Interplay of Asymptotically Dependent Insurance Risks and Financial Risks
|
|
- Vivien Franklin
- 5 years ago
- Views:
Transcription
1 Interplay of Asymptotically Dependent Insurance Risks and Financial Risks Zhongyi Yuan The Pennsylvania State University July 16, 2014 The 49th Actuarial Research Conference UC Santa Barbara Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 1/22
2 Outline 1 Introduction Model Risks Assumptions 2 Results 3 Concluding Remarks Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 2/22
3 Introduction Model 1 Question we consider Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 3/22
4 Introduction Model 1 Question we consider 2 Model: A discrete time model for an insurer Insurer s initial wealth: W 0 = x; wealth at time m: W m Insurer s net insurance loss within the m-th period: X m Insurer s investment return in the m-th period: R m Insurer s wealth process: 3 Comments m m W m = W m 1 R m X m = x R j j=1 i=1 X i m j=i+1 R j (1) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 3/22
5 Introduction Risks Insurance risks Losses from insurance claims One-claim-causes-ruin phenomenon (Embrechts et al. (1997), Muermann (2008), etc.) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 4/22
6 1992 Hurricane Andrew Insured loss: $16 billion More than 60 insurance companies became insolvent (Muermann (2008, NAAJ))
7 2004 Indian Ocean Earthquake and Tsunami Damaged about $15 billion Not much insurance loss due to lack of insurance coverage
8 2005 Hurricane Katrina Insured loss: $41.1 billion Damaged $108 billion
9 2011 Japan Earthquake, Tsunami and Nuclear Crisis Insured loss: $ billion
10 2012 Hurricane Sandy Insured loss: $19 billion Damage: over $68 billion
11 Introduction Risks Insurance risks Losses from insurance claims One-claim-causes-ruin phenomenon (Embrechts et al. (1997), Muermann (2008), etc.) Hedging Heavy-tailedness: assumption of regular variation Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 4/22
12 Introduction Risks Insurance risks Losses from insurance claims One-claim-causes-ruin phenomenon (Embrechts et al. (1997), Muermann (2008), etc.) Hedging Heavy-tailedness: assumption of regular variation A distribution function F is said to have a regularly varying tail with index α > 0, written as F R α, if F (xt) lim x F (x) = t α, t > 0. Examples: Pareto, t-distribution, Burr distribution Losses due to earthquakes: 0.6 < α < 1.5 Losses due to hurricanes: 1.5 < α < 2.5. (See, e.g. Ibragimov et al. (2009)) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 4/22
13 Introduction Risks Financial risks Daily log-returns for the period of 01/03/1989 to 06/30/2003. (Angelidis and Degiannakis (2005)) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 5/22
14 Introduction Risks Financial risks Daily log-returns for the period of 01/03/1989 to 06/30/2003. (Angelidis and Degiannakis (2005)) Sylized facts on returns from stocks/stock indices (Basrak et al. (2002), Rachev et al. (2005), Kelly and Jiang (2014), etc.): Regularly varying with tail index 2 < α < 4 Asymmetric Q: What are the effects of these risks on the survival of the insurer? Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 5/22
15 Introduction Risks Related studies 1 Related studies: Nyrhinen (1999) Tang and Tsitsiashvili (2003). Chen (2011) Fougères and Mercadier (2012) 2 Comments. Why asymptotic dependence? Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 6/22
16 Introduction Risks (Asymptotic/Extreme) dependence Copula of (X, Y ): C(, ) Survival copula of (X, Y ): Ĉ(, ) Asymptotic dependence Ĉ(u, u) Pr (X > FX lim = lim (1 u), Y > F Y u 0 u u 0 u (1 u)) > 0 Examples Assume asymptotic dependence for (X, 1/R) = (X, Y ) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 7/22
17 Introduction Assumptions Assumptions 1 (X 1, R 1 ), (X 2, R 2 ),... are i.i.d. copies of (X, R). 2 Suppose that F X R α, α > 0, with F X ( x) = o ( F X (x) ). Also suppose that the distribution of R has a regularly varying tail at 0 with index β > 0. 3 Suppose that there exists some function H(, ) on [0, ] 2 \{0}, such that H(t 1, t 2 ) > 0 for every (t 1, t 2 ) (0, ] 2 and Ĉ (ut 1, ut 2 ) lim = H(t 1, t 2 ) on [0, ] 2 \{0}. (2) u 0 u Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 8/22
18 Introduction Assumptions Example Let (X, Y ) have an Archimedean copula C (u, v) = ϕ 1 (ϕ (u) + ϕ (v)). (3) ϕ(u) Assume that the generator ϕ satisfies ϕ(1 tu) lim u 0 ϕ(1 u) = tθ, t > 0, (4) 0 1 u for some constant θ > 1 (Note that θ 1 if (4) holds). (See Charpentier and Segers (2009)) Example: ϕ(u) = ( ln u) θ Then (2) holds with H(t 1, t 2 ) = t 1 + t 2 ( t θ 1 + t θ 2) 1/θ > 0, (t1, t 2 ) (0, ] 2. (5) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 9/22
19 Introduction Assumptions Implications 1 Let Y = 1/R and Y i = 1/R i, i = 1, 2,.... Assumption 1 = Y is regularly varying with index β > 0. 2 X and Y are asymptotically dependent. 3 The random vector ( 1/F X (X ), 1/F Y (Y ) ) MRV 1 (multivariate regularly varying), and the vague convergence ( ( ) ) 1 1 x Pr x F X (X ), 1 v ν( ) F Y (Y ) on [0, ] 2 \{0} (6) holds with the Radon measure ν defined by ν [0, (t 1, t 2 )] c = ( 1 H, 1 ), t 1 t 2 t 1 t 2 (t 1, t 2 ) (0, ) 2. (7) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 10/22
20 Introduction Assumptions Implications cont d The random vector (X, Y ) follows a nonstandard MRV structure; i.e., for some Radon measure µ, the following vague convergence holds: (( ) ) X x Pr b X (x), Y v µ( ) on [0, ] 2 \{0}, (8) b Y (x)) ( ) ( ) where b X (x) = 1 F (x) and by (x) = 1 X F (x). Y See Resnick (2007). Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 11/22
21 Results 1 Introduction Model Risks Assumptions 2 Results 3 Concluding Remarks Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 12/22
22 Results Probabilities of ruin Finite-time horizon: ( ) ψ(x; n) = Pr min W m < 0 1 m n W 0 = x m m = Pr min x R j = Pr Infinite-time horizon: 1 m n max 1 m n ψ(x) = Pr m j=1 X i i=1 j=1 max 1 m< m X i R j i=1 j=i+1 i Y j > x m X i i=1 j=1 i Y j > x < 0 Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 13/22
23 Results Main result 1 Theorem 2.1 Under Assumptions 1 3 we have n i ψ(x; n) Pr Y j > x X i i=1 j=1 ( n 1 i=0 ) ( E [Y αβ/(α+β)]) i v(a) b (x), where the set A = {(t 1, t 2 ) [0, ] 2 : t 1/α 1 t 1/β 2 > 1}, the function b( ) = ( ) ( ) 1/F X 1/FY ( ), and the measure v is defined by relation (7). Note: ψ( ; n) R αβ/(α+β) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 14/22
24 Results Main result 1 cont d Theorem 2.2 In addition to Assumptions 1 3, assume that E [ Y αβ/(α+β)] < 1. Then ψ(x) 1 1 E [ v(a) Y αβ/(α+β)] b (x), On the estimation of ν. See, e.g., Resnick (2007), Nguyen and Samorodnitsky (2013). Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 15/22
25 Results Asymptotic dependence vs asymptotic independence Roughly, under (asymptotic) independence, the probability of ruin decays faster. Examples: Under corresponding conditions, ψ(x; n) C n F X (x) (Chen (2011)) ψ(x; n) C n F X (x) + D n F Y (x) (Li and Tang (2014)) Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 16/22
26 Results Main result 2 (& role of regulation) Prudent Person Investment Principle (PPIP) under Solvency II: no requirement on what insurers can invest and what they can not, but they are encouraged to reduce their investment in equities (take less investment risks) due to high capital charges, which would reduce the overall profit. If the insurer only invests a proportion π < 1 of its wealth into risky assets, and the rest earns a risk free return r f 1, then R > (1 π)r f, which would violate Assumption 2. Assumption 2 : Suppose that F X R α, α > 0, with F X ( x) = o ( F X (x) ). Also suppose that R is bounded below by some positive number r. Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 17/22
27 Results Main result 2 Theorem 2.3 Under the Assumptions 1, 2, and 3, we have ( n i n 1 ) ψ(x; n) Pr Y j > x (E [Y α ]) i r α F X (x). (9) Theorem 2.4 X i i=1 j=1 In addition to the assumptions of Theorem 2.3, assume that E [Y α ] < 1. Then 1 ψ(x) 1 E [Y α ] r α F X (x). (10) i=0 Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 18/22
28 Concluding Remarks 1 Introduction Model Risks Assumptions 2 Results 3 Concluding Remarks Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 19/22
29 Concluding Remarks Concluding Remarks Asymptotically dependent insurance risks and financial risks both play a significant role in affecting the insurer s survival. By regulating insurers investment behavior, like discouraging too risky investments, regulators can help significantly reduce their probability of ruin. Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 20/22
30 Concluding Remarks References Angelidis, T.; Degiannakis, S. Modeling risk for long and short trading positions. Journal of Risk Finance 6 (2005), no. 3, Basrak, B.; Davis, R. A.; Mikosch, T. Regular variation of GARCH processes. Stochastic Processes and Their Applications 99 (2002), no. 1, Charpentier, A.; Segers, J. Tails of multivariate Archimedean copulas. Journal of Multivariate Analysis 100 (2009), no. 7, Chen, Y. The finite-time ruin probability with dependent insurance and financial risks. Journal of Applied Probability 48 (2011), no. 4, Embrechts, P.; Klüppelberg, C.; Mikosch, T. Modelling Extremal Events for Insurance and Finance. Springer-Verlag, Berlin, Fougères, A.; Mercadier, C. Risk measures and multivariate extensions of Breiman s theorem. Journal of Applied Probability 49 (2012), no. 2, Ibragimov, R., Jaffee, D., Walden, J.. Non-diversication traps in markets for catastrophic risk. Review of Financial Studies 22 (2009), Kelly, B.; Jiang, H. Tail Risk and Asset Prices. Review of Financial Studies 2014, to appear. Li, J.; Tang, Q. Interplay of insurance and financial risks in a discrete-time model with strongly regular variation. Bernoulli (2014), to appear. Muermann, A. Market price of insurance risk implied by catastrophe derivatives. North American Actuarial Journal 12 (2008), no. 3, Nguyen, T.; Samorodnitsky, G. Multivariate tail estimation with application to analysis of covar. Astin Bulletin 43 (2013), no. 2, Rachev, S.T., Menn, C., Fabozzi, F.J. Fat-Tailed and Skewed Asset Return Distributions: Implications for Risk Management, Portfolio Selection, and Option Pricing. Wiley, Hoboken, NJ, Resnick, S. I. Heavy-Tail Phenomena. Probabilistic and Statistical Modeling. Springer, New York, Tang, Q.; Tsitsiashvili, G. Precise estimates for the ruin probability in finite horizon in a discrete-time model with heavy-tailed insurance and financial risks. Stochastic Processes and Their Applications 108 (2003), no. 2, Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 21/22
31 Concluding Remarks Thank you! Zhongyi Yuan (Penn State University) Asymptotically Dependent Insurance/Financial Risks 22/22
Ruin with Insurance and Financial Risks Following a Dependent May 29 - June Structure 1, / 40
1 Ruin with Insurance and Financial Risks Following a Dependent May 29 - June Structure 1, 2014 1 / 40 Ruin with Insurance and Financial Risks Following a Dependent Structure Jiajun Liu Department of Mathematical
More informationHeavy-tailedness and dependence: implications for economic decisions, risk management and financial markets
Heavy-tailedness and dependence: implications for economic decisions, risk management and financial markets Rustam Ibragimov Department of Economics Harvard University Based on joint works with Johan Walden
More informationThe ruin probabilities of a multidimensional perturbed risk model
MATHEMATICAL COMMUNICATIONS 231 Math. Commun. 18(2013, 231 239 The ruin probabilities of a multidimensional perturbed risk model Tatjana Slijepčević-Manger 1, 1 Faculty of Civil Engineering, University
More informationEstimation of Value at Risk and ruin probability for diffusion processes with jumps
Estimation of Value at Risk and ruin probability for diffusion processes with jumps Begoña Fernández Universidad Nacional Autónoma de México joint work with Laurent Denis and Ana Meda PASI, May 21 Begoña
More informationDependence Structure and Extreme Comovements in International Equity and Bond Markets
Dependence Structure and Extreme Comovements in International Equity and Bond Markets René Garcia Edhec Business School, Université de Montréal, CIRANO and CIREQ Georges Tsafack Suffolk University Measuring
More informationAGGREGATION OF LOG-LINEAR RISKS
Applied Probability Trust (9 January 2014) AGGREGATION OF LOG-LINEAR RISKS PAUL EMBRECHTS, ETH Zurich and Swiss Finance Institute, Switzerland ENKELEJD HASHORVA, University of Lausanne, Switzerland THOMAS
More informationComparative Analyses of Expected Shortfall and Value-at-Risk under Market Stress
Comparative Analyses of Shortfall and Value-at-Risk under Market Stress Yasuhiro Yamai Bank of Japan Toshinao Yoshiba Bank of Japan ABSTRACT In this paper, we compare Value-at-Risk VaR) and expected shortfall
More informationREINSURANCE RATE-MAKING WITH PARAMETRIC AND NON-PARAMETRIC MODELS
REINSURANCE RATE-MAKING WITH PARAMETRIC AND NON-PARAMETRIC MODELS By Siqi Chen, Madeleine Min Jing Leong, Yuan Yuan University of Illinois at Urbana-Champaign 1. Introduction Reinsurance contract is an
More informationQuantitative Models for Operational Risk
Quantitative Models for Operational Risk Paul Embrechts Johanna Nešlehová Risklab, ETH Zürich (www.math.ethz.ch/ embrechts) (www.math.ethz.ch/ johanna) Based on joint work with V. Chavez-Demoulin, H. Furrer,
More informationFinancial Risk Forecasting Chapter 9 Extreme Value Theory
Financial Risk Forecasting Chapter 9 Extreme Value Theory Jon Danielsson 2017 London School of Economics To accompany Financial Risk Forecasting www.financialriskforecasting.com Published by Wiley 2011
More informationA Limit Distribution of Credit Portfolio Losses with Low Default Probabilities
A Limit Distribution of Credit Portfolio Losses with Low Default Probabilities Xiaojun Shi [a], Qihe Tang [b] and Zhongyi Yuan [c] [a] School of Finance, Renmin University of China No. 59 Zhongguancun
More informationFinancial Econometrics Jeffrey R. Russell. Midterm 2014 Suggested Solutions. TA: B. B. Deng
Financial Econometrics Jeffrey R. Russell Midterm 2014 Suggested Solutions TA: B. B. Deng Unless otherwise stated, e t is iid N(0,s 2 ) 1. (12 points) Consider the three series y1, y2, y3, and y4. Match
More informationEconomic factors and solvency
Economic factors and solvency Harri Nyrhinen, University of Helsinki ASTIN Colloquium Helsinki 2009 Insurance solvency One of the main concerns in actuarial practice and theory. The companies should have
More informationModeling of Price. Ximing Wu Texas A&M University
Modeling of Price Ximing Wu Texas A&M University As revenue is given by price times yield, farmers income risk comes from risk in yield and output price. Their net profit also depends on input price, but
More informationMeasuring Financial Risk using Extreme Value Theory: evidence from Pakistan
Measuring Financial Risk using Extreme Value Theory: evidence from Pakistan Dr. Abdul Qayyum and Faisal Nawaz Abstract The purpose of the paper is to show some methods of extreme value theory through analysis
More informationUniversität Regensburg Mathematik
Universität Regensburg Mathematik Modeling financial markets with extreme risk Tobias Kusche Preprint Nr. 04/2008 Modeling financial markets with extreme risk Dr. Tobias Kusche 11. January 2008 1 Introduction
More informationMEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL
MEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL Isariya Suttakulpiboon MSc in Risk Management and Insurance Georgia State University, 30303 Atlanta, Georgia Email: suttakul.i@gmail.com,
More informationWeak Convergence to Stochastic Integrals
Weak Convergence to Stochastic Integrals Zhengyan Lin Zhejiang University Join work with Hanchao Wang Outline 1 Introduction 2 Convergence to Stochastic Integral Driven by Brownian Motion 3 Convergence
More informationRisk-adjusted Stock Selection Criteria
Department of Statistics and Econometrics Momentum Strategies using Risk-adjusted Stock Selection Criteria Svetlozar (Zari) T. Rachev University of Karlsruhe and University of California at Santa Barbara
More informationFinancial Risk Management
Financial Risk Management Professor: Thierry Roncalli Evry University Assistant: Enareta Kurtbegu Evry University Tutorial exercices #4 1 Correlation and copulas 1. The bivariate Gaussian copula is given
More informationSynthetic CDO Pricing Using the Student t Factor Model with Random Recovery
Synthetic CDO Pricing Using the Student t Factor Model with Random Recovery Yuri Goegebeur Tom Hoedemakers Jurgen Tistaert Abstract A synthetic collateralized debt obligation, or synthetic CDO, is a transaction
More informationModelling financial data with stochastic processes
Modelling financial data with stochastic processes Vlad Ardelean, Fabian Tinkl 01.08.2012 Chair of statistics and econometrics FAU Erlangen-Nuremberg Outline Introduction Stochastic processes Volatility
More informationA note on the Kesten Grincevičius Goldie theorem
A note on the Kesten Grincevičius Goldie theorem Péter Kevei TU Munich Probabilistic Aspects of Harmonic Analysis Outline Introduction Motivation Properties Results EA κ < 1 Further remarks More general
More informationAsymptotic methods in risk management. Advances in Financial Mathematics
Asymptotic methods in risk management Peter Tankov Based on joint work with A. Gulisashvili Advances in Financial Mathematics Paris, January 7 10, 2014 Peter Tankov (Université Paris Diderot) Asymptotic
More informationModelling Joint Distribution of Returns. Dr. Sawsan Hilal space
Modelling Joint Distribution of Returns Dr. Sawsan Hilal space Maths Department - University of Bahrain space October 2011 REWARD Asset Allocation Problem PORTFOLIO w 1 w 2 w 3 ASSET 1 ASSET 2 R 1 R 2
More informationTHE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION
THE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION SILAS A. IHEDIOHA 1, BRIGHT O. OSU 2 1 Department of Mathematics, Plateau State University, Bokkos, P. M. B. 2012, Jos,
More informationADVANCED OPERATIONAL RISK MODELLING IN BANKS AND INSURANCE COMPANIES
Small business banking and financing: a global perspective Cagliari, 25-26 May 2007 ADVANCED OPERATIONAL RISK MODELLING IN BANKS AND INSURANCE COMPANIES C. Angela, R. Bisignani, G. Masala, M. Micocci 1
More informationNoureddine Kouaissah, Sergio Ortobelli, Tomas Tichy University of Bergamo, Italy and VŠB-Technical University of Ostrava, Czech Republic
Noureddine Kouaissah, Sergio Ortobelli, Tomas Tichy University of Bergamo, Italy and VŠB-Technical University of Ostrava, Czech Republic CMS Bergamo, 05/2017 Agenda Motivations Stochastic dominance between
More informationFat Tailed Distributions For Cost And Schedule Risks. presented by:
Fat Tailed Distributions For Cost And Schedule Risks presented by: John Neatrour SCEA: January 19, 2011 jneatrour@mcri.com Introduction to a Problem Risk distributions are informally characterized as fat-tailed
More informationPricing Exotic Options Under a Higher-order Hidden Markov Model
Pricing Exotic Options Under a Higher-order Hidden Markov Model Wai-Ki Ching Tak-Kuen Siu Li-min Li 26 Jan. 2007 Abstract In this paper, we consider the pricing of exotic options when the price dynamic
More informationCopulas? What copulas? R. Chicheportiche & J.P. Bouchaud, CFM
Copulas? What copulas? R. Chicheportiche & J.P. Bouchaud, CFM Multivariate linear correlations Standard tool in risk management/portfolio optimisation: the covariance matrix R ij = r i r j Find the portfolio
More informationExchangeable risks in actuarial science and quantitative risk management
Exchangeable risks in actuarial science and quantitative risk management Etienne Marceau, Ph.D. A.S.A Co-director, Laboratoire ACT&RISK Actuarial Research Conference 2014 (UC Santa Barbara, Santa Barbara,
More informationIntroduction to Algorithmic Trading Strategies Lecture 8
Introduction to Algorithmic Trading Strategies Lecture 8 Risk Management Haksun Li haksun.li@numericalmethod.com www.numericalmethod.com Outline Value at Risk (VaR) Extreme Value Theory (EVT) References
More informationAll Investors are Risk-averse Expected Utility Maximizers. Carole Bernard (UW), Jit Seng Chen (GGY) and Steven Vanduffel (Vrije Universiteit Brussel)
All Investors are Risk-averse Expected Utility Maximizers Carole Bernard (UW), Jit Seng Chen (GGY) and Steven Vanduffel (Vrije Universiteit Brussel) First Name: Waterloo, April 2013. Last Name: UW ID #:
More informationEstimation of VaR Using Copula and Extreme Value Theory
1 Estimation of VaR Using Copula and Extreme Value Theory L. K. Hotta State University of Campinas, Brazil E. C. Lucas ESAMC, Brazil H. P. Palaro State University of Campinas, Brazil and Cass Business
More informationI. Time Series and Stochastic Processes
I. Time Series and Stochastic Processes Purpose of this Module Introduce time series analysis as a method for understanding real-world dynamic phenomena Define different types of time series Explain the
More informationLong-Term Risk Management
Long-Term Risk Management Roger Kaufmann Swiss Life General Guisan-Quai 40 Postfach, 8022 Zürich Switzerland roger.kaufmann@swisslife.ch April 28, 2005 Abstract. In this paper financial risks for long
More informationModeling. joint work with Jed Frees, U of Wisconsin - Madison. Travelers PASG (Predictive Analytics Study Group) Seminar Tuesday, 12 April 2016
joint work with Jed Frees, U of Wisconsin - Madison Travelers PASG (Predictive Analytics Study Group) Seminar Tuesday, 12 April 2016 claim Department of Mathematics University of Connecticut Storrs, Connecticut
More informationLindner, Szimayer: A Limit Theorem for Copulas
Lindner, Szimayer: A Limit Theorem for Copulas Sonderforschungsbereich 386, Paper 433 (2005) Online unter: http://epub.ub.uni-muenchen.de/ Projektpartner A Limit Theorem for Copulas Alexander Lindner Alexander
More informationRisk Management and Time Series
IEOR E4602: Quantitative Risk Management Spring 2016 c 2016 by Martin Haugh Risk Management and Time Series Time series models are often employed in risk management applications. They can be used to estimate
More informationA Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims
International Journal of Business and Economics, 007, Vol. 6, No. 3, 5-36 A Markov Chain Monte Carlo Approach to Estimate the Risks of Extremely Large Insurance Claims Wan-Kai Pang * Department of Applied
More informationWeek 1 Quantitative Analysis of Financial Markets Distributions B
Week 1 Quantitative Analysis of Financial Markets Distributions B Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 October
More informationEvaluation of Credit Value Adjustment with a Random Recovery Rate via a Structural Default Model
Evaluation of Credit Value Adjustment with a Random Recovery Rate via a Structural Default Model Xuemiao Hao and Xinyi Zhu University of Manitoba August 6, 2015 The 50th Actuarial Research Conference University
More informationCase Study: Heavy-Tailed Distribution and Reinsurance Rate-making
Case Study: Heavy-Tailed Distribution and Reinsurance Rate-making May 30, 2016 The purpose of this case study is to give a brief introduction to a heavy-tailed distribution and its distinct behaviors in
More informationSome Simple Stochastic Models for Analyzing Investment Guarantees p. 1/36
Some Simple Stochastic Models for Analyzing Investment Guarantees Wai-Sum Chan Department of Statistics & Actuarial Science The University of Hong Kong Some Simple Stochastic Models for Analyzing Investment
More informationTwo hours. To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER
Two hours MATH20802 To be supplied by the Examinations Office: Mathematical Formula Tables and Statistical Tables THE UNIVERSITY OF MANCHESTER STATISTICAL METHODS Answer any FOUR of the SIX questions.
More informationEmpirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange
ANNALS OF ECONOMICS AND FINANCE 8-1, 21 31 (2007) Empirical Analyses of Industry Stock Index Return Distributions for the Taiwan Stock Exchange Svetlozar T. Rachev * School of Economics and Business Engineering,
More informationCONSISTENCY AMONG TRADING DESKS
CONSISTENCY AMONG TRADING DESKS David Heath 1 and Hyejin Ku 2 1 Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA, USA, email:heath@andrew.cmu.edu 2 Department of Mathematics
More informationAn Insight Into Heavy-Tailed Distribution
An Insight Into Heavy-Tailed Distribution Annapurna Ravi Ferry Butar Butar ABSTRACT The heavy-tailed distribution provides a much better fit to financial data than the normal distribution. Modeling heavy-tailed
More informationIs the Potential for International Diversification Disappearing? A Dynamic Copula Approach
Is the Potential for International Diversification Disappearing? A Dynamic Copula Approach Peter Christoffersen University of Toronto Vihang Errunza McGill University Kris Jacobs University of Houston
More information14.461: Technological Change, Lectures 12 and 13 Input-Output Linkages: Implications for Productivity and Volatility
14.461: Technological Change, Lectures 12 and 13 Input-Output Linkages: Implications for Productivity and Volatility Daron Acemoglu MIT October 17 and 22, 2013. Daron Acemoglu (MIT) Input-Output Linkages
More informationEstimating Value at Risk of Portfolio: Skewed-EWMA Forecasting via Copula
Estimating Value at Risk of Portfolio: Skewed-EWMA Forecasting via Copula Zudi LU Dept of Maths & Stats Curtin University of Technology (coauthor: Shi LI, PICC Asset Management Co.) Talk outline Why important?
More informationIntroduction Recently the importance of modelling dependent insurance and reinsurance risks has attracted the attention of actuarial practitioners and
Asymptotic dependence of reinsurance aggregate claim amounts Mata, Ana J. KPMG One Canada Square London E4 5AG Tel: +44-207-694 2933 e-mail: ana.mata@kpmg.co.uk January 26, 200 Abstract In this paper we
More informationOptimal retention for a stop-loss reinsurance with incomplete information
Optimal retention for a stop-loss reinsurance with incomplete information Xiang Hu 1 Hailiang Yang 2 Lianzeng Zhang 3 1,3 Department of Risk Management and Insurance, Nankai University Weijin Road, Tianjin,
More informationTesting for non-correlation between price and volatility jumps and ramifications
Testing for non-correlation between price and volatility jumps and ramifications Claudia Klüppelberg Technische Universität München cklu@ma.tum.de www-m4.ma.tum.de Joint work with Jean Jacod, Gernot Müller,
More informationMeasures of Contribution for Portfolio Risk
X Workshop on Quantitative Finance Milan, January 29-30, 2009 Agenda Coherent Measures of Risk Spectral Measures of Risk Capital Allocation Euler Principle Application Risk Measurement Risk Attribution
More informationModelling catastrophic risk in international equity markets: An extreme value approach. JOHN COTTER University College Dublin
Modelling catastrophic risk in international equity markets: An extreme value approach JOHN COTTER University College Dublin Abstract: This letter uses the Block Maxima Extreme Value approach to quantify
More informationEXTREME CYBER RISKS AND THE NON-DIVERSIFICATION TRAP
EXTREME CYBER RISKS AND THE NON-DIVERSIFICATION TRAP Martin Eling Werner Schnell 1 This Version: August 2017 Preliminary version Please do not cite or distribute ABSTRACT As research shows heavy tailedness
More informationAnalyzing Oil Futures with a Dynamic Nelson-Siegel Model
Analyzing Oil Futures with a Dynamic Nelson-Siegel Model NIELS STRANGE HANSEN & ASGER LUNDE DEPARTMENT OF ECONOMICS AND BUSINESS, BUSINESS AND SOCIAL SCIENCES, AARHUS UNIVERSITY AND CENTER FOR RESEARCH
More informationSYSM 6304 Risk and Decision Analysis Lecture 2: Fitting Distributions to Data
SYSM 6304 Risk and Decision Analysis Lecture 2: Fitting Distributions to Data M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu September 5, 2015
More informationActuarially Consistent Valuation of Catastrophe Derivatives
Financial Institutions Center Actuarially Consistent Valuation of Catastrophe Derivatives by Alexander Muermann 03-18 The Wharton Financial Institutions Center The Wharton Financial Institutions Center
More informationOptimal Securitization via Impulse Control
Optimal Securitization via Impulse Control Rüdiger Frey (joint work with Roland C. Seydel) Mathematisches Institut Universität Leipzig and MPI MIS Leipzig Bachelier Finance Society, June 21 (1) Optimal
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Moments of a distribubon Measures of
More informationRisk Measurement in Credit Portfolio Models
9 th DGVFM Scientific Day 30 April 2010 1 Risk Measurement in Credit Portfolio Models 9 th DGVFM Scientific Day 30 April 2010 9 th DGVFM Scientific Day 30 April 2010 2 Quantitative Risk Management Profit
More informationIEOR E4602: Quantitative Risk Management
IEOR E4602: Quantitative Risk Management Basic Concepts and Techniques of Risk Management Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com
More informationAn Application of Extreme Value Theory for Measuring Financial Risk in the Uruguayan Pension Fund 1
An Application of Extreme Value Theory for Measuring Financial Risk in the Uruguayan Pension Fund 1 Guillermo Magnou 23 January 2016 Abstract Traditional methods for financial risk measures adopts normal
More informationChapter 4: Asymptotic Properties of MLE (Part 3)
Chapter 4: Asymptotic Properties of MLE (Part 3) Daniel O. Scharfstein 09/30/13 1 / 1 Breakdown of Assumptions Non-Existence of the MLE Multiple Solutions to Maximization Problem Multiple Solutions to
More informationAll Investors are Risk-averse Expected Utility Maximizers
All Investors are Risk-averse Expected Utility Maximizers Carole Bernard (UW), Jit Seng Chen (GGY) and Steven Vanduffel (Vrije Universiteit Brussel) AFFI, Lyon, May 2013. Carole Bernard All Investors are
More informationReinsurance Optimization GIE- AXA 06/07/2010
Reinsurance Optimization thierry.cohignac@axa.com GIE- AXA 06/07/2010 1 Agenda Introduction Theoretical Results Practical Reinsurance Optimization 2 Introduction As all optimization problem, solution strongly
More informationAn Information Based Methodology for the Change Point Problem Under the Non-central Skew t Distribution with Applications.
An Information Based Methodology for the Change Point Problem Under the Non-central Skew t Distribution with Applications. Joint with Prof. W. Ning & Prof. A. K. Gupta. Department of Mathematics and Statistics
More informationAre stylized facts irrelevant in option-pricing?
Are stylized facts irrelevant in option-pricing? Kyiv, June 19-23, 2006 Tommi Sottinen, University of Helsinki Based on a joint work No-arbitrage pricing beyond semimartingales with C. Bender, Weierstrass
More informationI. Maxima and Worst Cases
I. Maxima and Worst Cases 1. Limiting Behaviour of Sums and Maxima 2. Extreme Value Distributions 3. The Fisher Tippett Theorem 4. The Block Maxima Method 5. S&P Example c 2005 (Embrechts, Frey, McNeil)
More informationAggregation and capital allocation for portfolios of dependent risks
Aggregation and capital allocation for portfolios of dependent risks... with bivariate compound distributions Etienne Marceau, Ph.D. A.S.A. (Joint work with Hélène Cossette and Mélina Mailhot) Luminy,
More informationA Bivariate Shot Noise Self-Exciting Process for Insurance
A Bivariate Shot Noise Self-Exciting Process for Insurance Jiwook Jang Department of Applied Finance & Actuarial Studies Faculty of Business and Economics Macquarie University, Sydney Australia Angelos
More informationApproximation of Continuous-State Scenario Processes in Multi-Stage Stochastic Optimization and its Applications
Approximation of Continuous-State Scenario Processes in Multi-Stage Stochastic Optimization and its Applications Anna Timonina University of Vienna, Abraham Wald PhD Program in Statistics and Operations
More informationExtreme Value Analysis for Partitioned Insurance Losses
Extreme Value Analysis for Partitioned Insurance Losses by John B. Henry III and Ping-Hung Hsieh ABSTRACT The heavy-tailed nature of insurance claims requires that special attention be put into the analysis
More informationQuantitative relations between risk, return and firm size
March 2009 EPL, 85 (2009) 50003 doi: 10.1209/0295-5075/85/50003 www.epljournal.org Quantitative relations between risk, return and firm size B. Podobnik 1,2,3(a),D.Horvatic 4,A.M.Petersen 1 and H. E. Stanley
More informationExercises on the New-Keynesian Model
Advanced Macroeconomics II Professor Lorenza Rossi/Jordi Gali T.A. Daniël van Schoot, daniel.vanschoot@upf.edu Exercises on the New-Keynesian Model Schedule: 28th of May (seminar 4): Exercises 1, 2 and
More informationInfinite Horizon Optimal Policy for an Inventory System with Two Types of Products sharing Common Hardware Platforms
Infinite Horizon Optimal Policy for an Inventory System with Two Types of Products sharing Common Hardware Platforms Mabel C. Chou, Chee-Khian Sim, Xue-Ming Yuan October 19, 2016 Abstract We consider a
More informationWeek 2 Quantitative Analysis of Financial Markets Hypothesis Testing and Confidence Intervals
Week 2 Quantitative Analysis of Financial Markets Hypothesis Testing and Confidence Intervals Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg :
More informationLimit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies George Tauchen Duke University Viktor Todorov Northwestern University 2013 Motivation
More informationPricing Volatility Derivatives with General Risk Functions. Alejandro Balbás University Carlos III of Madrid
Pricing Volatility Derivatives with General Risk Functions Alejandro Balbás University Carlos III of Madrid alejandro.balbas@uc3m.es Content Introduction. Describing volatility derivatives. Pricing and
More informationinduced by the Solvency II project
Asset Les normes allocation IFRS : new en constraints assurance induced by the Solvency II project 36 th International ASTIN Colloquium Zürich September 005 Frédéric PLANCHET Pierre THÉROND ISFA Université
More informationZhongyi Yuan, Ph.D., A.S.A.
Zhongyi Yuan, Ph.D., A.S.A. Department of Risk Management Smeal College of Business The Pennsylvania State University 362 Business Building University Park, PA 16802 Office phone: (814) 865-6211 Email:
More informationA Multivariate Analysis of Intercompany Loss Triangles
A Multivariate Analysis of Intercompany Loss Triangles Peng Shi School of Business University of Wisconsin-Madison ASTIN Colloquium May 21-24, 2013 Peng Shi (Wisconsin School of Business) Intercompany
More informationOPTIMAL PORTFOLIO OF THE GOVERNMENT PENSION INVESTMENT FUND BASED ON THE SYSTEMIC RISK EVALUATED BY A NEW ASYMMETRIC COPULA
Advances in Science, Technology and Environmentology Special Issue on the Financial & Pension Mathematical Science Vol. B13 (2016.3), 21 38 OPTIMAL PORTFOLIO OF THE GOVERNMENT PENSION INVESTMENT FUND BASED
More informationOPTIMAL PORTFOLIO CONTROL WITH TRADING STRATEGIES OF FINITE
Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 005 Seville, Spain, December 1-15, 005 WeA11.6 OPTIMAL PORTFOLIO CONTROL WITH TRADING STRATEGIES OF
More informationUnit 5: Sampling Distributions of Statistics
Unit 5: Sampling Distributions of Statistics Statistics 571: Statistical Methods Ramón V. León 6/12/2004 Unit 5 - Stat 571 - Ramon V. Leon 1 Definitions and Key Concepts A sample statistic used to estimate
More informationUnit 5: Sampling Distributions of Statistics
Unit 5: Sampling Distributions of Statistics Statistics 571: Statistical Methods Ramón V. León 6/12/2004 Unit 5 - Stat 571 - Ramon V. Leon 1 Definitions and Key Concepts A sample statistic used to estimate
More informationComputational aspects of risk estimation in volatile markets: A survey
Computational aspects of risk estimation in volatile markets: A survey Stoyan V. Stoyanov EDHEC Business School e-mail: stoyan.stoyanov@edhec-risk.com Svetlozar T. Rachev University of Karlsruhe, KIT,
More informationMODELING AND MANAGEMENT OF NONLINEAR DEPENDENCIES COPULAS IN DYNAMIC FINANCIAL ANALYSIS
MODELING AND MANAGEMENT OF NONLINEAR DEPENDENCIES COPULAS IN DYNAMIC FINANCIAL ANALYSIS Topic 1: Risk Management of an Insurance Enterprise Risk models Risk categorization and identification Risk measures
More information"Pricing Exotic Options using Strong Convergence Properties
Fourth Oxford / Princeton Workshop on Financial Mathematics "Pricing Exotic Options using Strong Convergence Properties Klaus E. Schmitz Abe schmitz@maths.ox.ac.uk www.maths.ox.ac.uk/~schmitz Prof. Mike
More informationAnalysis of truncated data with application to the operational risk estimation
Analysis of truncated data with application to the operational risk estimation Petr Volf 1 Abstract. Researchers interested in the estimation of operational risk often face problems arising from the structure
More informationRisk, Coherency and Cooperative Game
Risk, Coherency and Cooperative Game Haijun Li lih@math.wsu.edu Department of Mathematics Washington State University Tokyo, June 2015 Haijun Li Risk, Coherency and Cooperative Game Tokyo, June 2015 1
More informationCalibration of Interest Rates
WDS'12 Proceedings of Contributed Papers, Part I, 25 30, 2012. ISBN 978-80-7378-224-5 MATFYZPRESS Calibration of Interest Rates J. Černý Charles University, Faculty of Mathematics and Physics, Prague,
More informationAn Approximation for Credit Portfolio Losses
An Approximation for Credit Portfolio Losses Rüdiger Frey Universität Leipzig Monika Popp Universität Leipzig April 26, 2007 Stefan Weber Cornell University Introduction Mixture models play an important
More informationIntegration & Aggregation in Risk Management: An Insurance Perspective
Integration & Aggregation in Risk Management: An Insurance Perspective Stephen Mildenhall Aon Re Services May 2, 2005 Overview Similarities and Differences Between Risks What is Risk? Source-Based vs.
More informationGPD-POT and GEV block maxima
Chapter 3 GPD-POT and GEV block maxima This chapter is devoted to the relation between POT models and Block Maxima (BM). We only consider the classical frameworks where POT excesses are assumed to be GPD,
More informationRohini Kumar. Statistics and Applied Probability, UCSB (Joint work with J. Feng and J.-P. Fouque)
Small time asymptotics for fast mean-reverting stochastic volatility models Statistics and Applied Probability, UCSB (Joint work with J. Feng and J.-P. Fouque) March 11, 2011 Frontier Probability Days,
More informationFolia Oeconomica Stetinensia DOI: /foli A COMPARISON OF TAIL BEHAVIOUR OF STOCK MARKET RETURNS
Folia Oeconomica Stetinensia DOI: 10.2478/foli-2014-0102 A COMPARISON OF TAIL BEHAVIOUR OF STOCK MARKET RETURNS Krzysztof Echaust, Ph.D. Poznań University of Economics Al. Niepodległości 10, 61-875 Poznań,
More information