When Micro Prudence Increases Macro Risk: The Destabilizing Effects of Financial Innovation, Leverage, and Diversification

Corsi, Fulvio; Marmi, Stefano; Lillo, Fabrizio

doi:10.1287/opre.2015.1464

Cited by 58 publications

(50 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This behaviour may be due to a regulatory constraint as proposed in Zigrand et al (2010), Adrian and Boyarchenko (2012) and Corsi et al (2013). Here, we assume that banks use a Value-at-Risk (VaR) approach to control their exposure to the stock market.…”

Section: Risk Managementmentioning

confidence: 99%

The dynamics of the leverage cycle

Aymanns

Farmer

2015

Journal of Economic Dynamics and Control

View full text Add to dashboard Cite

a b s t r a c tWe present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high and vice versa; a phenomenon that has been dubbed pro-cyclical leverage. We show that this leads to endogenous irregular oscillations, in which gradual increases in stock prices and leverage are followed by drastic market collapses, i.e. a leverage cycle. This phenomenon is studied using simplified models that give a deeper understanding of the dynamics and the nature of the feedback loops and instabilities underlying the leverage cycle. We introduce a flexible leverage regulation policy in which it is possible to continuously tune from pro-cyclical to countercyclical leverage. When the policy is sufficiently countercyclical and bank risk is sufficiently low the endogenous oscillation disappears and prices go to a fixed point. While there is always a leverage ceiling above which the dynamics are unstable, countercyclical leverage policies can be used to raise the ceiling. We also study the impact on leverage cycles of direct, temporal control of the bank's riskiness via the bank's required Value-at-Risk quantile. Under such a rule the regulator relaxes the Value-at-Risk quantile following a negative stock price shock and tightens it following a positive shock. While such a policy rule can reduce the amplitude of leverage cycles, its effectiveness is highly dependent on the choice of parameters. Finally, we investigate fixed limits on leverage and show how they can control the leverage cycle.

show abstract

Section: Risk Managementmentioning

confidence: 99%

The dynamics of the leverage cycle

Aymanns

Farmer

2015

Journal of Economic Dynamics and Control

View full text Add to dashboard Cite

show abstract

“…A stochastic dynamic is for instance the one considered in Corsi et al [68]. They consider a system of N banks investing in m randomly chosen assets out of a universe of M possible assets, where m is, however, computed as the optimal value of diversification that corresponds to banks maximizing their profit conditional on a VaR constraint, which is equivalent to leverage targeting [69].…”

Section: Cont and Schaanningmentioning

confidence: 99%

“…As for the other models we discuss here, the relative composition of the portfolio does not change over time, but at each time banks change the volume of their investment on the portfolio to maintain their target leverage. Corsi et al [68] show that, upon increasing diversification, the system goes from a stable regime where time series of asset returns are stationary to an unstable regime where they are characterized by bubbles and bursts.…”

Section: Cont and Schaanningmentioning

confidence: 99%

Network models of financial systemic risk: a review

2017

View full text Add to dashboard Cite

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus, in particular, on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

show abstract

“…Shared investments create a significant overlap of portfolios between couples of financial institutions. Such (indirect) financial interconnectedness is an important source of contagion, since partial liquidation of assets by a single market player is expected to affect all other market participants that share with it a large fraction of their own investments (see Corsi et al, 2013;Huang et al, 2013;Caccioli et al, 2014;Lillo and Pirino, 2015, for a survey of the role of portfolio overlap in spreading financial distress). Fire sales move prices due to the finite liquidity of assets and to market impact.…”

Section: Introductionmentioning

confidence: 99%

Assessing Systemic Risk Due to Fire Sales Spillover Through Maximum Entropy Network Reconstruction

2015

Self Cite

View full text Add to dashboard Cite

Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemic risk due to fire sales spillover and portfolio rebalancing by using the risk metrics defined by Greenwood et al. (2015). By using the Maximum Entropy principle we propose a method to assess aggregated and single bank's systemicness and vulnerability and to statistically test for a change in these variables when only the information on the size of each bank and the capitalization of the investment assets are available. We prove the effectiveness of our method on 2001-2013 quarterly data of US banks for which portfolio composition is available.JEL codes: C45;C80;G01;G33.

show abstract

When Micro Prudence Increases Macro Risk: The Destabilizing Effects of Financial Innovation, Leverage, and Diversification

Cited by 58 publications

References 49 publications

The dynamics of the leverage cycle

The dynamics of the leverage cycle

Network models of financial systemic risk: a review

Assessing Systemic Risk Due to Fire Sales Spillover Through Maximum Entropy Network Reconstruction

Contact Info

Product

Resources

About