nep-rmg New Economics Papers
on Risk Management
Issue of 2019‒07‒29
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. A simulation of the insurance industry: The problem of risk model homogeneity By Torsten Heinrich; Juan Sabuco; J. Doyne Farmer
  2. Risk reduction and efficiency increase in large portfolios: leverage and shrinkage By Zhao Zhao; Olivier Ledoit; Hui Jiang
  3. Incremental Sharpe and other performance ratios By Eric Benhamou; Beatrice Guez
  4. Potential benefits of optimal intra-day electricity hedging for the environment : the perspective of electricity retailers By Raphaël Boroumand; Stéphane Goutte; Thomas Porcher
  5. The Valuation of Credit Default Swap with Counterparty Risk and Collateralization By Tim Xiao
  6. Power generation portfolios: A parametric formulation of the efficient frontier By Juárez-Luna, David
  7. Mathematical Analysis of Dynamic Risk Default in Microfinance By Mohammed Kaicer; Abdelilah Kaddar
  8. The impact of a higher leverage ratio on the South African economy By Davies Rob; Makrelov Konstantin; Harris Laurence
  9. The likelihood of divorce and the riskiness of financial decisions By Stark, Oded; Szczygielski, Krzysztof
  10. Predicting Bitcoin Returns: Comparing the Roles of Newspaper- and Internet Search-Based Measures of Uncertainty By Elie Bouri; Rangan Gupta
  11. Distributions of Historic Market Data -- Relaxation and Correlations By M. Dashti Moghaddam; Zhiyuan Liu; R. A. Serota
  12. Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches By Yuqing Zhang; Neil Walton
  13. Maximum likelihood estimation of score-driven models with dynamic shape parameters : an application to Monte Carlo value-at-risk By Escribano, Álvaro; Blazsek, Szabolcs; Ayala, Astrid
  14. Is the financial system sufficiently resilient: a research programme and policy agenda By Paul Tucker

  1. By: Torsten Heinrich; Juan Sabuco; J. Doyne Farmer
    Abstract: We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors, and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric, long tailed firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry for testing policies and strategies for better capital management.
    Date: 2019–07
  2. By: Zhao Zhao; Olivier Ledoit; Hui Jiang
    Abstract: Two basic solutions have been proposed to fix the well-documented incompatibility of the sample covariance matrix with Markowitz mean-variance portfolio optimization: first, restrict leverage so much that no short sales are allowed; or, second, linearly shrink the sample covariance matrix towards a parsimonious target. Mathematically, there is a deep connection between the two approaches, and empirically they display similar performances. Recent developments have turned the choice between no-short-sales and linear shrinkage into a false ’either-or’ dichotomy. What if, instead of 0% leverage we considered fully-invested, long-short 130/30 portfolios, or even 150/50, given that prime brokers, fund regulators and investors have started to allow it? And instead of linearly shrinking the unconditional covariance matrix, what if we allowed for each of the eigenvalues of the sample covariance matrix to have its own shrinkage intensity, optimally determined under large-dimensional asymptotics, while also incorporating Multivariate GARCH effects? Our empirical evidence finds that, indeed, these new developments enable us to have ‘the best of both worlds’ by combining some appropriate leverage constraint with a judiciously chosen shrinkage method. The overall winner is a 150/50 investment strategy where the covariance matrix estimator is a combination of DCC (Dynamic Conditional Correlation — a well-known Multivariate GARCH model) — with NL (Non-Linear shrinkage, a substantial upgrade upon linear shrinkage technology); although 130/30 DCC-NL comes a close second. This is true both in the ‘pure’ case of estimating the Global Minimum Variance portfolio, and also for textbook-style construction of Markowitz mean-variance efficient portfolio.
    Keywords: DCC, nonlinear shrinkage, leverage constraints, large portfolios, risk reduction, Markowitz mean-variance efficiency, multivariate GARCH
    JEL: C13 C58 G11
    Date: 2019–07
  3. By: Eric Benhamou (MILES - LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique, LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique); Beatrice Guez
    Abstract: We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This allows understanding the drivers of these performance ratios as well as deriving a condition for a new asset to provide incremental performance for the portfolio. We provide various numerical examples of this performance ratio decomposition. JEL classification: C12, G11.
    Keywords: Treynor,recovery,Sharpe,incremental Sharpe ratio,portfolio diversification,MILES,LAMSADE
    Date: 2018
  4. By: Raphaël Boroumand (PSB - Paris School of Business); Stéphane Goutte (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis); Thomas Porcher (ESG Research Lab - ESG Management School)
    Abstract: Our article provides a better understanding of risk management strategies for all energy market stakeholders. A good knowledge of optimal risk hedging strategies is not only important for energy companies but also for regulators and policy makers in a context of climate emergency. Indeed, the electricity sector is key to achieve energy and ecological transition. Electricity companies should be on frontline of climate change struggle. Taking the perspective of electricity retailers, we analyze a range of portfolios made of forward contracts and/or power plants for specific hourly clusters based on electricity market data from the integrated German-Austrian spot market. We prove that intra-day hedging with forward contracts is sub-optimal compared to financial options and physical assets. By demonstrating the contribution of intra-day hedging with options and physical assets, we highlight the specificities of electricity markets as hourly markets with strong volatility during peak hours. By simulating optimal hedging strategies, our article proposes a range of new portfolios for electricity retailers to manage their risks and reduce their sourcing costs. A lower hedging cost enables to allocate more resources to digitalization and energy efficiency services to take into account customers' expectations for more climate-friendly retailers. This is a virtuous circle. Retailers provide high value-added energy efficiency services so that consumers consume less. The latter contributes to reach electricity reduction targets to fight climate warming.
    Keywords: Diversification,Climate,Electricity,Risk,Intra-day,Hedging
    Date: 2019–07–05
  5. By: Tim Xiao (University of Toronto)
    Abstract: This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market.
    Keywords: valuation model,credit risk modeling,collateralization,correlation,CDS 1
    Date: 2019–07–05
  6. By: Juárez-Luna, David
    Abstract: The Portfolio Theory has been extensively used as a planning tool for power generation diversification. However, no one of the existing papers provide a detailed explanation on how the efficient frontier of the Power Generation Portfolio (PGP) is costructed. We provide a parametric formulation of the efficient frontier of PGP of up to 5 technologies. The analysys takes advantages of the fact that the risk of the PGP is a convex function of the shares of the different technologies. The parametric formulation of the efficient frontier of the PGP constitutes a powerfull policy tool for power generation policy-makers.
    Keywords: Portfolio, Power Generation, Efficient Frontier, Risk, NPV.
    JEL: D81 G11 Q40 Q49
    Date: 2019–07–02
  7. By: Mohammed Kaicer; Abdelilah Kaddar
    Abstract: In this work we will develop a new approach to solve the non repayment problem in microfinance due to the problem of asymmetric information. This approach is based on modeling and simulation of ordinary differential systems where time remains a primordial component, they thus enable microfinance institutions to manage their risk portfolios by a prediction of numbers of solvent and insolvent borrowers ever a period, in order to define or redefine its development strategy, investment and management in an area, where the population is often poor and in need a mechanism of financial inclusion.
    Date: 2019–07
  8. By: Davies Rob; Makrelov Konstantin; Harris Laurence
    Abstract: We employ a micro-founded and stock-and-flow-consistent model to study the impact of a higher leverage ratio on the South African economy. The model provides a rich representation of institutional balance sheets.The relationship between bank capital, risk-taking behaviour, lending spreads, and economic activity is highlighted. The financial accelerator mechanism operates through the balance sheets of all economic institutions. The introduction of a higher leverage ratio is likely to generate negative economic impacts in the short run. The negative gross domestic product effect is greatest if the financial sector reduces leverage by reducing the value of assets.The regulatory shock leads the sector to change its perception of risk, reducing the size of the money multiplier and increasing lending spreads. Higher regulatory requirements also affect the transmission of monetary policy. Effective monetary policy requires understanding how the financial sector is likely to meet new requirements and change its perceptions of risk.
    Keywords: Computable general equilibrium,Financial dynamics,leverage ratio,Stock and flow,Risk management
    Date: 2019
  9. By: Stark, Oded; Szczygielski, Krzysztof
    Abstract: We link causally the riskiness of men's management of their finances with the probability of their experiencing a divorce. Our point of departure is that when comparing single men to married men, the former manage their finances in a more aggressive (that is, riskier) manner. Assuming that single men believe that low relative wealth has a negative effect on their standing in the marriage market and that they care about their standing in that market more than married men do, we find that a stronger distaste for low relative wealth translates into reduced relative risk aversion and, consequently, into riskier financial behavior. With this relationship in place we show how this difference varies depending on the "background" likelihood of divorce and, hence, on the likelihood of re-entry into the marriage market: married men in environments that are more prone to divorce exhibit risk-taking behavior that is more similar to that of single men than married men in environments that are little prone to divorce. We offer a theoretical contribution that helps inform and interpret empirical observations and regularities and can serve as a guide for follow-up empirical work, having established and identified the direction of causality.
    Keywords: Men's preferences towards risk,Risk-taking behavior,Concern at having low relative wealth,Relative and absolute risk aversion,Marital-based difference in attitudes towards risk,Likelihood of divorce
    JEL: D21 D81 G32
    Date: 2019
  10. By: Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: We compare the ability of two measures of uncertainty, a newspaper-based measure and an internet search-based measure, to predict Bitcoin returns. Using monthly data from July 2010 to May 2019 and a predictive regression model characterized by a heteroskedastic error structure and, we show that Bitcoin is a hedge against both measures. However, the predictive content of the internet-derived uncertainty related queries measure is statistically stronger than the measure of uncertainty based on newspapers for predicting Bitcoin returns, which is possibly due to the fact that the measure of uncertainty is now directly obtained from individual investors via internet searches.
    Keywords: Bitcoin, Hedging, Predictability, Economic Uncertainty
    JEL: C32 G12
    Date: 2019–07
  11. By: M. Dashti Moghaddam; Zhiyuan Liu; R. A. Serota
    Abstract: We show that, for a class of mean-reverting models, the correlation function of stochastic variance (squared volatility) contains only one -- relaxation -- parameter. We generalize and simplify the expression for leverage for this class of models. We apply our results to specific examples of such models -- multiplicative, Heston, and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance.
    Date: 2019–07
  12. By: Yuqing Zhang; Neil Walton
    Abstract: We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss caused by not using the optimal price. As is commonplace in insurance, we model demand and claims by GLMs. In our adaptive GLM design, we use the maximum quasi-likelihood estimation (MQLE) to estimate the unknown parameters. We show that, if prices are chosen with suitably decreasing variability, the MQLE parameters eventually exist and converge to the correct values, which in turn implies that the sequence of chosen prices will also converge to the optimal price. In the adaptive GP regression model, we sample demand and claims from Gaussian Processes and then choose selling prices by the upper confidence bound rule. We also analyze these GLM and GP pricing algorithms with delayed claims. Although similar results exist in other domains, this is among the first works to consider dynamic pricing problems in the field of insurance. We also believe this is the first work to consider Gaussian Process regression in the context of insurance pricing. These initial findings suggest that online machine learning algorithms could be a fruitful area of future investigation and application in insurance.
    Date: 2019–07
  13. By: Escribano, Álvaro; Blazsek, Szabolcs; Ayala, Astrid
    Abstract: Dynamic conditional score (DCS) models with time-varying shape parameters provide a exible method for volatility measurement. The new models are estimated by using the maximum likelihood (ML) method, conditions of consistency and asymptotic normality of ML are presented, and Monte Carlo simulation experiments are used to study the precision of ML. Daily data from the Standard & Poor's 500 (S&P 500) for the period of 1950 to 2017 are used. The performances of DCS models with constant and dynamic shape parameters are compared. In-sample statistical performance metrics and out-of-sample value-at-risk backtesting support the use of DCS models with dynamic shape.
    Keywords: Outliers; Value-At-Risk; Score-Driven Shape Parameters; Dynamic Conditional Score Models
    JEL: C58 C52 C22
    Date: 2019–07–19
  14. By: Paul Tucker
    Abstract: The paper discusses why the financial system is not as resilient as policymakers currently claim - despite extensive regulatory reforms from a very weak starting point.The paper discusses different policy strategies for making some of the debt of some banks "information-insensitive", so that they it would be treated as safe in all but the most stressed circumstances. For the current prudential strategy, which is centred on minimum equity requirements, the paper argues that central banks and other agencies should start publishing annual staff reports on where regulatory and supervisory policy has been surreptitiously tightened or loosened.The paper aims to spark and contribute to the debate on the second phase of stability reforms that will be needed. It sets out an alternative policy strategy based on 100% liquidity cover for the short-term debt of banks (and shadow banks), and for the creditor hierarchy of operating banks and holding companies. In this proposal, the haircut policy of central banks would become the key instrument in determining bank equity requirements and the terms on which they could borrow in secured money markets. As such, this strategy would operationalise the theoretical and empirical work of Bengt Holmström and Gary Gorton.
    Keywords: regulatory reforms, Basel III, great financial crisis
    JEL: E44 E58 G28
    Date: 2019–07

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