nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒10‒12
twenty-six papers chosen by
Stan Miles
Thompson Rivers University

  1. A pandemic business interruption insurance By Alexis Louaas; Pierre Picard
  2. Self-insurance and Non-concave Distortion Risk Measures By Sarah Bensalem
  3. Dispersion estimation; Earnings risk; Censoring; Quantile regression; Occupational choice; Sorting; Risk preferences; SOEP; IABS By Daniel Pollmann; Thomas Dohmen; Franz Palm
  4. Adaptive Interest Rate Modelling By Mengmeng Guo; Wolfgang Karl H\"ardle
  5. Meta-learning approaches for recovery rate prediction By Gambetti, Paolo; Roccazzella, Francesco; Vrins, Frédéric
  6. Mean-variance portfolio selection with tracking error penalization By Willliam Lefebvre; Gregoire Loeper; Huyên Pham
  7. Forecasting recovery rates on non-performing loans with machine learning By Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
  8. Investors' Uncertainty and Forecasting Stock Market Volatility By Ruipeng Liu; Rangan Gupta
  9. Affine term-structure models: A time-changed approach with perfect fit to market curves By Mbaye, Cheikh; Vrins, Frédéric
  10. Predictability and the cross-section of expected returns: A challenge for asset pricing models By Schlag, Christian; Semenischev, Michael; Thimme, Julian
  11. Disastrous Defaults By Gouriéroux Christian; Monfort Alain; Mouabbi Sarah; Renne Jean-Paul
  12. Did Too-Big-To-Fail Reforms Work Globally? By Asani Sarkar
  13. Minimum Rényi entropy portfolios By Lassance, Nathan; Vrins, Frédéric
  14. Forecasting the Old-Age Dependency Ratio to Determine a Sustainable Pension Age By Rob J Hyndman; Yijun Zeng; Han Lin Shang
  15. How does the Coastal Housing Market View Flood zone-A Risk Signal or Mandatory Costs? By Chen, Zhenshan
  16. Reinsurance of multiple risks with generic dependence structures By Manuel Guerra; Alexandra B. Moura
  17. The Scope of Risk Pooling By Putman, Daniel S.
  18. A first econometric analysis of the CRIX family By Shi Chen; Cathy Yi-Hsuan Chen; Wolfgang Karl H\"ardle
  19. Climate Sin Stocks: Stock Price Reactions to Global Climate Strikes By Ramelli, Stefano; Ossola, Elisa; Rancan, Michela
  20. How People Know Their Risk Preference By Arslan, Ruben C.; Brümmer, Martin; Dohmen, Thomas; Drewelies, Johanna; Hertwig, Ralph; Wagner, Gert G.
  21. Auctioning C02 Emission Allowances in Europe. A Time Series Analysis of Equilibrium Prices By Bruno Bosco
  22. Practical Option Valuations of Futures Contracts with Negative Underlying Prices By Anatoliy Swishchuk; Ana Roldan-Contreras; Elham Soufiani; Guillermo Martinez; Mohsen Seifi; Nishant Agrawal; Yao Yao
  23. Time-Varying Spillovers between Housing Sentiment and Housing Market in the United States By Christophe Andre; David Gabauer; Rangan Gupta
  24. Economics of Bushfire-Risk Mitigation By Pannell, David; Florec , Veronique; Dempster, Fiona
  25. The Currency Composition of International Portfolio Assets By Vahagn Galstyan; Caroline Mehigan; Rogelio V. Mercado, Jr.
  26. Redrawing of a Housing Market: Insurance Payouts and Housing Market Recovery in the Wake of the Christchurch Earthquake of 2011 By Cuong Nguyen; Ilan Noy; Dag Einar Sommervoll; Fang Yao

  1. By: Alexis Louaas (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique); Pierre Picard (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique - ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique, X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique)
    Abstract: We analyze how pandemic business interruption coverage can be put in place by building on capitalization mechanisms. The pandemic risk cannot be mutualized since it affects simultaneously a large number of businesses, and furthermore, it has a systemic nature because it goes along with a severe decline in the real economy. However, as shown by COVID-19, pandemics affect economic sectors in a differentiated way: some of them are very severely affected because their activity is strongly impacted by travel bans and constraints on work organisation, while others are more resistant. This opens the door to risk coverage mechanisms based on a portfolio of financial securities, including long-short positions and options in stock markets. We show that such financial investment allow insurers to offer business interruption coverage in pandemic states, while simultaneously hedging the risks associated with the alternation of bullish and bearish non-pandemic states. These conclusions are derived from a theoretical model of corporate risk management, and they are illustrated by numerical simulations, using data from the French stock exchange.
    Keywords: pandemic,business interruption,insurance,risk management
    Date: 2020–09–17
  2. By: Sarah Bensalem (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Abstract: This article considers an optimization problem for an insurance buyer in the context of proportional insurance and furnishing effort to reduce the size of a potential loss. The buyer's risk preference is given by a distortion risk measure, with risk probabilities being evaluated via a non-concave distortion function. This kind of distortion function reflects potential cognitive biases in the way in which individuals perceive risk probabilities. The buyer will select the optimal level of both insurance coverage and the prevention effort that he/she will furnish to reduce the amount that he/she stands to lose. The distribution of losses is given by a family of stochastically-ordered probability measures, indexed by the prevention effort. Contrary to what is found in the standard economic literature, introducing a non-concave distortion function leads to indeterminacy in the relationship between market insurance and self-insurance. Self-insurance and market insurance may be either substitutes, with a rise in one producing a fall in the other, or complements, so that the two rise or fall together, depending on the price elasticity.
    Keywords: Prevention,Self-insurance,Distortion risk measures,Distortion function,Non-concave distortion,Cognitive bias
    Date: 2020–09–04
  3. By: Daniel Pollmann (Department of Economics, Harvard University); Thomas Dohmen (University of Bonn, Maastricht University, IZA, DIW and CESifo); Franz Palm (Maastricht University)
    Abstract: We present a semiparametric method to estimate group-level dispersion, which is particularly effective in the presence of censored data. We apply this procedure to obtain measures of occupation-specific wage dispersion using top-coded administrative wage data from the German IAB Employment Sample (IABS). We then relate these robust measures of earnings risk to the risk attitudes of individuals working in these occupations. We find that willingness to take risk is positively correlated with the wage dispersion of an individual's occupation.
    Keywords: Dispersion estimation; Earnings risk; Censoring; Quantile regression; Occupational choice; Sorting; Risk preferences; SOEP; IABS
    JEL: C14 C21 C24 J24 J31 D01 D81
    Date: 2020–09
  4. By: Mengmeng Guo; Wolfgang Karl H\"ardle
    Abstract: A standard quantitative method to access credit risk employs a factor model based on joint multivariate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate into the conditional factor loading, and model them by sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously and creates their association implicitly. In accordance with Basel III, this paper shows that the tendency of default is more governed by systematic risk rather than idiosyncratic risk during a hectic period. Among the models considered, the one with random factor loading and a state-dependent recovery rate turns out to be the most superior on the default prediction.
    Date: 2020–09
  5. By: Gambetti, Paolo; Roccazzella, Francesco; Vrins, Frédéric
    Keywords: machine learning ; forecasts combination ; loss given default ; credit risk ; model risk
    Date: 2020–01–01
  6. By: Willliam Lefebvre (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Gregoire Loeper (Monash University [Melbourne], BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, ENSAE ParisTech - École Nationale de la Statistique et de l'Administration Économique)
    Abstract: This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a refe\-rence portfolio with same wealth and fixed weights. Such consideration is motivated as fo\-llows: (i) On the one hand, it is a way to robustify the mean-variance allocation in case of misspecified parameters, by ``fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean-Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.
    Keywords: robustified alloca- tion,parameter misspecification *,parameter misspecification.,robustified allocation,tracking error,Continuous-time mean-variance problem
    Date: 2020–09–17
  7. By: Bellotti, Anthony; Brigo, Damiano; Gambetti, Paolo; Vrins, Frédéric
    Keywords: loss given default ; credit risk ; defaulted loans ; debt collection ; superior set of models
    Date: 2020–01–01
  8. By: Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper examines if incorporating investors' uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors' uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model.
    Keywords: Investors' uncertainty, Stock market risk, MSM, Volatility forecasting
    Date: 2020–09
  9. By: Mbaye, Cheikh; Vrins, Frédéric
    Keywords: model calibration ; credit risk ; stochastic intensity ; jump-diffusions ; term-structure models ; time-change techniques
    Date: 2019–01–01
  10. By: Schlag, Christian; Semenischev, Michael; Thimme, Julian
    Abstract: Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples.
    Keywords: asset pricing,cross-section of stock returns,predictability
    JEL: G12 E44 D81
    Date: 2020
  11. By: Gouriéroux Christian; Monfort Alain; Mouabbi Sarah; Renne Jean-Paul
    Abstract: We define a disastrous default as the default of a systemic entity. Such an event is expected to have a negative effect on the economy and to be contagious. Bringing macroeconomic structure to a no-arbitrage asset-pricing framework, we exploit prices of disaster-exposed assets (credit and equity derivatives) to extract information on the expected (i) influence of a disastrous default on consumption and (ii) probability of a financial meltdown. We find that the returns of disaster-exposed assets are consistent with a systemic default being followed by a 3% decrease in consumption. The recessionary influence of disastrous defaults implies that financial instruments whose payoffs are exposed to such credit events carry substantial risk premiums. We also produce systemic risk indicators based on the probability of observing a certain number of systemic defaults or a sharp drop of consumption.
    Keywords: Disaster Risk, Systemic Entities, Default Dependencies, Credit Derivatives, Equilibrium Model.
    JEL: E43 E44 E47 G01 G12
    Date: 2020
  12. By: Asani Sarkar
    Abstract: Once a bank grows beyond a certain size or becomes too complex and interconnected, investors often perceive that it is “too big to fail” (TBTF), meaning that if the bank were to fail, the government would likely bail it out. Following the global financial crisis (GFC) of 2008, the G20 countries agreed on a set of reforms to eliminate the perception of TBTF, as part of a broader package to enhance financial stability. In June 2020, the Financial Stability Board (FSB), a sixty-eight-member international advisory body set up in 2009, published the results of a year-long evaluation of the effectiveness of TBTF reforms. In this post, we discuss the main conclusions of the report—in particular, the finding that implicit funding subsidies to global banks have decreased since the implementation of reforms but remain at levels comparable to the pre-crisis period.
    Keywords: too-big-to-fail; global banks; systemic risk; Financial Stability Board
    JEL: G32 G21
    Date: 2020–09–30
  13. By: Lassance, Nathan; Vrins, Frédéric
    Keywords: portfolio selection ; Shannon entropy ; Rényi entropy ; risk measure ; information theory
    Date: 2019–01–01
  14. By: Rob J Hyndman; Yijun Zeng; Han Lin Shang
    Abstract: We forecast the old-age dependency ratio for Australia under various pension age proposals, and estimate a pension age scheme that will provide a stable old-age dependency ratio at a specified level. Our approach involves a stochastic population forecasting method based on coherent functional data models for mortality, fertility and net migration, which we use to simulate the future age-structure of the population. Our results suggest that the Australian pension age should be increased to 68 by 2030, 69 by 2036, and 70 by 2050, in order to maintain the old-age dependency ratio at 23%, just above the 2018 level. Our general approach can easily be extended to other target levels of the old-aged dependency ratio and to other countries.
    Keywords: coherent forecasts, demographic components, functional time series, pension age
    JEL: J11 J14 C22
    Date: 2020
  15. By: Chen, Zhenshan
    Keywords: Resource/Energy Economics and Policy, Risk and Uncertainty
    Date: 2020–07
  16. By: Manuel Guerra; Alexandra B. Moura
    Abstract: We consider the optimal reinsurance problem from the point of view of a direct insurer owning several dependent risks, assuming a maximal expected utility criterion and independent negotiation of reinsurance for each risk. Without any particular hypothesis on the dependency structure, we show that optimal treaties exist in a class of independent randomized contracts. We derive optimality conditions and show that under mild assumptions the optimal contracts are of classical (non-randomized) type. A specific form of the optimality conditions applies in that case. We illustrate the results with some numerical examples.
    Date: 2020–09
  17. By: Putman, Daniel S.
    Keywords: Risk and Uncertainty, International Development, Institutional and Behavioral Economics
    Date: 2020–07
  18. By: Shi Chen; Cathy Yi-Hsuan Chen; Wolfgang Karl H\"ardle
    Abstract: In order to price contingent claims one needs to first understand the dynamics of these indices. Here we provide a first econometric analysis of the CRIX family within a time-series framework. The key steps of our analysis include model selection, estimation and testing. Linear dependence is removed by an ARIMA model, the diagnostic checking resulted in an ARIMA(2,0,2) model for the available sample period from Aug 1st, 2014 to April 6th, 2016. The model residuals showed the well known phenomenon of volatility clustering. Therefore a further refinement lead us to an ARIMA(2,0,2)-t-GARCH(1,1) process. This specification conveniently takes care of fat-tail properties that are typical for financial markets. The multivariate GARCH models are implemented on the CRIX index family to explore the interaction.
    Date: 2020–09
  19. By: Ramelli, Stefano (University of Zurich); Ossola, Elisa (European Commission); Rancan, Michela (Universita Politecnica delle Marche)
    Abstract: The First Global Climate Strike on March 15, 2019 has represented a historical turn in climate activism. We investigate the cross-section of European stock price reactions to this event. Looking at a large sample of European firms, we find that the unanticipated success of this event caused a substantial stock price reaction on high-carbon intensity companies. These findings are likely driven by an update of investors' beliefs about the level of environmental social norms in the economy and the anticipation of future developments of climate regulation.
    Keywords: climate risks, stock returns, event study, environmental preferences, sustainable finance, investor attention
    JEL: Q01 G14 G23
    Date: 2020–06
  20. By: Arslan, Ruben C. (Max Planck Institute for Human Development); Brümmer, Martin (University of Leipzig); Dohmen, Thomas (University of Bonn and IZA); Drewelies, Johanna (Humboldt University Berlin); Hertwig, Ralph (Max Planck Institute for Human Development); Wagner, Gert G. (Max Planck Institute for Human Development)
    Abstract: People differ in their willingness to take risks. Recent work found that revealed preference tasks (e.g., laboratory lotteries)—a dominant class of measures—are outperformed by survey-based stated preferences, which are more stable and predict real-world risk taking across different domains. How can stated preferences, often criticised as inconsequential "cheap talk," be more valid and predictive than controlled, incentivized lotteries? In our multimethod study, over 3,000 respondents from population samples answered a single widely used and predictive risk-preference question. Respondents then explained the reasoning behind their answer. They tended to recount diagnostic behaviours and experiences, focusing on voluntary, consequential acts and experiences from which they seemed to infer their risk preference. We found that third- party readers of respondents' brief memories and explanations reached similar inferences about respondents' preferences, indicating the intersubjective validity of this information. Our results help unpack the self perception behind stated risk preferences that permits people to draw upon their own understanding of what constitutes diagnostic behaviours and experiences, as revealed in high-stakes situations in the real world.
    Keywords: risk preferences, self-report, self-perception
    JEL: D80 D81 D91 D01
    Date: 2020–09
  21. By: Bruno Bosco
    Abstract: The purpose of this paper is to offer an analysis of the price behavior of Phase III (2013–2020) EU- ETS emission allowances of CO2, by focusing on the dynamics of daily auction equilibrium prices and on the changes of the volatility of the underlying stochastic process. The paper initially investigates the characteristics of equilibrium prices as they result from auction rules and bidders' behavior and uses them as a theoretical basis of the statistical hypothesis–common to the empirical literature active in this field– of a changing conditional variance of prices. Then, different versions of a GARCH model are employed to estimate both mean and variance equations of price dynamics and to evaluate what factors affect price volatility, recorded excess supply, and bidders’ surplus. Brief policy considerations are also offered.
    Keywords: EU-ETS emission auctions; Equilibrium prices volatility; GARCH
    Date: 2020–06
  22. By: Anatoliy Swishchuk; Ana Roldan-Contreras; Elham Soufiani; Guillermo Martinez; Mohsen Seifi; Nishant Agrawal; Yao Yao
    Abstract: Here we propose two alternatives to Black 76 to value European option future contracts in which the underlying market prices can be negative or mean reverting. The two proposed models are Ornstein-Uhlenbeck (OU) and continuous time GARCH (generalized autoregressive conditionally heteroscedastic). We then analyse the values and compare them with Black 76, the most commonly used model, when the underlying market prices are positive
    Date: 2020–09
  23. By: Christophe Andre (Economics Department, Organisation for Economic Co-operation and Development (OECD), Paris, France); David Gabauer (Data Analysis Systems, Software Competence Center Hagenberg, Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: This paper investigates spillovers between the housing sentiment index of Bork et al. (2020), common factors in US real housing returns and their volatility (derived from a time-varying dynamic factor model with stochastic volatility), GDP growth and real interest rates, using the time-varying parameter vector autoregressive version of the Diebold and Yilmaz (2012, 2014) methodology. We find that in contrast to spillovers from the common factor in housing returns, reverse spillovers are relatively weak. Net spillovers from the common factor of housing returns to housing sentiment and GDP increase durably after the Global Financial Crisis. This suggests that, while a shock to housing prices is likely to have a significant impact on housing sentiment and the economy, a purely exogenous shock to housing sentiment, may in itself have little impact on housing returns and volatility.
    Keywords: Common Housing Market Movements, Sentiment, Time-Varying Spillovers
    JEL: C32 R31
    Date: 2020–09
  24. By: Pannell, David; Florec , Veronique; Dempster, Fiona
    Abstract: Wildfires in Australia regularly cause major losses of life, property, water resources, wildlife and habitat. Various mitigation options exists and are applied to varying degrees across Australia, including : prescribed burning, land-use planning, retrofitting houses to reduce their flammability, and fire breaks. We have undertaken economic analyses of these options for diverse case studies in four Australian states and New Zealand. Fire economics is about trade-offs between various costs (of risk mitigation, fire suppression, and losses due to fire). Prescribed burning (PB) is often worth doing but it is not a panacea. The big costs are from catastrophic fires, but PB makes little difference to them. Even so, the main benefits of PB can be from small effects on catastrophic fires. The optimal area of prescribed burning is not clear-cut but around 5% per year is indicated in several of our case studies. Fire risk is highly sensitive to weather so climate change is relevant. Land-use planning to keep assets out of the highest risk areas has been under-emphasised in the current debate. Retrofitting has costs well in excess of benefits in two cases studies.
    Keywords: Risk and Uncertainty
    Date: 2020–09–16
  25. By: Vahagn Galstyan (Trinity College Dublin); Caroline Mehigan (Organisation for Economic Co-Operation and Development); Rogelio V. Mercado, Jr. (The SEACEN Centre)
    Abstract: In this paper, we empirically assess the importance of gravity-type variables and measures of macroeconomic and financial volatilities in explaining portfolio holdings denominated across the main global currencies: US dollar (USD), euro (EUR), Pound sterling (GBP), Japanese yen (JPY) and Swiss franc (CHF). Our findings underscore the importance of trade ties and membership of the euro area. We also find that international positions co-move with the level of macroeconomic and financial uncertainty. Importantly, we identify heterogeneous patterns at a currency level.
    Keywords: Currency Composition, International Portfolio Assets, Trade, Volatility
    JEL: F31 F36 F41 G15
    Date: 2019–01
  26. By: Cuong Nguyen; Ilan Noy; Dag Einar Sommervoll; Fang Yao
    Abstract: On the 22nd of February 2011, much of the residential housing stock in the city of Christchurch, New Zealand, was damaged by an unusually destructive earthquake. Almost all of the houses were insured. We ask whether insurance was able to mitigate the damage adequately, or whether the damage from the earthquake, and the associated insurance payments, led to a spatial re-ordering of the housing market in the city. We find a negative correlation between insurance pay-outs and house prices at the local level. We also uncover evidence that suggests that the mechanism behind this result is that in some cases houses were not fixed (i.e., owners having pocketed the payments) - indeed, insurance claims that were actively repaired (rather than paid directly) did not lead to any relative deterioration in prices. We use a genetic machine-learning algorithm which aims to improve on a standard hedonic model, and identify the dynamics of the housing market in the city, and three data sets: All housing market transactions, all earthquake insurance claims submitted to the public insurer, and all of the local authority’s building-consents data. Our results are important not only because the utility of catastrophe insurance is often questioned, but also because understanding what happens to property markets after disasters should be part of the overall assessment of the impact of the disaster itself. Without a quantification of these impacts, it is difficult to design policies that will optimally try to prevent or ameliorate disaster impacts.
    Keywords: house price prediction, machine learning, genetic algorithm, spatial aggregation
    JEL: G22 Q54 R11 R31
    Date: 2020

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