nep-rmg New Economics Papers
on Risk Management
Issue of 2018‒04‒09
twenty papers chosen by

  1. Modeling Longevity Risk using Consistent Dynamics Affine Mortality Models By Rihab Bedoui; Islem Kedidi
  2. When gambling for resurrection is too risky By Kirti, Divya
  3. Lapse tables for lapse risk management in insurance: a competing risk approach By Xavier Milhaud; Christophe Dutang
  4. Brexit and CDS spillovers across UK and Europe By Jamal Bouoiyour; Refk Selmi
  5. Spatial risk measures and rate of spatial diversification By Erwan Koch
  6. Oil volatility, oil and gas firms and portfolio diversification By Nikolaos Antonakakis; Juncal Cunado; George Filis; David Gabauer; Fernando Perez de Gracia
  7. Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk Modeling By Luca Spadafora; Francesca Sivero; Nicola Picchiotti
  8. Multi-factor approximation of rough volatility models By Eduardo Abi Jaber; Omar El Euch
  9. The credit portfolio management by the econometric models: A theoretical analysis By Abdelkader Derbali
  10. The variance risk premium and capital structure By Lotfaliei, Babak
  11. The Term Structure of the Price of Variance Risk By Yichuan Wang; Thomas Eisenbach; Martin Schmalz; Marianne Andries
  12. Credit Risk Analysis using Machine and Deep learning models By Peter Martey Addo; Dominique Guegan; Bertrand Hassani
  13. How the default probability is defined by the CreditRisk+ model? By Abdelkader Derbali
  14. Exchange rate appreciations and corporate risk taking By Sebnem Kalemli-Ozcan; Xiaoxi Liu; Ilhyock Shim
  15. The credit portfolio management by structural models: A theoretical analysis By Abdelkader Derbali
  16. Pricing Credit Default Swap Subject to Counterparty Risk and Collateralization By Alan White
  17. Agent-based model of system-wide implications of funding risk By Hałaj, Grzegorz
  18. House Price Markups and Mortgage Defaults By Paul E. Carrillo; William M. Doerner; William D. Larson
  19. Dynamic Equicorrelation between S&P500 Index and S&P GSCI By Abdelkader Derbali; Tarek Chebbi
  20. Flight to Safety from European Stock Markets By Aslanidis, Nektarios,; Christiansen, Charlotte

  1. By: Rihab Bedoui (IHEC Sousse - IHEC); Islem Kedidi (LAREMFIQ - Laboratory Research for Economy, Management and Quantitative Finance - Institut des Hautes Etudes Commerciales (Université de Sousse))
    Abstract: Longevity Risk becomes an important challenge in the recent Year because of the decreases in the mortality rates and the rising in the life expectancy through the decades. In this article, we propose a consistent multi-factor dynamics affine mortality model to the longevity risk model-ing, we show that this model is an appropriate model to fit the historical mortality rates.To our Knowledge this is the first work that uses a consistent Mortality models to model USA Longevity risk.Indeed the multiple risk factors permitting applications not only to the hedge and price of the longevity risk but also in mortality derivatives and the general problems in the risk management .A state space presentation is used to estimate the model parameters through the kalman filter .To capture the effect of the size of the population sample we include a measurement error variance for each age. We evaluate 2-and 3-factor implementation of the model through the use of the USA mortality data, we employ Bootstrapping method to derive parameter estimated and the Consistent models prove the performance and the stability of the model. We show that the 3-factor independent model is the best model that can provide a better fit to our survivals curves and especially for the elderly persons
    Keywords: consistent,multi-factor,Mortality model,Longevity Risk,Affine,Kalman filter
    Date: 2018–01–08
  2. By: Kirti, Divya
    Abstract: Rather than taking on more risk, US insurers hit hard by the crisis pulled back from risk taking, relative to insurers hit less hard by the crisis. Capital requirements alone do not explain this risk reduction: insurers hit hard reduced risk within assets with identical regulatory treatment. State level US insurance regulation makes it unlikely this risk reduction was driven by moral suasion. Other financial institutions also reduce risk after large shocks: the same approach applied to banks yields similar results. My results suggest that, at least in some circumstances, franchise value can dominate, making gambling for resurrection too risky. JEL Classification: G22, G21, G32, G28
    Keywords: banking, financial frictions, franchise value, life insurance, risk shifting
    Date: 2018–02
  3. By: Xavier Milhaud (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Christophe Dutang (LMM - Laboratoire Manceau de Mathématiques - UM - Le Mans Université)
    Abstract: This paper deals with the crucial problem of modeling policyholders' behaviours in life insurance. We focus here on the surrender behaviours and model the contract lifetime through the use of survival regression models. Standard models fail at giving acceptable forecasts for the timing of surrenders because of too much heterogeneity, whereas the competing risk framework provides interesting insights and more accurate predictions. Numerical results follow from using Fine & Gray model ([13]) on an insurance portfolio embedding Whole Life contracts. Through backtests, this framework reveals to be quite efficient and recovers the empirical lapse rate trajectory by aggregating individual predicted lifetimes. These results could be particularly useful to design future insurance product. Moreover, this setting allows to calibrate experimental lapse tables, simplifying the lapse risk management for operational teams.
    Keywords: life insurance,lifetime,surrender,lapse,competing risks,cumulative incidence functions
    Date: 2018
  4. By: Jamal Bouoiyour (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour); Refk Selmi (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour)
    Abstract: Understanding the transmission process between markets is critical for risk management and economic policy. The objective of this paper is twofold. First, it identifies when UK and European (France, Germany, Italy and Spain) Credit Default Swaps (CDSs) exhibit explosivity with respect to their past behaviors. Second, it quantifies the dynamics of CDS volatility spillover effects surrounding the UK's EU membership referendum commonly known as " Brexit ". Using a recursive identification algorithm and new spillover measures suggested by Diebold and Yilmaz (2012), quite interesting results were drawn. We detect significant build-ups in CDS prices for all countries under study soon after the day relative to the announcement of Brexit. Besides, we show that the great uncertainty over Brexit generates significant volatility spillovers across the underlined CDS. In particular, we find that UK, Italy and Spain are the " net volatility transmitters " , while France and Germany seem the " net volatility receivers ". Such information can help policy makers in undertaking decoupling policies to (1) insulate the economy from risk spillovers effects, (2) lighten the spread of the damage done by Brexit and (3) preserve the stability of financial system. To attenuate the risk transmission across CDS markets over Brexit, regulators can, for example, put forth preventive strategies by foregrounding the most influential volatility senders (UK, Italy and Spain).
    Keywords: Brexit,Credit Default Swaps,Explosivity,Volatility spillover effects,UK,Europe
    Date: 2018–03–17
  5. By: Erwan Koch
    Abstract: An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms which are well suited to analyse the risk due to events having a spatial extent, precisely such as environmental phenomena. The axiom of asymptotic spatial homogeneity is of particular interest since it allows to quantify the rate of spatial diversification when the region under consideration becomes large. In this paper, we first investigate the general concepts of spatial risk measures and corresponding axioms further. Second, in the case of a general cost field, we especially give sufficient conditions such that spatial risk measures associated with expectation, variance, Value-at-Risk as well as expected shortfall and induced by this cost field satisfy the axioms of asymptotic spatial homogeneity of order 0, -2, -1 and -1, respectively. Last but not least, in the case where the cost field is a function of a max-stable random field, we mainly provide conditions on both the function and the max-stable field ensuring the latter properties. Max-stable random fields are relevant when assessing the risk of extreme events since they appear as a natural extension of multivariate extreme-value theory to the level of random fields. Overall, this paper improves our understanding of spatial risk measures as well as of their properties with respect to the space variable and generalises many results obtained in Koch (2017).
    Date: 2018–03
  6. By: Nikolaos Antonakakis (Economics and Finance Subject Group, University of Portsmouth and Department of Business and Management, Webster Vienna Private University); Juncal Cunado (Department of Economics, University of Navarra); George Filis (Department of Accounting, Finance and Economics, Bournemouth University); David Gabauer (Department of Economics, Johannes Kepler University); Fernando Perez de Gracia (Department of Economics, University of Navarra)
    Abstract: This paper investigates the volatility spillovers and co-movements among oil prices and stock prices of major oil and gas corporations over the period between 18th June 2001 and 1st February 2016. To do so, we use the spillover index approach by Diebold and Yilmaz (2009, 2012, 2014, 2015) and the dynamic correlation coefficient model of Engle (2002) so as to identify the transmission mechanisms of volatility shocks and the contagion of volatility among oil prices and stock prices of oil and gas companies, respectively. Given that volatility transmission across oil and major oil and gas corporations is important for portfolio diversification and risk management, we also examine optimal weights and hedge ratios among the aforementioned series. Our results point to the existence of significant volatility spillover effects among oil and oil and gas companies’ stock volatility. However, the spillover is usually unidirectional from oil and gas companies’ stock volatility to oil volatility, with BP, CHEVRON, EXXON, SHELL and TOTAL being the major net transmitters of volatility to oil markets. Conditional correlations are positive and time-varying, with those between each of the aforementioned companies and oil being the highest. Finally, the diversification benefits and hedging effectiveness based on our results are discussed.
    Keywords: Oil prices; oil and gas corporations; volatility spillovers; volatility co-movement; hedging; portfolio weights
    JEL: C32 F3 G12 Q43
    Date: 2018–03
  7. By: Luca Spadafora; Francesca Sivero; Nicola Picchiotti
    Abstract: This paper proposes a new integrated variance estimator based on order statistics within the framework of jump-diffusion models. Its ability to disentangle the integrated variance from the total process quadratic variation is confirmed by both simulated and empirical tests. For practical purposes, we introduce an iterative algorithm to estimate the time-varying volatility and the occurred jumps of log-return time series. Such estimates enable the definition of a new market risk model for the Value at Risk forecasting. We show empirically that this procedure outperforms the standard historical simulation method applying standard back-testing approach.
    Date: 2018–03
  8. By: Eduardo Abi Jaber (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris-Dauphine - CNRS - Centre National de la Recherche Scientifique); Omar El Euch (Ecole Polytechnique - X)
    Abstract: Rough volatility models are very appealing because of their remarkable fit of both historical and implied volatilities. However, due to the non-Markovian and non-semimartingale nature of the volatility process, there is no simple way to simulate efficiently such models, which makes risk management of derivatives an intricate task. In this paper, we design tractable multi-factor stochastic volatility models approximating rough volatility models and enjoying a Markovian structure. Furthermore, we apply our procedure to the specific case of the rough Heston model. This in turn enables us to derive a numerical method for solving fractional Riccati equations appearing in the characteristic function of the log-price in this setting.
    Keywords: Rough volatility models,rough Heston models,stochastic Volterra equations,affine Volterra processes,fractional Riccati equations,limit theorems
    Date: 2018–01–31
  9. By: Abdelkader Derbali (Institut Supérieur de Gestion Sousse, Université de Sousse)
    Abstract: This main idea of this paper is to examine theoretically the current model of credit portfolio management. We employ the credit portfolio view to examine the default probability measurement. The development of this type of model is based on a theoretical basis developed by several researchers. The evolution of their default frequencies and the size of the loan portfolio are expressed as functions of macroeconomic and microeconomic conditions as well as unobservable credit risk factors, which explained by other factors. We developed three sections to explain the different characteristics of this model. The purpose of this model is to assess the default probability of credit portfolio.
    Keywords: Default probability,Credit risk,Risk management,Credit Portfolio View
    Date: 2018–01–30
  10. By: Lotfaliei, Babak
    Abstract: This paper investigates how the asset-return variance risk premium changes leverage. I find that the premium lowers leverage by increasing risk-neutral bankruptcy probability and costs in a model where asset returns have stochastic variance with risk premium. Empirically, the model calibrations verify significant reduction in optimal leverage, closer to observed leverage than the model without the premium. In model-free regressions, I also document negative correlation between leverage and the variance premium. The most negative correlation is among investment-grade firms with low asset beta and historical variance but high variance premium because their assets have high exposure to market variance premium. JEL Classification: G32, G33, G12
    Keywords: capital structure, optimal leverage, variance risk premium
    Date: 2018–03
  11. By: Yichuan Wang (University of Michigan); Thomas Eisenbach (Federal Reserve Bank of New York); Martin Schmalz (University of Michigan); Marianne Andries (Toulouse School of Economics)
    Abstract: We estimate the term structure of the price of variance risk (PVR), which helps distinguish between competing asset-pricing theories. First, we measure the PVR as proportional to the Sharpe ratio of short-term holding returns of delta-neutral index straddles; second, we estimate the PVR in a Heston (1993) stochastic-volatility model. In both cases, the estimation is performed separately for different maturities. We find the PVR is negative and decreases in absolute value with maturity; it is more negative and its term structure is steeper when volatility is high. These findings are inconsistent with calibrations of established asset-pricing models that assume constant risk aversion across maturities.
    Date: 2017
  12. By: Peter Martey Addo (Expert Synapses SNCF Mobilité; LabEx ReFi); Dominique Guegan (University Paris 1 Pantheon Sorbonne; Ca' Foscari Unversity Venice; IPAG Business School; LabEx ReFi); Bertrand Hassani (Capgemini Consulting; LabEx ReFi)
    Abstract: Due to the hyper technology associated to Big Data, data availability and computing power, most banks or lending financial institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modelling process to test the stability of binary classifiers by comparing performance on separate data. We observe that tree-based models are more stable than models based on multilayer artificial neural networks. This opens several questions relative to the intensive used of deep learning systems in the enterprises.
    Keywords: Credit risk, Financial regulation, Data Science, Bigdata, Deep learning
    Date: 2018
  13. By: Abdelkader Derbali (Institut Supérieur de Gestion Sousse, Université de Sousse)
    Abstract: The aim of this paper is to investigate theoretically one of the current models of credit portfolio management. There are currently three types of models to consider the risk of credit portfolio: the structural models (Moody's KMV model and CreditMetrics model) also defined by the models of the value of the firm, reduced form models also defined by models with intensity models (the actuarial models) and the econometric models (the Macro-factors model). The development of the three types of models is based on a theoretical basis developed by several researchers. The evolution of their default frequencies and the size of the loan portfolio are expressed as functions of macroeconomic and microeconomic conditions as well as unobservable credit risk factors, which explained by other factors. We developed this paper to explain the different characteristics of the CreditRisk+ models. The purpose of this model is to calculate the default probability of credit portfolio.
    Keywords: Risk management,Credit risk,Default probability,Structural models,KMV model,CreditRisk+,Credit Portfolio View
    Date: 2018–01–30
  14. By: Sebnem Kalemli-Ozcan; Xiaoxi Liu; Ilhyock Shim
    Abstract: We test the risk taking channel of exchange rate appreciations using firm-level data from private and public firms in ten Asian emerging market economies during 2002-2015. Since foreign currency (FX) debt at the firm level is not observed for the Asian economies, we approximate the FX debt of a given firm by assuming that any given firm will hold a constant share of its total debt in foreign currency, where this share is given by the firm's country's share of FX liabilities in total liabilities. We measure risk taking by firm leverage. We show that firms with a higher volume of FX debt before the exchange rate appreciates, increase their leverage relatively more after the appreciation. Our results imply that more indebted firms become even more leveraged after exchange rate appreciations.
    Keywords: capital flows, exchange rates, FX borrowing, firm heterogeneity, firm leverage
    JEL: E0 F0 F1
    Date: 2018–03
  15. By: Abdelkader Derbali (Institut Supérieur de Gestion Sousse, Université de Sousse)
    Abstract: The purpose of this paper is to study the credit portfolio management by the structural models (Moody's KMV model and CreditMetrics model) also defined by the models of the value of the firm. The development of this type of models is based on a theoretical basis developed by several researchers. The evolution of their default frequencies and the size of the loan portfolio are expressed as functions of macroeconomic and microeconomic conditions as well as unobservable credit risk factors, which explained by other factors. We develop two sections to explain the different characteristics of those two models. The purpose of all its models is to express the default probability of credit portfolio.
    Keywords: Structural models,KMV model 2,Default probability,Credit risk,Risk management
    Date: 2018–01–30
  16. By: Alan White (FinPricing)
    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: 2018–03–20
  17. By: Hałaj, Grzegorz
    Abstract: Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based modelling fashion. The model is confronted with data from the 2014 EU stress test covering all the major banking groups in the EU. The potential amplification role of asset managers is taken into account in a stylised fashion. In particular, we investigate the importance of the channels through which the funding shock to financial institutions can spread across the financial system. JEL Classification: G11, G21, C61
    Keywords: ABM, liquidity, systemic risk
    Date: 2018–01
  18. By: Paul E. Carrillo; William M. Doerner (Federal Housing Finance Agency); William D. Larson (Federal Housing Finance Agency)
    Abstract: The transaction price of identical housing units can vary widely due to heterogeneity in buyer and seller preferences, appraisers, and search costs, generating "markups" above or below the average market price. These markups are mean reverting upon subsequent transactions, suggesting transitory factors play a role in same-unit dynamics. We show markups are an important driver of mortgage delinquencies, defaults, prepayments, and credit losses conditional on default. In general, our findings highlight several important aspects of mortgage risk management, including underwriting, insurance, and unit-level house value dynamics.
    Keywords: collateral risk, automated valuation model, Great Recession
    JEL: C43 R14 R30
    Date: 2018–04
  19. By: Abdelkader Derbali (Université de Sousse, Institut Supérieur de Gestion Sousse); Tarek Chebbi (Université de Sousse)
    Abstract: In this paper, we use for the first time the GARCH-DECO (1,1) to investigate empirically the dependence between S&P500 index and the sixteen selected S&P GSCI commodities index. We employ daily prices of S&P500 and S&P GSCI commodities indices over the period from January 01, 2003 to December 31, 2015. From the empirical results, the conditional dependence between S&P500 and S&P GSCI commodities indices demonstrate the presence of highly volatility and validate the existence of a greatly time-varying variance in the dynamic equicorrelation between time serie returns obtained after the estimation of the GARCH-DECO (1,1) model. Besides, the conditional heteroscedasticity volatility prediction attains their maximum after the financial crisis of 2007, especially on both years 2008 and 2009. Our empirical finding indicates the existence of highly dependency between S&P500 and S&P GSCI commodities indices which prove the financialisation of US stock market indices and commodities.
    Keywords: S&P 500,S&P GSCI,Commodities,Equicorrelation,GARCH-DECO
    Date: 2018–01–29
  20. By: Aslanidis, Nektarios,; Christiansen, Charlotte
    Abstract: This paper investigates flight-to-safety from stocks to bonds in seven European markets. We use quantile regressions to identify flight- to-safety episodes. The simple risk-return trade-off on the stock markets is negative which is caused by flight-to-safety episodes: During normal periods, the risk-return trade-off is positive and during flight-to-safety episodes it is negative. The effects of flight-to-safety episodes on the risk-return trade-off are qualitatively similar for own country flight-to-safety episodes, for flight from own country stock market to the US bond market, and for US flight- to-safety. The strength of the trade-off is strongest for own country flight- to-safety episodes. The risk-return trade-off is not significantly influenced by recession periods or the recent sovereign debt crisis. The main results hold for flight to gold instead of to bonds. Keywords: flight-to-safety; risk-return trade-off; European markets; stock market; bond market; gold futures. JEL Classfications: C58, F30, G11, G15
    Keywords: Finances internacionals, 336 - Finances. Banca. Moneda. Borsa,
    Date: 2018

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