1 / 18

A Macroeconomic Model of Endogenous Systemic Risk Taking

A Macroeconomic Model of Endogenous Systemic Risk Taking. Discussion Rafal Raciborski DG ECFIN, European Commission Norges Bank, Oslo,  29 - 30 November 2012. D. Martinez-Miera and J. Suarez. Disclaimer.

holland
Download Presentation

A Macroeconomic Model of Endogenous Systemic Risk Taking

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A Macroeconomic Model of Endogenous Systemic Risk Taking Discussion Rafal Raciborski DG ECFIN, European Commission Norges Bank, Oslo,  29 - 30 November 2012 D. Martinez-Miera and J. Suarez

  2. Disclaimer The views expressed are the author’s alone and do not necessarily correspond to those of the European Commission.

  3. Context • It's been almost 5 years that the world has been in the financial and economic crisis… • …with its causes still not yet fully understood… • …but with a contribution of the financial sector generally unquestioned Most economists would agree the financial sector (banks in particular) may contribute to and perhaps generate systemic risk

  4. This paper • Discusses one particular channel via which systemic risk may originate in the banking sector • Idea most closely linked to the 'risk-shifting literature’ • Embeds it into a general equilibrium model • May be disputed whether the systemic risk is truly endogenous; more on it later • Solves nonlinearly to discuss optimal bank capital requirements

  5. The model: general idea • General result (Jensen&Meckling, 1976; Stiglitz&Weiss, 1981; Allen&Gale, 2000): • Limited liabilitynon-convexities in the profit maximizer's problem • The maximizer may then prefer a riskier project, pushing its risk on other agents (=risk shifting) • Banks protected by deposit insurance (limited liability) they like riskier projects • But: riskier behavioursystemic risk • Assume that riskier projects are systematically linked

  6. The model: available projects • 2 types of projects: • Less risky projects (in terms of its variance and itsmean): idiosyncratic risk • More risky projects: risk perfectly correlated • Higher variance of the risky projects to induce risk-shifting in the banks • Correlation of risky projects=systemic risk • Lower unconditional mean of the risky project probably makes things harder; conveys the idea of systemic risk being "bad"

  7. The model: equilibrating force Due to limited liability banks like riskier projects; why don't we observe only the riskier ones being chosen (share of risky projects x=1)? • Crucial variable: stochastic marginal value of one unit of a banker's wealth • Upon the realization of the systemic risk: • Wealth of 'risky banks' is wiped out • Scarce driven up for save banks: last bank standing effect (in the spirit of Perotti&Suarez, 2002) • In equilibrium banks indifferent between projectsx

  8. Welfare • Banks’ agency problem affects negatively the economy via 2 channels: • Static losses: picking inefficient projects • Dynamic losses: loss of bank equity (and, hence, lending capacity) in the event of a systemic shock • Measurement: • All agents risk neutral; but GDP does not reflect welfare well • GDP (=added value) excludes capital losses • Does output (y=GDP+undepreciated K) correlate perfectly with welfare in your model?

  9. Capital requirements • Increased capital requirements γ make capital scarcer ( higher) higher incentive to choose safer projects higher proportion of bank equity invested in safer projects • But, banks’ lending capacity reduced lower average efficiency • Trade-off optimal γ

  10. Results • For the benchmark calibration: • With low γ=7% fraction of capital invested in systemic projects very large (70%) • Systemic shocks very painful (31% drop of GDP) • Optimal γ large (14%) • Optimal γ welfare higher by about 1% • Number of extensions • Interesting perverse results

  11. Minor remarks (I) • You assume a pooling equilibrium • Are there other types of equilibria? • If so, how do we know yours is the relevant one? • One of your main contributions: quantitative results (“high optimal γ”); but your model ‘very stylized’. For example: • Crucial role of the slope of • It would be less steep if labour were variable…

  12. Minor remarks (II) • An issue with calibration? • You assume 35% depreciation in failed firms • For γ=7%, 70% of all projects are systemic • This gives 35%×70%=25% capital depreciation in the economy in the event of a systemic shock • Also the fall in GDP (30%) very large • Develop the sensitivity analysis • “The choices for the values of […] ψ and φ are quite tentative.”

  13. General equilibrium? Is systemic risk endogenous? • Yes: share of bad projects x=f(,regulation) • No: systemically-risky projects are always there to be picked only the severity of the crisis endogenous I believe we cannot do w/o opening the black box – see next 2 slides

  14. Take the black box as given What are the systemic projects? • Allen&Gale (2000): oil shock – convincing, but with a limited application (Norway!) • Your footnote 1: housing bust: • Is it systemic? What makes it so? • Was it (before 2007) considered risky? (The notion that “house prices never fall”) • Even so: Is it plausible? Convince the reader! • What happens in your model if you have 2 types of risky projects: identical payoffs, but projects of the 2nd type independent

  15. Bring your channel to the data “Systemic Banking Crises facts” (Boissay et al.): • SBC’s are rare and deep • SBC’s are closely linked to credit developments Ad. a) Your model can obviously match it, but: • by imposing exogenous prob. of a systemic crisis • endogenous risk correlation in recessions, Brunnermeier&Sannikov, 2011 (parsimony) Ad. b) Nothing to say about it • again, endogenous link (Boissay et al., 2012) • hard to make policy advice w/o a crucial channel Need to open up the black box

  16. Interesting perverse effect? • Your results sensitive to the exogenous probability of a systemic crisis • Benchmark: ε=0.03 • One view: makes your results fragile • Alternative view: innovations that make the economy safer (ε↘) make crises deeper… Worth exploring?

More Related