nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒05‒08
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  1. Model Risk for Acceptable, but Imperfect, Discrimination and Calibration in Basel PD and LGD Models By Henry Penikas
  2. Hedging Valuation Adjustment for Callable Claims By Cyril B\'en\'ezet; St\'ephane Cr\'epey; Dounia Essaket
  3. Dark Matter in (Volatility and) Equity Option Risk Premiums By Gurdip Bakshi; John Crosby; Xiaohui Gao
  4. Medium-term growth-at-risk in the euro area By Lang, Jan Hannes; Rusnák, Marek; Greiwe, Moritz
  5. Adjust factor with volatility model using MAXFLAT low-pass filter and construct portfolio in China A share market By Ke Zhang
  6. Risk-Taking Behavior during Downturn: Evidence of Loss-Chasing and Realization Effect in the Cryptocurrency Market By Voraprapa Nakavachara; Roongkiat Ratanabanchuen; Kanis Saengchote; Thitiphong Amonthumniyom; Pongsathon Parinyavuttichai; Polpatt Vinaibodee
  7. The Role of Climate in Deposit Insurers' Fund Management: More Than a Financial Risk Management Factor? By Van Roosebeke, Bert; Defina, Ryan
  8. Identifying Preference for Early Resolution from Asset Prices By Hengjie Ai; Ravi Bansal; Hongye Guo; Amir Yaron
  9. Biodiversity Risk By Giglio, Stefano; Kuchler, Theresa; Stroebel, Johannes; Zeng, Xuran
  10. Identifying and assessing systemic risks in Ireland: a review of the Central Bank’s toolkit By Hallissey, Niamh; Killeen, Neill; Wosser, Michael
  11. Biodiversity Risk By Stefano Giglio; Theresa Kuchler; Johannes Stroebel; Xuran Zeng
  12. Optimal self-protection and health risk perception: bridging the gap between risk theory and the Health Belief Model By Emmanuelle Augeraud-Véron; Marc Leandri
  13. Banking on Uninsured Deposits By Itamar Drechsler; Alexi Savov; Philipp Schnabl; Olivier Wang
  14. Robust optimized certainty equivalents and quantiles for loss positions with distribution uncertainty By Weiwei Li; Dejian Tian
  15. On heavy-tailed risks under Gaussian copula: the effects of marginal transformation By Bikramjit Das; Vicky Fasen-Hartmann
  16. The Interrelationship of Credit and Climate Risks By Henry Penikas
  17. Modeling Indian Bank Nifty volatility using univariate GARCH models By M N, Nikhil; Chakraborty, Suman; B M, Lithin; Ledwani, Sanket

  1. By: Henry Penikas (Bank of Russia, Russian Federation)
    Abstract: The Basel Internal-Ratings-Based (IRB) approach allows banks to use sufficiently good credit risk models for the daily computation of their capital adequacy ratio. However, being sufficiently good does not naturally mean being perfect. Conventionally, risk managers increase the mean probability of default (PD) and loss given default (LGD) values by some margin when developing a model. They expect that it is sufficient to offset for potential model risk. This add-on, thought to be conservative enough, gave rise to the term Ôconservative marginÕ. The novelty of this paper is that we are the first to identify the cases when such a margin is not sufficient. The principal cause is the previously ignored requirement to ÒfreezeÓ capital against the existing loans. This capital tie-up does not allow reallocating excessive capital requirements from actual non-defaults (false negatives) to unforeseen defaults (false positives). This is the first time when such a novel cause of model risk is discussed. The value the paper creates is severalfold. First, the revealed model risk has a material scale and can be part of the bankÕs explicit or implicit risk-taking strategy. Therefore, it should be considered by researchers, as well as by validators and supervisors. Second, the paper offers an indicator to expand the risk indicator dashboard suggested by the Basel Committee, when designing risk-reward remuneration. This is especially true of contracts with risk model developers.
    Keywords: validation, IRB, Basel II, AUROC, CLAR, Loss Shortfall, Brier skill score, traffic light approach
    JEL: C52 G28 G32
    Date: 2022–04
    URL: http://d.repec.org/n?u=RePEc:bkr:wpaper:wps92&r=rmg
  2. By: Cyril B\'en\'ezet (LaMME, ENSIIE); St\'ephane Cr\'epey (LPSM); Dounia Essaket (LPSM)
    Abstract: Darwinian model risk is the risk of mis-price-and-hedge biased toward short-to-medium systematic profits of a trader, which are only the compensator of long term losses becoming apparent under extreme scenarios where the bad model of the trader no longer calibrates to the market. The alpha leakages that characterize Darwinian model risk are undetectable by the usual market risk tools such as value-at-risk, expected shortfall, or stressed value-at-risk.Darwinian model risk can only be seen by simulating the hedging behavior of a bad model within a good model. In this paper we extend to callable assets the notion of hedging valuation adjustment introduced in previous work for quantifying and handling such risk. The mathematics of Darwinian model risk for callable assets are illustrated by exact numerics on a stylized callable range accrual example. Accounting for the wrong hedges and exercise decisions, the magnitude of the hedging valuation adjustment can be several times larger than the mere difference, customarily used in banks as a reserve against model risk, between the trader's price of a callable asset and its fair valuation.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.02479&r=rmg
  3. By: Gurdip Bakshi; John Crosby; Xiaohui Gao
    Abstract: Emphasizing the statistics of jumps crossing the strike and local time, we develop a decomposition of equity option risk premiums. Operationalizing this theoretical treatment, we equip the pricing kernel process with unspanned risks, embed (unspanned) jump risks, and allow equity return volatility to contain unspanned risks. Unspanned risks are consistent with negative risk premiums for jumps crossing the strike and local time and imply negative risk premiums for out-of-the-money call options and straddles. The empirical evidence from weekly and farther-dated index options is supportive of our theory of economically relevant unspanned risks and reveals ``dark matter" in option risk premiums.
    Date: 2023–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2303.16371&r=rmg
  4. By: Lang, Jan Hannes; Rusnák, Marek; Greiwe, Moritz
    Abstract: Financial stability indicators can be grouped into financial stress indicators that reflect heightened spreads and market volatility, and financial vulnerability indicators that reflect credit and asset price imbalances. Based on a panel of euro area countries, we show that both types of indicators contain information about downside risks to real GDP growth (growth-at-risk) in the short-term (1-year ahead). However, only vulnerability indicators contain information about growth-at-risk in the medium-term (3-years ahead and beyond). Among various vulnerability indicators suggested in the literature, the Systemic Risk Indicator (SRI) proposed by Lang et al. (2019) outperforms in terms of in-sample explanatory power and out-of-sample predictive ability for medium-term growth-at-risk in euro area countries. Shocks to the SRI induce a rich ”term structure” for growth-at-risk: downside risks to real GDP growth are reduced in the short-term, but over the medium-term the effect reverses and downside risks to real GDP growth go up considerably. We also show that using cross-country information from the panel of euro area countries can improve the out-of-sample forecasting performance of growth-at-risk for the euro area aggregate. JEL Classification: E37, E44, G01, G17, C22
    Keywords: financial stress, financial vulnerabilities, growth-at-risk, local projections, quantile regression
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:ecb:ecbwps:20232808&r=rmg
  5. By: Ke Zhang
    Abstract: In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China's CSI 300 and CSI 500 universe which represent China's large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index.
    Date: 2023–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.04676&r=rmg
  6. By: Voraprapa Nakavachara; Roongkiat Ratanabanchuen; Kanis Saengchote; Thitiphong Amonthumniyom; Pongsathon Parinyavuttichai; Polpatt Vinaibodee
    Abstract: Psychologists and economists have both explored how past outcomes influence subsequent risktaking behavior. However, psychologists traditionally focused on gambling, while economists mainly looked at investors’ decisions under uncertainty. As a result, the two fields arrived at different conclusions. Psychology literature identified loss-chasing behavior among casino gamblers and labeled it “compulsive gambling, †a disorder requiring treatment. Economists, on the other hand, introduced a concept of the “realization effect, †suggesting that risk-taking may increase after an unrealized loss but decrease after a realized loss (Imas, 2016). This paper aims to reconcile these two perspectives using empirical evidence from the cryptocurrency market, where gamblers and investors coexist. We find that high-risk individuals increase risk-taking after both unrealized and realized losses. In the most severe case, a one-standard-deviation increase in loss would raise risk-taking, as measured by portfolio volatility, by 58.72%. Thus, high-risk individuals in the cryptocurrency market behave like casino gamblers observed in Psychology literature. On the other hand, low-risk individuals increase risk-taking only after unrealized losses and avoid risks after realized losses. Thus, low-risk individuals in the cryptocurrency market behave like investors facing risky choices, which can be explained by the “realization effect†in Economics literature.
    Keywords: Cryptocurrency; Realization Effect; Loss-Chasing; Behavioral Finance
    JEL: D81 G11 G41
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:pui:dpaper:206&r=rmg
  7. By: Van Roosebeke, Bert; Defina, Ryan
    Abstract: Drawing on a survey amongst IADI Members, this IADI Survey Brief takes stock of the incorporation of climate related issues in fund management by deposit insurers. It provides a snapshot of current deposit insurer practices, identifies deposit insurers’ expectations, and explores possibilities for future developments.
    Keywords: deposit insurance; bank resolution; ESG
    JEL: G21 G33
    Date: 2023–03–01
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:116936&r=rmg
  8. By: Hengjie Ai; Ravi Bansal; Hongye Guo; Amir Yaron
    Abstract: This paper develops an asset market based test for preference for the timing of resolution of uncertainty. Our main theorem provides a characterization of preference for early resolution of uncertainty in terms of the risk premium of assets realized during the period when the informativeness of macroeconomic announcements is resolved. Empirically, we find support for preference for early resolution of uncertainty based on evidence on the dynamics of the implied volatility of S&P 500 index options before FOMC announcements.
    JEL: D0 D9 D91 G0
    Date: 2023–03
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31087&r=rmg
  9. By: Giglio, Stefano; Kuchler, Theresa; Stroebel, Johannes; Zeng, Xuran
    Abstract: We explore the effects of physical and regulatory risks related to biodiversity loss on economic activity and asset values. We first develop a news-based measure of aggregate biodiversity risk and analyze how it varies over time. We also construct and publicly release several firm-level measures of exposure to biodiversity risk, based on textual analyses of firms' 10-K statements, a large survey of financial professionals, regulators, and academics, and the holdings of biodiversity-related funds. Exposures to biodiversity risk vary substantially across industries in a way that is economically sensible and distinct from exposures to climate risk. We find evidence that biodiversity risks already affect equity prices: returns of portfolios that are sorted on our measures of biodiversity risk exposure covary positively with innovations in aggregate biodiversity risk. However, our survey indicates that market participants do not perceive the current pricing of biodiversity risks to be adequate.
    Date: 2023–04–04
    URL: http://d.repec.org/n?u=RePEc:osf:socarx:n7pbj&r=rmg
  10. By: Hallissey, Niamh (Central Bank of Ireland); Killeen, Neill (Central Bank of Ireland); Wosser, Michael (Central Bank of Ireland)
    Abstract: This Note describes the Central Bank of Ireland’s overall approach and toolkit for assessing systemic risks in Ireland. The aim of systemic risk assessments is to identify and measure the potential for negative macro-financial outcomes (“tail risks”) to occur in the future. Evaluating the nature and magnitude of risks facing the financial system in a forward-looking, systematic manner is an important input to the setting of macroprudential policy. There are four main elements to the risk identification and assessment framework including (i) the monitoring of selected indicators, (ii) the development of analytical tools and modelling approaches, (iii) qualitative tools such as the use of surveys and engagement with stakeholders and (iv) targeted deep dives on specific topics to complement regular analysis. The risk assessment draws on these different elements to inform judgements on key risks facing the financial system in Ireland.
    Date: 2022–11
    URL: http://d.repec.org/n?u=RePEc:cbi:fsnote:16/fs/22&r=rmg
  11. By: Stefano Giglio; Theresa Kuchler; Johannes Stroebel; Xuran Zeng
    Abstract: We explore the effects of physical and regulatory risks related to biodiversity loss on economic activity and asset values. We first develop a news-based measure of aggregate biodiversity risk and analyze how it varies over time. We also construct and publicly release several firm-level measures of exposure to biodiversity risk, based on textual analyses of firms’ 10-K statements, a large survey of financial professionals, regulators, and academics, and the holdings of biodiversity-related funds. Exposures to biodiversity risk vary substantially across industries in a way that is economically sensible and distinct from exposures to climate risk. We find evidence that biodiversity risks already affect equity prices: returns of portfolios that are sorted on our measures of biodiversity risk exposure covary positively with innovations in aggregate biodiversity risk. However, our survey indicates that market participants do not perceive the current pricing of biodiversity risks to be adequate.
    JEL: G10 G11 G12 Q5 Q53 Q57
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31137&r=rmg
  12. By: Emmanuelle Augeraud-Véron; Marc Leandri
    Abstract: In this contribution to the longstanding risk theory debate on optimal self-protection, we aim to bridge the gap between the microeconomic modeling of self-protection, in the wake of Ehrlich and Becker (1972), and the Health Belief Model, a conceptual framework extremely influential in Public Health studies (Janz and Becker, 1984). In doing so, we highlight the crucial role of risk perception in the individual decision to adopt a preventive behavior towards a generic health risk. We discuss the optimal prevention effort engaged by an agent displaying either imperfect knowledge of the susceptibility (probability of occurrence) or the severity (magnitude of the loss) of a health hazard, or facing uncertainty on these risk components. We assess the impact of risk aversion and prudence on the optimal level of self-protection, an issue at the core of the risk and insurance economic literature. Our results also pave the way for the design of efficient information instruments to improve health prevention when risk perceptions are biased.
    Keywords: Prevention, Self-protection, Health Belief Model, Risk perception, Risk aversion, Prudence
    JEL: D81 I12 D9
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:drm:wpaper:2023-12&r=rmg
  13. By: Itamar Drechsler; Alexi Savov; Philipp Schnabl; Olivier Wang
    Abstract: Motivated by the regional bank crisis of 2023, we model the impact of interest rates on the liquidity risk of banks. Prior work shows that banks hedge the interest rate risk of their assets with their deposit franchise: when interest rates rise, the value of the assets falls but the value of the deposit franchise rises. Yet the deposit franchise is only valuable if depositors remain in the bank. This creates run incentives for uninsured depositors. We show that a run equilibrium is absent at low interest rates but appears when rates rise because the deposit franchise comes to dominate the value of the bank. The liquidity risk of the bank thus increases with interest rates. We provide a formula for the bank’s optimal risk management policy. The bank should act as if its deposit rate is more sensitive to market rates than it really is, i.e., as if its “deposit beta” is higher. This leads the bank to shrink the duration of its assets. Shortening duration has a downside, however: it exposes the bank to insolvency if interest rates fall. The bank thus faces a dilemma: it cannot simultaneously hedge its interest rate risk and liquidity risk exposures. The dilemma disappears only if uninsured deposits do not contribute to the deposit franchise (if they have a deposit beta of one). The recent growth of low-beta uninsured checking and savings accounts thus poses stability risks to banks. The risks increase with interest rates and are amplified by other exposures such as credit risk. We show how they can be addressed with an optimal capital requirement that rises with interest rates.
    JEL: E52 G12 G21
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31138&r=rmg
  14. By: Weiwei Li; Dejian Tian
    Abstract: The paper investigates the robust optimized certainty equivalents and analyzes the relevant properties of them as risk measures for loss positions with distribution uncertainty. On this basis, the robust generalized quantiles are proposed and discussed. The robust expectiles with two specific penalization functions $\varphi_{1}$ and $\varphi_{2}$ are further considered respectively. The robust expectiles with $\varphi_{1}$ are proved to be coherent risk measures, and the dual representation theorems are established. In addition, the effect of penalization functions on the robust expectiles and its comparison with expectiles are examined and simulated numerically.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.04396&r=rmg
  15. By: Bikramjit Das; Vicky Fasen-Hartmann
    Abstract: In this paper, we compute multivariate tail risk probabilities where the marginal risks are heavy-tailed and the dependence structure is a Gaussian copula. The marginal heavy-tailed risks are modeled using regular variation which leads to a few interesting consequences. First, as the threshold increases, we note that the rate of decay of probabilities of tail sets vary depending on the type of tail sets considered and the Gaussian correlation matrix. Second, we discover that although any multivariate model with a Gaussian copula admits the so called asymptotic tail independence property, the joint tail behavior under heavier tailed marginal variables is structurally distinct from that under Gaussian marginal variables. The results obtained are illustrated using examples and simulations.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.05004&r=rmg
  16. By: Henry Penikas (Bank of Russia, Russian Federation)
    Abstract: The focus of our study is the environmental (E) risk score. For this paper, we have collected a unique database of public ESG ratings for the world largest companies in the Fortune Global 2000 list. The credit risk estimates are derived from publicly available credit ratings. The probability of default (PD) levels result from the use of historical default data. We control for the specifics of industries and sectors. The availability of E-risk data for half of the sample implies the need to apply the Heckman selection model. We show cases when the climate-credit risk relationship is robustly positive for a particular industry and region: in such cases, loan subsidies are indeed advisable to finance large green projects and green corporations (e.g. the 2021 Bank of Japan program Ð though it was tailored for SMEs). Otherwise Ð in the predominant number of cases Ð such a loan rate reduction may foster the accumulation of credit risks and pose a threat to financial stability. We contribute to the literature by showing that the revealed positive climate-credit risks dependence is not ubiquitous Ð which is argued by (Capasso, Gianfrate, & Spinelli, 2020).
    Keywords: green company, brown company, Sustainalytics, carbon dioxide emissions, Heckman.
    JEL: C24 E52 H23 O44
    Date: 2022–09
    URL: http://d.repec.org/n?u=RePEc:bkr:wpaper:wps100&r=rmg
  17. By: M N, Nikhil; Chakraborty, Suman; B M, Lithin; Ledwani, Sanket
    Abstract: The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.
    Keywords: anti-leverage, asymmetry, bank nifty, GARCH, index returns, Indian stock, leverage, return volatility
    JEL: C22 C52 G10 G17
    Date: 2022–10–11
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:116824&r=rmg

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