Applications are now open for the IX St. Petersburg Spring School in Risk Management, Insurance, and Finance. On March 2–4, 2020 Department of Economics of the European University at St. Petersburg will host Karim Barigou (ISFA), Zinoviy Landsman (University of Haifa) and Daniël Linders (University of Illinois / University of Amsterdam).
The official Certificates for Professional Development will be issued.
The participation fee for all three days is the following:
- For students and PhD students 1000 Russian Rubles;
- For university teachers and academicians 5000 Russian Rubles;
- For employees of supervisory agencies 5000 Russian Rubles;
- For others 10 000 Russian Rubles.
If the participants plan to join for one or two days only, the participation fee is the following:
- For students and PhD students 400 Russian Rubles per day;
- For university teachers and academicians 2000 Russian Rubles per day;
- For employees of supervisory agencies 2000 Russian Rubles per day;
- For others 4 000 Russian Rubles per day.
The working language of the School is English.
The deadlines for the registration are the following:
- January 15, for those who need a Russian visa;
- February 10, for the Russian citizens and foreigners who do not need a Russian visa.
To register, please send an e-mail to Elena Koltsova (ekoltsova@eu.spb.ru).
The Programme
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March 2nd, 2020
Fair valuation of insurance liabilities: by Karim Barigou, ISFA (Institut de Science Financière et d’Assurances), France |
Short CV
Karim Barigou holds a Master degree in Actuarial Science (ULB) and a Ph.D. in Actuarial Science from KU Leuven, where he worked under the guidance of Prof. Jan Dhaene. Currently, he is postdoctoral researcher in the Actuarial Research Group of ISFA, part of the University Lyon 1 in France. His main research interests are at the interplay between life insurance and quantitative finance, including derivative pricing, longevity modelling and risk management of retirement products. During his PhD, he worked on market-consistent valuation of life insurance liabilities and was mainly involved in developing new valuations methods which merge market-consistency and actuarial judgement.
Summary of the Course
In this short course, we investigate the integrated financial – actuarial valuation of liabilities related to an insurance policy or portfolio in a single period framework. We define a fair valuation as a valuation which is both financial market-consistent (mark-to market for any hedgeable part of a claim) and actuarial model-consistent (mark-to-model for any claim that is independent of financial market evolutions). We introduce the class of hedge-based valuations, where in a first step of the valuation process, a «best hedge» for the liability is set up, based on the traded assets in the market, while in a second step, the remaining part of the claim is valuated via an actuarial model. We also introduce the class of two-step valuations, where, in the inner step, a claim is first actuarially valuated, conditional on the market information that will be available at the moment the claim is due. This inner step leads to a financial derivative. In the outer step, one determines the financial market price of this financial derivative. We show that the classes of fair, hedge-based and two-step valuations are identical. The theoretical content will be illustrated by many simple examples and exercises. We will also discuss the extensions to a dynamic multi-period framework taking into account time-consistent considerations.
References
Dhaene J., Stassen B., Barigou K., Linders D., Chen Z. (2017) Fair valuation of insurance liabilities: Merging actuarial judgment and market-consistency. Insurance: Mathematics and Economics, 76:14–27.
Barigou K., Chen Z., Dhaene J. (2019). Fair dynamic valuation of insurance liabilities: Merging actuarial judgement with market- and time-consistency. Insurance: Mathematics and Economics, 88:19-29.
Delong Ł., Dhaene J., Barigou K. (2019). Fair valuation of insurance liability cash-flow streams in continuous time: Theory. Insurance: Mathematics and Economics, 88:196-208.
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March 3rd, 2020
Elliptical distributions by Zinoviy Landsman, Actuarial Research Center, University of Haifa |
Short CV
Professor Zinoviy Landsman obtained his Master Degree in the Department of Theory of Probability and Mathematical Statistics, Tashkent State University, Tashkent, former USSR, and his PhD degree in Statistics at Romanovsky Mathematical Institute, Department of Mathematical Statistics, Academy of Sciences Uzb. SSR, Tashkent. From 1991 he is working on the Actuarial Program of the Department of Statistics, University of Haifa. Now he is a Director of the Actuarial Research Center of University of Haifa. Several times he was a guest visitor of Risk and Actuarial Studies School of University of New South Wales (UNSW), Sydney, Australia. His research interest includes many fields of Actuarial and Financial Science: Credibility Theory, Risk Measures and Premium Principles, Multivariate Dependence Structures, Portfolio Allocation under VaR, Tail VaR and other risk measures, Optimization of Nonlinear functionals under a system of equality constraints and Optimal Portfolio Management, Option Price Theory for symmetrical distributions of log returns. He published about 90 articles, most of them in the top Actuarial journals such as Insurance: Mathematics and Economics (IME), Scandinavian Actuarial Journal, ASTIN Bulletin, North American Actuarial Journal (NAAJ) and others. Now he is being an Associate Editor of IME journal and the academic member of Israel Institute of Actuaries. Several times he won a research grants of American Actuarial Society (SoA) and Israel Science Foundation.
Summary of the Course
Significant changes in the insurance and financial markets are giving increasing attention to the need for developing a standard framework for risk measurement. Recently, there has been growing interest among insurance and investment experts to focus on the use of a Tail Conditional Expectation (TCE) because it shares properties that are considered desirable and applicable in a variety of situations. It is derived explicit formulas for computing TCE for elliptical distributions, a family of symmetric distributions which includes the more familiar normal and Student t-distributions. It extends this investigation to multivariate elliptical distributions allowing us to model combinations of correlated risks. We are able to exploit properties of these distributions naturally permitting us to decompose the conditional expectation so that we are able to allocate contribution of individual risks to the aggregated risks.
While the above considered TCE risk measure provides a risk manager with information about the average of the tail of the loss distribution, Tail Variance (TV) risk measure estimates the variability along such a tail. We derive explicit expressions for TV and risk capital decomposition rules based on them, in the general framework of multivariate elliptical distributions. This class is very popular among actuaries and risk managers because it contains distributions with marginals whose tails are heavier than those of normal distributions. This distinctive feature is desirable when modelling actuarial and financial datasets.
The considered risk measures (TCE, TV and others) being univariate unfortunately suffer annoying shortage: they are not reflexed the dependence structure of the multivariate vector of risks, in particular, elliptical dependence structure. It is proposed the multivariate extensions of the tail risk measures called Multivariate Tail Condition Expectation (MTCE) and Multivariate Tail Covariance (MTCov) and others. These measures have the attractive forms in the framework of elliptical family.
Problem of the optimal portfolio selection of stock returns in the context of tail risk measures leads to the problem of minimizing a combination of a linear functional and a square root of a quadratic functional for the case of elliptical multivariate underlying distributions. In this course of lectures, we consider the problem of optimal portfolio selection within the class of translation-invariant and positive homogeneous (TIPH) risk measures, popular in actuarial and financial theory. The results are illustrated using the data of stocks from NASDAQ/Computers.
References
Furman, E., Landsman, Z. (2006). Tail variance premium with applications for elliptical portfolio of risks. ASTIN Bulletin, 36(2):433–462.
Ignatieva, K., Landsman, Z. (2015). Estimating the tails of loss severity via conditional risk measures for the family of symmetric generalized hyperbolic distributions. Insurance: Mathematics and Economics. 65:172–186.
Ignatieva, K., Landsman, Z. (2019). Conditional Tail Risk Measures for Skewed Generalised Hyperbolic Family. Insurance: Mathematics and Economics, 86:98–114.
Landsman, Z., Makov, U. (2010). Translation invariant and positive homogeneous risk measures and optimal portfolio management. European Journal of Finance, 17(4):307–320.
Landsman, Z. (2010). On the tail mean-variance optimal portfolio selection. Insurance: Mathematics and Economics, 46:547–553.
Landsman, Z., Makov, U. (2012). Translation-invariant and positive homogeneous risk measures and optimal portfolio management in the presence of a riskless component. Insurance: Mathematics and Economics, 50(1):94–98.
Landsman, Z., Valdez, E. (2003). Tail Conditional Expectations for Elliptical Distributions. North American Actuarial Journal, 7:55–71.
Landsman, Z., Makov, U., Shushi, T. (2016). Multivariate tail conditional expectation for elliptical distributions. Insurance: Mathematics and Economics, 70:216–223.
Landsman, Z., Makov, U., Shushi T. (2018). A multivariate tail covariance measure for elliptical distributions. Insurance: Mathematics and Economics, 81:27–35.
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March 4th, 2020
Measuring option-implied dependence by Daniël Linders, University of Illinois / University of Amsterdam |
Short CV
Dr. Daniël Linders is an Assistant Professor of Mathematics at the University of Illinois and of the University of Amsterdam. He obtained his Ph.D. from the KU Leuven, Belgium, in 2013 under the supervision of Professor Jan Dhaene. He has been a postdoctoral researcher for the KU Leuven, University of Amsterdam and the Technical University of Munich. He worked in insurance and banking sectors and provided consulting services to various insurance companies in life, non-life and health. His research interests include multivariate derivative pricing, dependence modeling and the development and risk management of retirement products. He published in actuarial science and quantitative finance, and has obtained grants from the Society of Actuaries, the Flanders Research Institute, the AXA research institute and the KU Leuven Research Fund.
Summary of the Course
Never put all your eggs in one basket. Investors are well-aware of this advice and prefer to diversify their portfolios and compose a blend of different assets to invest in. The extent to which market participants can benefit from this diversification benefit is determined by the strength of the co-movement between the asset prices, also referred to as the degree of herd behavior. This degree of herd behavior is changing over time. Moreover, it is well-documented that in a period of increased market fear, asset prices are moving more strongly together. As a result, the diversification one initially hopes for, is evaporating when it is needed the most. Therefore, having a notion about today’s degree of herd behavior is of utmost importance because it gives market participants the opportunity to take the necessary, cautionary, actions.
Determining today's diversification level is a challenging task. An estimate based on historical time series will result in a backwards looking measure. Prices of traded index and vanilla options contain information about the market's view about the future movements of the financial market. Backing out today’s degree of herd behavior from these traded derivatives results in a forward looking estimate of the future degree of herd behavior, also called the implied degree of herd behavior.
This workshop will explore different methodologies for determining the implied degree of herd behavior. At the moment, the only quoted index for the degree of herd behavior between asset prices is the CBOE Implied Correlation Index (Tickers: JCJ, KCJ). We will introduce a model-free alternative, called the Herd Behavior Index (HIX). The HIX is an implied estimate for the 30 days implied degree of herd behavior.
Outline
1. Basket option pricing: a model-free approach.
2. The Herd Behavior Index.
3. Implied Correlation vs. HIX.
References
Dhaene, J., Linders, D., Schoutens, W., Vyncke, D. (2012). The herd behavior index: a new measure for the implied degree of co-movement in stock markets. Insurance: Mathematics and Economics, 50(3):357–370.
Dhaene, J., Dony, J., Forys, M., Linders, D., Schoutens, W. (2012). FIX: The Fear Index — Measuring market fear. Topics in Numerical Methods for Finance, Cummins M. et al. (eds.). Springer Proceedings in Mathematics and Statistics.
Linders, D., Schoutens, W. (2016). Basket option pricing and implied correlation in a one-factor Lévy model. Proceedings of the conference: Challenges in Derivatives Markets.
Linders, D., Stassen, B. (2016). The multivariate Variance Gamma model: basket option pricing and calibration. Quantitative Finance, 16(4):555–572.
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The Organising Committee
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Contact Ms Elena Koltsova Department of Economics E-mail: ekoltsova@eu.spb.ru Phone/fax: +7 (812) 386-7632 |
