nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒10‒29
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement By Michael Ludkovski; James Risk
  2. What Drives Systemic Bank Risk in Europe: the balance sheet effect By Wosser, Michael
  3. Asset encumbrance, bank funding and fragility By Toni Ahnert; Kartik AnandAuthor-Name: Prasanna Gai; James Chapman
  4. The real effects of bank capital requirements By Henri Fraisse; Mathias LéAuthor-Name: David Thesmar
  5. Dynamic Portfolio Optimization with Looping Contagion Risk By Longjie Jia; Martijn Pistorius; Harry Zheng
  6. Prospect Theory and Earnings Manipulation: Examination of the Non-Uniform Relationship between Earnings Manipulation and Stock Returns Using Quantile Regression By Leon Li; Nen-Chen Richard Hwang
  7. Costless Capital Requirements By Flore, Raphael
  8. Asset Price Bubbles and Systemic Risk By Brunnermeier, Markus K; Rother, Simon; Schnabel, Isabel
  9. Natural disasters and bank stability: Evidence from the U.S. financial system By Noth, Felix; Schüwer, Ulrich
  10. Financial Stability in Europe: Banking and Sovereign Risk By Jan Bruha; Evžen Kocenda
  11. Minimize risk by monitoring farm energy costs By Petersen, Dana; Hanna, Mark
  12. Guiding Principles for Financial Regulation; Panel Remarks at "The Future of Global Finance: Populism, Technology, and Regulation" Conference, Columbia University, New York, NY By Mester, Loretta J.
  13. Networks of Volatility Spillovers among Stock Markets By Eduard Baumöhl; Evžen Kocenda; Stefan Lyócsa; Tomás Vyrost

  1. By: Michael Ludkovski; James Risk
    Abstract: We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor levels at the risk horizon. Traditionally, evaluating portfolio losses of an outer scenario is done by computing a conditional expectation via inner-level Monte Carlo and is computationally expensive. We introduce several inter-related machine learning techniques to speed up this computation, in particular by properly accounting for the simulation noise. Our main workhorse is an advanced Gaussian Process (GP) regression approach which uses nonparametric spatial modeling to efficiently learn the relationship between the stochastic factors defining scenarios and corresponding portfolio value. Leveraging this emulator, we develop sequential algorithms that adaptively allocate inner simulation budgets to target the quantile region. The GP framework also yields better uncertainty quantification for the resulting VaR/TVaR estimators that reduces bias and variance compared to existing methods. We illustrate the proposed strategies with two case-studies in two and six dimensions.
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1710.05204&r=rmg
  2. By: Wosser, Michael (Central Bank of Ireland)
    Abstract: Since the 2008 global financial crisis (GFC) several systemic risk measures (SRMs) have gained traction in the literature. This paper examines whether Delta-CoVaR (?CoVaR) is relevant in the context of European banks and compares risk rankings against those found using marginal expected shortfall (MES). The analysis reveals that a cluster of large banks, operating in one particular country, is the principal contributor to financial system risk, if measured by ?CoVaR. When the direction of risk flow is reversed, i.e. from the system to the institution (via MES), a second cluster of banks, headquartered in a different jurisdiction, would be most affected by a large and systemic financial shock. The analysis reveals that future realisations of systemic risk is strongly associated with institution size, maturity mismatch, non-performing loans and non-interest-to-interest-income ratios. However, in certain cases, the relationship depends upon the systemic risk measure used. For example, forward bank leverage appears correlated with MES but not with ?CoVaR.
    Keywords: Systemic banking crisis, Systemic risk measurement, ?CoVaR, MES, Bank Balance Sheet, Macroprudential policy
    JEL: G01 G21 G28
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:cbi:wpaper:08/rt/17&r=rmg
  3. By: Toni Ahnert; Kartik AnandAuthor-Name: Prasanna Gai; James Chapman
    Abstract: We propose a model of asset encumbrance by banks subject to rollover risk and study the consequences for fragility, funding costs, and prudential regulation. A bank’s choice of encumbrance trades off the benefit of expanding profitable investment funded by cheap long-term secured debt against the cost of greater fragility due to unsecured debt runs. We derive several testable implications about privately optimal encumbrance ratios. Deposit insurance or wholesale funding guarantees induce excessive encumbrance and exacerbate fragility. We show how regulations such as explicit limits on encumbrance ratios and revenueneutral Pigouvian taxes can mitigate the risk-shifting incentives of banks.JEL Classification: G01, G21, G28
    Keywords: asset encumbrance, rollover risk, wholesale funding, fragility, runs, secured debt, unsecured debt, encumbrance limits, encumbrance surcharges
    Date: 2017–07
    URL: http://d.repec.org/n?u=RePEc:srk:srkwps:201752&r=rmg
  4. By: Henri Fraisse; Mathias LéAuthor-Name: David Thesmar
    Abstract: We measure the impact of bank capital requirements on corporate borrowing and investment using loanE level data. The Basel II regulatory framework makes capital requirements vary across both banks and across firms, which allows us to control for firm level credit demand shocks and bankE level credit supply shocks. We find that a 1 percentage point increase in capital requirements reduces lending by 10%. Firms can attenuate this reduction by substituting borrowing across banks, but only partially. The resulting reduction in borrowing capacity impacts investment, but not working capital: Fixed assets are reduced by 2.6%, but lending to customers is unaffected. JEL Classification: E51, G21, G28
    Keywords: bank capital ratios, bank regulation, credit supply
    Date: 2017–06
    URL: http://d.repec.org/n?u=RePEc:srk:srkwps:201747&r=rmg
  5. By: Longjie Jia; Martijn Pistorius; Harry Zheng
    Abstract: In this paper we consider a utility maximization problem with defaultable stocks and looping contagion risk. We assume that the default intensity of one company depends on the stock prices of itself and another company, and the default of the company induces an immediate drop in the stock price of the surviving company. We prove the value function is the unique continuous viscosity solution of the HJB equation. We also compare and analyse the statistical distributions of terminal wealth of log utility based on two optimal strategies, one using the full information of intensity process, the other a proxy constant intensity process. These two strategies may be considered respectively the active and passive optimal portfolio investment. Our simulation results show that, statistically, active portfolio investment is more volatile and performs either much better or much worse than the passive portfolio investment in extreme scenarios.
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1710.05168&r=rmg
  6. By: Leon Li (University of Waikato); Nen-Chen Richard Hwang (California State University, San Marcos)
    Abstract: Using the prospect theory as a research framework, this paper makes contributions by demonstrating that managerial risk preference and stock return may influence a firm’s earnings manipulation behavior. Specifically, this study argues that corporate executives may develop risk-averting (risk-seeking) attitudes because of high and positive (low and negative) stock returns. Under this scenario, managers may decide to actively manage the reported earnings in order to preserve gain (gamble on loss) on stock returns. On the other hand, firm executives may not actively manipulate their reported incomes when experiencing average and close-to-zero stock returns. Using quantile regression method to examine the relation between earnings manipulation and stock returns, this study finds that there is a significantly positive (negative) relation between earnings manipulation and stock returns at the high (low) stock returns quantiles. As predicated, such relation is not significant at the middle range of stock returns. To ensure the findings reported in this study are robust, we conduct several tests. In conclusion, we offer policy implications to regulators.
    Keywords: prospect theory; quantile regression; earnings manipulation; stock return; discretionary accruals
    JEL: G12 G32
    Date: 2017–10–27
    URL: http://d.repec.org/n?u=RePEc:wai:econwp:17/25&r=rmg
  7. By: Flore, Raphael
    Abstract: Arguments for costs of capital requirements in the long run are based on trade-off theories of capital structure. This paper provides a critical assessment of these theories by studying how the optimal capital structure can be modified, when a firm uses the financial markets to alter its cash flow distribution. I show that, for any initial set of assets, the implementation of a certain fund structure allows the firm to reduce its leverage and its bankruptcy probability without a loss of value.
    JEL: G32 G30 G28 G10
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:zbw:vfsc17:168226&r=rmg
  8. By: Brunnermeier, Markus K; Rother, Simon; Schnabel, Isabel
    Abstract: This paper empirically analyzes the effects of asset price bubbles on systemic risk. Based on a broad sample of banks from 17 OECD countries between 1987 and 2015, we show that asset price bubbles in stock and real estate markets raise systemic risk at the bank level. The strength of the effect depends strongly on bank characteristics (bank size, loan growth, leverage, and maturity mismatch) as well as bubble characteristics (length and size). These findings suggest that the adverse effects of bubbles can be mitigated substantially by strengthening the resilience of financial institutions.
    Keywords: Asset price bubbles; CoVaR; Credit Booms; Financial crises; systemic risk
    JEL: E32 G01 G12 G20 G32
    Date: 2017–10
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:12362&r=rmg
  9. By: Noth, Felix; Schüwer, Ulrich
    Abstract: We document that natural disasters significantly weaken the stability of banks with business activities in affected regions. This is reflected, among others, in higher probabilities of default and foreclosure ratios. The effects are economically relevant and suggest that insurance payments and public aid programs do not sufficiently protect bank borrowers against financial difficulties. We also find that the adverse effects dissolve after some years if no further disasters occur in the meantime.
    JEL: G21 Q54
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:zbw:vfsc17:168263&r=rmg
  10. By: Jan Bruha; Evžen Kocenda
    Abstract: We analyze the link between banking sector quality and sovereign risk in the whole European Union over 1999–2014. We employ four different indicators of sovereign risk (including market- and opinion-based assessments), a rich set of theoretically and empirically motivated banking sector characteristics, and a Bayesian inference in panel estimation as a methodology. We show that a higher proportion of non-performing loans is the single most influential sector-specific variable that is associated with increased sovereign risk. The sector’s depth provides mixed results. The stability (capital adequacy ratio) and size (TBA) of the industry are linked to lower sovereign risk in general. Foreign bank penetration and competition (a more diversified structure of the industry) are linked to lower sovereign risk. Our results also support the wake-up call hypothesis in that markets re-appraised a number of banking sector-related issues in the pricing of sovereign risk after the onset of the sovereign crisis in Europe.
    Keywords: sovereign default risk, banking sector, global financial crisis, financial stability, European Union
    JEL: E58 F15 G21 G28 H63
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_6453&r=rmg
  11. By: Petersen, Dana; Hanna, Mark
    Date: 2016–03–25
    URL: http://d.repec.org/n?u=RePEc:isu:genstf:201603251509491410&r=rmg
  12. By: Mester, Loretta J. (Federal Reserve Bank of Cleveland)
    Abstract: At times, the regulatory framework that has arisen since the global financial crisis can seem like the game of fizzbin (appeared in the original Star Trek TV Show) — very complicated, seemingly without rationale, and constantly changing. In such an environment, sometimes it helps to take a step back and focus on some underlying principles that should serve as a foundation for any financial regulatory framework, and that can help guide any potential changes to strengthen the framework and promote cross-country harmonization.
    Keywords: Regulation; Risk Management; incentives;
    Date: 2017–10–20
    URL: http://d.repec.org/n?u=RePEc:fip:fedcsp:87&r=rmg
  13. By: Eduard Baumöhl; Evžen Kocenda; Stefan Lyócsa; Tomás Vyrost
    Abstract: In our network analysis of 40 developed, emerging and frontier stock markets during 2006–2014, we describe and model volatility spillovers during global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market – volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.
    Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis
    JEL: C31 C58 F01 G01 G15
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_6476&r=rmg

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