nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒10‒03
nineteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Out-of-Model Adjustments of Variable Annuities By Zhiyi Shen
  2. Quasi-Logconvex Measures of Risk By Roger J. A. Laeven; Emanuela Rosazza Gianin
  3. Common Idiosyncratic Quantile Risk By Jozef Barunik; Matej Nevrla
  4. On the Correspondence and the Risk Contribution for Conditional Coherent and Deviation Risk Measures By Guangyan Jia; Mengjin Zhao
  5. Biology-inspired geometric representation of probability and applications to completion and options' pricing By Felix Polyakov
  6. Big data analytics for supply chain risk management: research opportunities at process crossroads By Leonardo de Assis Santos; Leonardo Marques
  7. Precautionary Saving Behaviour under Ambiguity By Suen, Richard M. H.
  8. The systemic risk of US oil and natural gas companies By Caporin, Massimiliano; Fontini, Fulvio; Panzica, Roberto
  9. Constrained portfolios in incomplete markets: a dynamic programming approach to Heston's model By Marcos Escobar-Anel; Yevhen Havrylenko; Rudi Zagst
  10. SRISK: una medida de riesgo sistémico para la banca colombiana 2005-2021 By Camilo Eduardo Sánchez-Quinto
  11. The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes By Anand, Vaibhav
  12. How many inner simulations to compute conditional expectations with least-square Monte Carlo? By Aur\'elien Alfonsi; Bernard Lapeyre; J\'er\^ome Lelong
  13. Accounting for climate transition risk in banks' capital requirements By Alessi, Lucia; Di Girolamo, Francesca Erica; Pagano, Andrea; Petracco Giudici, Marco
  14. How Can Safe Asset Markets Be Fragile? By Thomas M. Eisenbach; Gregory Phelan
  15. Metanomics: Adaptive market and volatility behaviour in Metaverse By Shah, Anand; Bahri, Anu
  16. Modeling Volatility and Dependence of European Carbon and Energy Prices By Jonathan Berrisch; Sven Pappert; Florian Ziel; Antonia Arsova
  17. Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices By Emanuel Kohlscheen
  18. Till debt do us part: strategic divorces and a test of moral hazard By Yeorim Kim; Mauro Mastrogiacomo; Stefan Hochguertel; Hans Bloemen
  19. Quantitative Analysis of Haircuts: Evidence from the Japanese Repo and Securities Lending Markets By Kazuya Suzuki; Kana Sasamoto

  1. By: Zhiyi Shen
    Abstract: This paper studies the model risk of the Black-Scholes (BS) model in pricing and risk-managing variable annuities motivated by its wide usage in the insurance industry. Specifically, we derive a model-free decomposition of the no-arbitrage price of the variable annuity into the BS model price in conjunction with three out-of-model adjustment terms. This sheds light on all risk drivers behind the product, that is, spot price, realized volatility, future smile, and sub-optimal withdrawal. We further investigate the efficacy of the BS-based hedging strategy given the market diverges from the model assumptions. We disclose that the spot price risk can always be eliminated by the strategy and the hedger's cumulative P\&L exhibits gradual slippage and instantaneous leakage. We finally show that the pricing, risk and hedging models can be separated from each other in managing the risks of variable annuities.
    Date: 2022–08
  2. By: Roger J. A. Laeven; Emanuela Rosazza Gianin
    Abstract: This paper introduces and fully characterizes the novel class of quasi-logconvex measures of risk, to stand on equal footing with the rich class of quasi-convex measures of risk. Quasi-logconvex risk measures naturally generalize logconvex return risk measures, just like quasi-convex risk measures generalize convex monetary risk measures. We establish their dual representation and analyze their taxonomy in a few (sub)classification results. Furthermore, we characterize quasi-logconvex risk measures in terms of properties of families of acceptance sets and provide their law-invariant representation. Examples and applications to portfolio choice and capital allocation are also discussed.
    Date: 2022–07
  3. By: Jozef Barunik; Matej Nevrla
    Abstract: We propose a new model of asset returns with common factors that shift relevant parts of the stock return distributions. We show that shocks to such non-linear common movements in the panel of firm's idiosyncratic quantiles are priced in the cross-section of the US stock returns. Such risk premium is not subsumed by the common volatility, tail beta, downside beta, as well as other popular risk factors. Stocks with high loadings on past quantile risk in the left tail earn up to an annual five-factor alpha 7.4\% higher than stocks with low tail risk loadings. Further, we show that quantile factors have predictive power for aggregate market returns.
    Date: 2022–08
  4. By: Guangyan Jia; Mengjin Zhao
    Abstract: We give an axiomatic framework for conditional generalized deviation measures. Under financially reasonable assumptions, we give the correspondence between conditional coherent risk measures and generalized deviation measures. Moreover, we establish the notion of continuous-time risk contribution for conditional coherent risk measures and generalized deviation measures. With the help of the correspondence between these two different types of risk measures, we give a microscopic interpretation of their risk contributions. Particularly, we show that the risk contributions of time-consistent risk measures are still time-consistent. We also demonstrate that the second element of the BSDE solution $(Y, Z)$ associated with $g$-expectation has the meaning of risk contribution.
    Date: 2022–08
  5. By: Felix Polyakov
    Abstract: Geometry constitutes a core set of intuitions present in all humans, regardless of their language or schooling [1]. Could brain's built in machinery for processing geometric information take part in uncertainty representation? For decades already traders have been citing the price of uncertainty based FX optional contracts in terms of implied volatility, a dummy variable related to the standard deviation, instead of pricing with units of money. This work introduces a methodology for geometric representation of probability in terms of implied volatility and attempts to find ways to approximate certain probability distributions using intuitive geometric symmetry. In particular, it is shown how any probability distribution supported on $\mathbb{R}_{+}$ and having finite expectation may be represented with a planar curve whose geometric characteristics can be further analyzed. Log-normal distributions are represented with circles centered at the origin. Certain non-log-normal distributions with bell-shaped density profiles are represented by curves that can be closely approximated with circles whose centers are translated away from the origin. Only three points are needed to define a circle while it represents the candidate probability density approximating the distribution along the entire $\mathbb{R}_{+}$. Just three numbers: scaling and translations along the $x$ and $y$ axes map one circle to another. It is possible to introduce equivalence classes whose member distributions can be obtained by transitive actions of geometric transformations on any of corresponding representations. Approximate completion of probability with non-circular shapes and cases when probability is supported outside of $\mathbb{R}_{+}$ are considered too. Proposed completion of implied volatility is compared to the vanna-volga method.
    Date: 2022–09
  6. By: Leonardo de Assis Santos (UFRJ - Universidade Federal do Rio de Janeiro); Leonardo Marques (Audencia Business School)
    Abstract: Purpose The purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs. Design/methodology/approach The research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM. Findings The analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies. Originality/value This study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM.
    Date: 2022
  7. By: Suen, Richard M. H.
    Abstract: This paper analyses a two-period model in which a consumer faces a future income risk but is uncertain about its probability distribution. We derive three sets of sufficient conditions under which a consumer with generalised recursive smooth ambiguity (GRSA) preferences will save more under ambiguity than in a deterministic environment. Our results show how precautionary saving is jointly determined by attitudes toward atemporal risk, ambiguity and intertemporal substitution. We also find a close connection between risk prudence under non-expected utility and precautionary saving under GRSA preferences.
    Keywords: Precautionary Saving; Risk Aversion; Intertemporal Substitution; Smooth Ambiguity Preferences.
    JEL: D81 E21
    Date: 2022–08–02
  8. By: Caporin, Massimiliano (Universita degli Studi di Padova); Fontini, Fulvio (Universita degli Studi di Padova); Panzica, Roberto (European Commission)
    Abstract: We analyse the evolution of the systemic risk impact of oil and natural gas companies since 2000. This period is characterised by several events that affected energy source markets: the real effect of the global financial crisis, the explosion of shale production and the diffusion of the Covid-19 pandemic. The price of oil and natural gas showed extreme swings, impacting companies' financial situations, which, accompanied by technological developments in shale production, had an impact on the debt issuance and on the overall risk level of the oil and natural gas sector. By studying the systemic impact of oil and natural gas companies on risk in the financial market, measured by the ∆CoVaR, we observe that in the most recent decade, their role is sensibly increasing compared to 2000–2010, even accounting for the possible effect associated with the increase in companies' sizes. In addition, our results show evidence of a decreasing relevance of traditional drivers of systemic risk, suggesting that additional factors might be present. Finally, when focusing on the impact of Covid-19, we document its relevant role in fueling the increase in the oil and natural gas companies' systemic impact.
    Keywords: Systemic risk, Oil and Natural Gas, Fossil Fuel, Energy
    JEL: Q43 Q40 G10 C21 C58
    Date: 2022–07
  9. By: Marcos Escobar-Anel; Yevhen Havrylenko; Rudi Zagst
    Abstract: We solve an expected utility-maximization problem with terminal-wealth constraints via dynamic programming in a setting of incomplete markets due to stochastic volatility. We demonstrate that the value function in the constrained problem can be represented as an expected modified utility of a vega-neutral financial derivative on the optimal unconstrained wealth. The optimal wealth and the optimal investment strategy in the constrained problem follow similarly. The case of a power utility and a Value-at-Risk constraint is treated theoretically in details. In numerical studies, we substantiate the impact of risk aversion levels, and investment horizons on the optimal investment strategy. We find a 20% relative difference between constrained and unconstrained allocations for average parameters in a low risk-aversion, short-horizon setting.
    Date: 2022–08
  10. By: Camilo Eduardo Sánchez-Quinto
    Abstract: Una de las lecciones que dejó la crisis financiera de 2008 fue la importancia de monitorear el riesgo sistémico en la búsqueda de la estabilidad de los sistemas financieros. Al respecto se han desarrollado líneas de investigación que, tomando la mayor cantidad de información, tienen el objetivo de brindar métricas fiables y oportunas de este riesgo. Entre ellas se encuentra el SRISK (Brownlees & Engle, 2016), una medida que combina el comportamiento del mercado, la relación de solvencia, el nivel de apalancamiento y los resultados contables de las entidades financieras para hallar el riesgo sistémico bajo un escenario de crisis financiera. Este documento replica la metodología SRISKajustada para el sistema bancario colombiano a través de modelos GJR-GARCH-DCC. Los resultados indican que, si bien el riesgo sistémico en la banca ha sido históricamente bajo, este alcanzó su máximo histórico en 2020, mostrando el impacto de la crisis sanitaria del Covid-19. Adicionalmente, se encuentra que el SRISK se correlaciona con variables de la actividad productiva y financiera, además tener capacidad predictiva en sentido de Granger. **** ABSTRACT: One of the lessons we learned from the 2008 financial crisis was the importance of monitoring the systemic risk in the stability of financial systems. In this regard, lines of research have been developed with the aim to provide reliable and timely metrics on this risk, taking as much information as possible. Among these, SRISK(Brownlees & Engle, 2016) stands out, a measure that combines market behavior, capital ratio, leverage and balance sheet of financial institutions to find the systemic risk exposure under a sustained crisis scenario. This paper replicates the SRISKmethodology adjusted for the Colombian banking system using GJR-GARCH-DCC models. The results show that, although systemic risk of banks has been historically low, it reached its maximum in 2020, adding empirical evidence on the impact of Covid-19 crisis. Furthermore, it is found that SRISKcorrelates with leading indicators of economic and financial sectors, in addition to having predictive power in the sense of Granger causality.
    Keywords: Riesgo sistémico, sistema bancario, causalidad de Granger, modelos Garch multivariados, Colombia, Systemic risk, banking system, Granger causality, multivariate Garch models, Colombia
    JEL: C22 C53 E44 G01 G21
    Date: 2022–09
  11. By: Anand, Vaibhav
    Abstract: Scientific and technological advances are resulting in improved forecasts of risk, but do better forecasts result in better risk management? I investigate to what extent the improvements in lead time of winter weather advisories affect the frequency of motor vehicle crashes. I construct a data set of winter weather advisories, weather monitor readings, and vehicle crashes at the county-date level in 11 states in the US during 2006-2018. Using within county variation in lead time, I show that receiving winter advisories earlier reduces crash risk significantly. I also examine two potential mechanisms that might lead to these effects. First, using the mobile phone location data from SafeGraph, I show that longer lead times result in fewer visits by people to places outside their homes. Second, using snow plow truck location data, I show that road crews perform a greater level of winter maintenance activities when advisories arrive with longer lead time. Overall, this study provides evidence that improvements in forecast lead times result in meaningful economic benefits to society, and these benefits come from both the individual and institutional response to longer lead times.
    Keywords: Weather forecast, risk mitigation, auto crash
    JEL: D80 D81 H41 Q50 Q54 Q55 Q58 R00 R41
    Date: 2022–09–01
  12. By: Aur\'elien Alfonsi (MATHRISK, CERMICS); Bernard Lapeyre (MATHRISK, CERMICS); J\'er\^ome Lelong (DAO)
    Abstract: The problem of computing the conditional expectation E[f (Y)|X] with least-square Monte-Carlo is of general importance and has been widely studied. To solve this problem, it is usually assumed that one has as many samples of Y as of X. However, when samples are generated by computer simulation and the conditional law of Y given X can be simulated, it may be relevant to sample K $\in$ N values of Y for each sample of X. The present work determines the optimal value of K for a given computational budget, as well as a way to estimate it. The main take away message is that the computational gain can be all the more important that the computational cost of sampling Y given X is small with respect to the computational cost of sampling X. Numerical illustrations on the optimal choice of K and on the computational gain are given on different examples including one inspired by risk management.
    Date: 2022–09
  13. By: Alessi, Lucia (European Commission); Di Girolamo, Francesca Erica (European Commission); Pagano, Andrea (European Commission); Petracco Giudici, Marco (European Commission)
    Abstract: This paper uses a stylized simulation model to assess the potential impact of transition risk on banks' balance sheets and establishes a basis for calibrating relevant macro-prudential instruments. We show that even in the short run, a fire-sale mechanism could amplify an initially contained shock on high-carbon assets into a systemic crisis with significant losses for the EU banking sector. We calculate that an additional capital buffer of 0.5% RWA on average would be sufficient to protect the system. Moreover, under an orderly transition, the decrease in banks’ transition risk exposure due to the greening of the economy would reduce the effect of a fire-sale by a factor of 10.
    Keywords: Green transition risk, dynamic balance sheet, banking crisis
    JEL: C15 G2 Q54
    Date: 2022–06
  14. By: Thomas M. Eisenbach; Gregory Phelan
    Abstract: The market for U.S. Treasury securities experienced extreme stress in March 2020, when prices dropped precipitously (yields spiked) over a period of about two weeks. This was highly unusual, as Treasury prices typically increase during times of stress. Using a theoretical model, we show that markets for safe assets can be fragile due to strategic interactions among investors who hold Treasury securities for their liquidity characteristics. Worried about having to sell at potentially worse prices in the future, such investors may sell preemptively, leading to self-fulfilling “market runs” that are similar to traditional bank runs in some respects.
    Keywords: safe assets; liquidity shocks; global games; Treasury securities; COVID-19
    JEL: G1
    Date: 2022–09–08
  15. By: Shah, Anand; Bahri, Anu
    Abstract: This study presents stylized facts of the fungible tokens/currencies (MANA/USD and SAND/USD) in the Metaverses (Decentraland and The Sandbox). Metaverse currency exchange rate market exhibits very high conditional volatility, albeit no leverage effect, less impact of the real-world crisis (Global Lockdown due to COVID 19 pandemic) and low correlation with either cryptocurrency index (CCi30) or real-world equity index (S&P 500). Surprisingly, MANA and SAND – fungible tokens/ currencies in different Metaverses exhibit significant and increasing correlation between each other. The relative market efficiency of Metaverse currency market is comparable to that observed in the cryptocurrency and equity markets in the real-world.
    Keywords: Metanomics, Metaverse, Fungible Tokens, Cryptocurrency, Non-Fungible Tokens (NFTs), Blockchain, Adaptive Market Hypothesis, Dynamic Conditional Correlation
    JEL: G01 G11 G14 G32
    Date: 2022–09–01
  16. By: Jonathan Berrisch; Sven Pappert; Florian Ziel; Antonia Arsova
    Abstract: We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy markets. We propose a probabilistic multivariate conditional time series model that exploits key characteristics of the data. The forecasting performance of the proposed model and various competing models is evaluated in an extensive rolling window forecasting study, covering almost two years out-of-sample. Thereby, we forecast 30-steps ahead. The accuracy of the multivariate probabilistic forecasts is assessed by the energy score. We discuss our findings focusing on volatility spillovers and time-varying correlations, also in view of the Russian invasion of Ukraine.
    Date: 2022–08
  17. By: Emanuel Kohlscheen
    Abstract: This study analyses oil price movements through the lens of an agnostic random forest model, which is based on 1,000 regression trees. It shows that this highly disciplined, yet flexible computational model reduces in sample root mean square errors by 65% relative to a standard linear least square model that uses the same set of 11 explanatory factors. In forecasting exercises the RMSE reduction ranges between 51% and 68%, highlighting the relevance of non linearities in oil markets. The results underscore the importance of incorporating financial factors into oil models: US interest rates, the dollar and the VIX together account for 39% of the models RMSE reduction in the post 2010 sample, rising to 48% in the post 2020 sample. If Covid 19 is also considered as a risk factor, these shares become even larger.
    Date: 2022–08
  18. By: Yeorim Kim; Mauro Mastrogiacomo; Stefan Hochguertel; Hans Bloemen
    Abstract: We test whether households that face prospective home equity losses during a house price downturn use divorce to shed debt. We study the Dutch context, where qualifying homeowners can buy into a mortgage guarantee scheme that insures the lender against borrower default and transfers the risk to the public. Divorce is one of the major events that obliges the guarantor to repay outstanding residual debt after (foreclosure) sale. We argue in this paper that divorce is endogenous to holding underwater mortgages, and hence constitutes a choice that can be used for strategic use of the insurance. Using administrative data, we find a significant, 44% increase in the probability to divorce for households with an underwater mortgage. This effect is causal to being insured. The identification relies on a regression discontinuity design, that exploits the fact that the insurance is only available for properties with values below a legislated qualification threshold. The house price crisis (2008-2013) provides an unexpected shock to house values, leaving about 40% of owners with an underwater mortgage. Their home equity averages to about €-50.000. Couples with similar characteristics just above the qualification threshold experienced significantly less often a divorce than couples just below the threshold. We interpret this behavioral response as moral hazard, also because the induced divorcees reunite at a higher rate than other divorcees.
    Keywords: moral hazard, mortgage insurance, divorce
    JEL: D10 G21 G52 J12
    Date: 2022–08
  19. By: Kazuya Suzuki (Bank of Japan); Kana Sasamoto (Bank of Japan)
    Abstract: Given the absence of comprehensive studies on market structure and haircuts for repo and securities lending transactions, this study provides a quantitative analysis of the subject using government bonds and equities transaction data covering most of the Japanese market. Specifically, we conducted a panel data regression analysis of government bond repo transactions, controlling for factors such as transaction entities and transaction types, and provided a detailed analysis of the haircut-setting mechanism. Accordingly, we determined that explanatory variables affecting credit risk, market risk, and liquidity risk, such as the credit quality of government bonds, the residual maturity of government bonds, and the presence of foreign exchange risk, significantly impact haircut setting. Furthermore, financial institutions closer to the center of the network, which engage in transactions with additional financial institutions, tend to set lower haircut rates through more efficient matching of borrowing and lending needs for cash and securities. Thus, the credit quality of government bonds transacted, exchange rate stability, and the presence of intermediaries important to the trading network significantly impact the degree of market functioning. The results were robust, paving the way for further discussions on trends and risk management of securities financing transactions, which are essential to financial markets.
    Keywords: Securities Financing Transactions; Repurchase Agreement; Haircut; Network Analysis
    JEL: D80 E43 G10 G20 L14
    Date: 2022–08–22

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