nep-rmg New Economics Papers
on Risk Management
Issue of 2014‒04‒11
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Parallel American Monte Carlo By Calypso Herrera; Louis Paulot
  2. Forecasting the Volatility of the Dow Jones Islamic Stock Market Index: Long Memory vs. Regime Switching By Adnen Ben Nasr; Thomas Lux; Ahdi N. Ajmi; Rangan Gupta
  3. Asymptotic multivariate finite-time ruin probabilities with heavy-tailed claim amounts: Impact of dependence and optimal reserve allocation By Romain Biard
  4. The Risk Return Relationship: Evidence from Index Return and Realised Variance Series By Minxian Yang
  5. Life Insurance and Pension Contracts I: The Time Additive Life Cycle Model By Aase, Knut K.
  6. Social Security and the Interactions Between Aggregate and Idiosyncratic Risk By Daniel Harenberg; Alexander Ludwig
  7. The Determinants of Credit Default on Portuguese Start-Up Firms: .An Econometric model By Vitor Gonçalves; Francisco Vitorino Martins; Elísio Brandão
  8. Non-parametric Models for Univariate Claim Severity Distributions - an approach using R By Catalina Bolance; Montserrat Guillen; David Pitt
  9. Monetary policy implementation in an interbank network: Effects on systemic risk By Bluhm, Marcel; Faia, Ester; Krahnen, Jan Pieter
  10. Stochastic Evolution of Stock Market Volume-Price Distributions By Paulo Rocha; Frank Raischel; Jo\~ao P. da Cruz; Pedro G. Lind

  1. By: Calypso Herrera; Louis Paulot
    Abstract: In this paper we introduce a new algorithm for American Monte Carlo that can be used either for American-style options, callable structured products or for computing counterparty credit risk (e.g. CVA or PFE computation). Leveraging least squares regressions, the main novel feature of our algorithm is that it can be fully parallelized. Moreover, there is no need to store the paths and the payoff computation can be done forwards: this allows to price structured products with complex path and exercise dependencies. The key idea of our algorithm is to split the set of paths in several subsets which are used iteratively. We give the convergence rate of the algorithm. We illustrate our method on an American put option and compare the results with the Longstaff-Schwartz algorithm.
    Date: 2014–04
  2. By: Adnen Ben Nasr (Laboratoire BESTMOD, ISG de Tunis, Universite de Tunis, Tunisia); Thomas Lux (Department of Economics, University of Kiel, Germany and Banco de Espana Chair in Computational Economics, University Jaume I, Castellon, Spain); Ahdi N. Ajmi (College of Science and Humanities in Slayel, Salman bin Abdulaziz University, Kingdom of Saudi Arabia); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of Islamic Sharia rules. In this light, we investigate the statistical properties of the Dow Jones Islamic Finance (DJIM) index and explore its volatility dynamics using a number of up-to-date statistical models allowing for long memory and regime-switching dynamics. We find that the DJIM shares all stylized facts of traditional asset classes, and estimation results and forecasting performance for various volatility models are also in line with prevalent ndings in the literature. Overall, the relatively new Markov-switching multifractal model performs best under the majority of time horizons and loss criteria. Long memory GARCH-type models always improve upon the short-memory GARCH specification and additionally allowing for regime changes can further improve their performance.
    Keywords: Islamic finance, volatility dynamics, long memory, multifractals
    JEL: G15 G17 G23
    Date: 2014–03
  3. By: Romain Biard (LM-Besançon - Laboratoire de Mathématiques - CNRS : UMR6623 - Université de Franche-Comté)
    Abstract: In ruin theory, the univariate model may be found too restrictive to describe accurately the complex evolution of the reserves of an insurance company. In the case where the company is composed of multiple lines of business, we compute asymptotics of finite-time ruin probabilities. Capital transfers between lines are partially allowed. When claim amounts are regularly varying distributed, several forms of dependence between the lines are considered. We also study the optimal allocation of a large global initial reserve in order to minimize the asymptotic ruin probability.
    Keywords: Multivariate finite-time ruin probabilities; Multivariate regular variation; Capital transfer; Optimal allocation
    Date: 2013
  4. By: Minxian Yang (School of Economics, Australian School of Business, the University of New South Wales)
    Abstract: The risk return relationship is analysed in bivariate models for return and realised variance(RV) series. Based on daily time series from 21 international market indices for more than 13 years (January 2000 to February 2013), the empirical findings support the arguments of risk return tradeoff, volatility feedback and statistical balance. It is reasoned that the empirical risk return relationship is primarily shaped by two important data features: the negative contemporaneous correlation between the return and RV, and the difference in the autocorrelation structures of the return and RV.
    Keywords: risk premium, volatility feedback, return predictability, realised variance model, statistical balance
    JEL: C32 C52 G12 G10
    Date: 2014–03
  5. By: Aase, Knut K. (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: We analyze optimal consumption in the life cycle model by introducing life and pension insurance contracts. The model contains a credit market with biometric risk, and market risk via risky securities. This idealized framework enables us to clarify important aspects life insurance and pension contracts. We find optimal pension plans and life insurance contracts where the benefits are state dependent. We compare these solutions both to the ones of standard actuarial theory, and to policies offered in practice. Implications of this include what role the insurance industry may play to improve welfare. The relationship between substitution of consumption and risk aversion is highlighted in the presence of a consumption puzzle. One problem related portfolio choice is discussed - the horizon problem. Finally, we present some comments on longevity risk and cohort risk.
    Keywords: The life cycle model; pension insurance; optimal life insurance; longevity risk; the horizon problem; consumption puzzle
    JEL: D91
    Date: 2014–03–25
  6. By: Daniel Harenberg; Alexander Ludwig
    Abstract: We ask whether a PAYG-financed social security system is welfare improving in an economy with idiosyncratic and aggregate risk. We argue that interactions between the two risks are important for this question. One is a direct interaction in the form of a countercyclical variance of idiosyncratic income risk. The other indirectly emerges over a household's life-cycle because retirement savings contain the history of idiosyncratic and aggregate shocks. We show that this leads to risk interactions, even when risks are statistically independent. In our quantitative analysis, we find that introducing social security with a contribution rate of two percent leads to welfare gains of 2.2% of lifetime consumption in expectation, despite substantial crowding out of capital. This welfare gain stands in contrast to the welfare losses documented in the previous literature, which studies one risk in isolation. We show that jointly modeling both risks is crucial: 60% of the welfare benefits from insurance result from the interactions of risks.
    Keywords: social security, idiosyncratic risk, aggregate risk, welfare
    JEL: C68 E27 E62 G12 H55
    Date: 2014–03–20
  7. By: Vitor Gonçalves (Phd Student of Finance, FEP.UP); Francisco Vitorino Martins (Professor FEP.UP); Elísio Brandão (Professor of Finance, FEP.UP)
    Abstract: In this paper we investigate the behaviour of credit default in start-up companies. Using a logit regression technique on a panel data of 1430 start-ups and considering a tracking period of three years, we tested the impact on the probability of occurrence of the first credit event in financing agreements due to variables grouped into three categories: financial capital, human capital and industry dynamics. We concluded from a financial point of view, that the support provided by partners in the financing of the company’s activity, the intensity of use of assets under management and reduced debt pay-back periods, were decisive in mitigating risk of default. In addition we found that the occurrence of a credit event will only be as limited as higher the quality of human capital held by the promoter of the project in terms of educational background and management experience.
    Keywords: Credit Default; Start-Up; Financial Capital; Human Capital; Industry Dynamics
    JEL: G33 M13
    Date: 2014–04
  8. By: Catalina Bolance (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); Montserrat Guillen (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); David Pitt (Department of Applied Finance and Actuarial Studies, Macquarie University)
    Abstract: This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions esti- mated with non-parametric methods. The methods are implemented using the statistical package R. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statis- tical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described.
    Keywords: Loss modeling, insurance, finance
    Date: 2014–02
  9. By: Bluhm, Marcel; Faia, Ester; Krahnen, Jan Pieter
    Abstract: This paper makes a conceptual contribution to the e ffect of monetary policy on financial stability. We develop a microfounded network model with endogenous network formation to analyze the impact of central banks' monetary policy interventions on systemic risk. Banks choose their portfolio, including their borrowing and lending decisions on the interbank market, to maximize profit subject to regulatory constraints in an asset-liability framework. Systemic risk arises in the form of multiple bank defaults driven by common shock exposure on asset markets, direct contagion via the interbank market, and firesale spirals. The central bank injects or withdraws liquidity on the interbank markets to achieve its desired interest rate target. A tension arises between the bene ficial effects of stabilized interest rates and increased loan volume and the detrimental effects of higher risk taking incentives.We fi nd that central bank supply of liquidity quite generally increases systemic risk. --
    Keywords: network formation,contagion,central banks' interventions
    JEL: C63 D85 G01 G28
    Date: 2014
  10. By: Paulo Rocha; Frank Raischel; Jo\~ao P. da Cruz; Pedro G. Lind
    Abstract: Using available data from the New York stock market (NYSM) we test four different bi-parametric models to fit the correspondent volume-price distributions at each $10$-minute lag: the Gamma distribution, the inverse Gamma distribution, the Weibull distribution and the log-normal distribution. The volume-price data, which measures market capitalization, appears to follow a specific statistical pattern, other than the evolution of prices measured in similar studies. We find that the inverse Gamma model gives a superior fit to the volume-price evolution than the other models. We then focus on the inverse Gamma distribution as a model for the NYSM data and analyze the evolution of the pair of distribution parameters as a stochastic process. Assuming that the evolution of these parameters is governed by coupled Langevin equations, we derive the corresponding drift and diffusion coefficients, which then provide insight for understanding the mechanisms underlying the evolution of the stock market.
    Date: 2014–04

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