of Verona,
Systemic Risk and the Macroeconomy

De Nicolo and Lucchetta design a model that delivers joint forecasts of indicators of systemic risk and financial system-at-risk , as well as stress-tests of these indicators as impulse responses to structural shocks identified by standard macroeconomic and banking theory. The model is implemented using large sets of quarterly time series of aggregate and sectoral indicators of financial and real activity for the G-7 economies for the 1980Q1-2009Q3 period. They obtain two main results. First, there is evidence of out-of sample forecasting power for tail risk realizations of real activity for several countries, suggesting the usefulness of the model as a risk monitoring tool. Second, in all countries aggregate demand shocks are the main drivers of the real cycle, and bank credit demand shocks are the main drivers of the bank lending cycle. These results challenge the common wisdom that constraints in the supply of credit have been a key driver of the sharp downturn in real activity of the G-7 economies experienced in 2008Q4-2009Q1.

 
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