This paper puts forward a manual for how to set up and solve a continuous time model that allows to analyze endogenous (1) level and risk dynamics. The latter includes (2) tail risk and crisis probability as well as (3) the Volatility Paradox. Concepts such as (4) illiquidity and liquidity mismatch, (5) endogenous leverage, (6) the Paradox of Prudence, (7) undercapitalized sectors (8) time-varying risk premia, and (9) the external funding premium are part of the analysis. Financial frictions also give rise to an endogenous (10) value of money.
This chapter surveys the literature on bubbles, financial crises, and systemic risk. The first part of the chapter provides a brief historical account of bubbles and financial crisis. The second part of the chapter gives a structured overview of the literature on financial bubbles. The third part of the chapter discusses the literatures on financial crises and systemic risk, with particular emphasis on amplification and propagation mechanisms during financial crises, and the measurement of systemic risk. Finally, we point toward some questions for future research.
Liquidity and deflationary spirals self-generate endogenous risk and redistribute wealth. Monetary policy can mitigate these effects and help rebalance wealth after an adverse shock, thereby reducing endogenous risk, stabilizing the economy, and stimulating growth. The redistributive channel differs from the classic Keynesian interest rate channel in models with price stickiness. Central banks assume and redistribute tail risk when purchasing assets or relaxing their collateral requirements. Monetary policy (rules) can be seen as a social insurance scheme for an economy beset by financial frictions. As with any insurance, it carries the cost of moral hazard. Redistributive monetary policy should be strictly limited to undoing the redistribution caused by the amplification effects and by moral hazard considerations.
This paper finds non-interest income to be positively correlated with total systemic risk for a large sample of U.S. banks. Decomposing total systemic risk into three components, we find that non-interest income has a positive relationship with a bank’s tail risk, a positive relationship with a bank’s interconnectedness risk, and an insignificant or positive relationship with a bank’s exposure to macroeconomic and finance factors. These results are generally robust to endogenizing for non-interest income and for trading and other non-interest income activities.
This paper develops a framework for measuring, allocating and managing systemic risk. SystRisk, our measure of total systemic risk captures the a priori cost to society for providing tail-risk insurance to the financial system. Our allocation principle distributes the total systemic risk among individual institutions according to their size-shifted marginal contributions. To describe economic shocks and systemic feedback effects we propose a reduced form stochastic model that can be calibrated to historical data. We also discuss systemic risk limits, systemic risk charges and a cap and trade system for systemic risk.
The aim of this paper is to conceptualize and design a risk topography that outlines a data acquisition and dissemination process that informs policymakers, researchers and market participants about systemic risk. Our approach emphasizes that systemic risk (i) cannot be detected based on measuring cash instruments, e.g., balance sheet items or ratios such as leverage and income statement items; (ii) typically builds up in the background before materializing in a crisis; and (iii), is determined by market participants’ endogenous response to various shocks. Our measurement system asks that regulators elicit from market participants their (partial equilibrium) risk as well as liquidity sensitivities (our response indicator) with respect to major risk factors and liquidity scenarios. General equilibrium responses and economy-wide system effects can be calibrated using this panel data set.
This paper summarizes and explains the main events of the liquidity and credit crunch in 2007-08. Starting with the trends leading up to the crisis, I explain how these events unfolded and how four different amplification mechanisms magnified losses in the mortgage market into large dislocations and turmoil in financial markets.
We propose a measure for systemic risk, \Delta-CoVaR, defined as the conditional value at risk CoVaR of the financial system conditional on institutions being under distress in excess of the CoVaR of the system conditional on the median state of the institution. From our estimates of Delta-CoVaR for the universe of publicly traded financial institutions, we quantify the extent to which characteristics such as leverage, size, maturity mismatch, and asset price booms predict systemic risk contribution. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk and show that the 2006Q4 value of this measure would have predicted more than one third of realized \Delta-CoVaR during the financial crisis.
Predicting and measuring a financial institution's contribution to systemic risk that internalizes externalities and avoids procyclicality.