< SUNY Korea Financial Engineering Seminar >
1. Subject : Market Crashes and Modeling Volatile Markets
2. Date & Time : 2012. 1. 12(Thu) ~ 1. 14(Sat)
(Thu, Fri - 19:00~21:30, Sat - 10:00~15:00)
3. Location : Korea Institute of Financial Investment Seminar room (
4. Host : SUNY Korea, CEWIT Korea
5. Sponsorship : Financial News
6. The number of attendance : 30
7. Seminar for : Portfolio Managers, Risk Managers, Quantitative Analysts,
Treasurers, Asset Managers, Consultants & Advisors,
CFOs and CIOs: Banks, Hedge Funds, Pension Funds,
Endowments & Foundations, Funds of Funds, Family Offices,
Corporate Treasuries, Insurance Companies, Students who concern
8. Entrance fee: free (It is free to enroll because of sponsorship)
9. a lecturer : Dr. Svetlozar (Zari) Rachev
He is one of the world foremost authorities in the application of
heavy-tailed distributions in finance, author of 14 books and over 300
published articles on finance, econometrics, probability, statistics and
actuarial science. He holds a Ph.D. from Lomonosove University and
Doctor of Science Degree from Steklov Mathematical Institute (Moscow).
(find more information : http://www.ams.sunysb.edu/~rachev/ )
Call : (032) 626-1552 Mr. Kwon (email@example.com)
Application : Click Here!
※ If you don't answer the call, you may not consider to enter the seminar.
Market Crashes and Modeling Volatile Markets
- Svetlozar (Zari) Rachev
Stock market crashes like those in October 1987 and October 1997, the turbulent period around the Asian Crisis in 1998 through 1999 or the burst of the “dotcom bubble” together with the severely volatile period after September 11, 2001 is a constant reminder to financial engineers and risk managers of how often extreme events actually happen in real-world financial markets. These events have led to increased efforts to improve the flexibility and statistical reliability of existing models to capture the dynamics of economic/financial variables.
In this presentation, we will discuss general frameworks (1) to price options, (2) to measure risk, and (3) to construct optimal portfolios. The economic ideas underlying the model come from three stylized facts about real-world financial markets. First, observed financial return series are asymmetric and heavy tailed, where the tails are important because bad news are tail events. The normal distribution is symmetric and has too light tails to match market data, but generally infinitely divisible distributions introduce heavier tails and skewness. Second, there is volatility clustering in time series (i.e., calm periods followed by highly volatile periods and vice versa). Finally, a dependence structure for risk factors is non-linear and asymmetric. Hence linear and symmetric correlation coefficients cannot describe the dependence structure.
In searching for an acceptable model to describe these three stylized facts, we address the following three issues. In the first part, we present some parametric distributions with asymmetric and heavy tailed properties, including the a-stable and a few subclasses of tempered stable distributions. In the second part, we present a GARCH model with infinitely divisible distributed innovation and subclasses of that GARCH model that incorporates volatility clustering together with excess leptokurtosis and asymmetry for the residual distribution. In the last part, we discuss multivariate models by means of the multi-tail elliptical distributions and the copula representation of dependence for a multivariate distribution.
This is a joint talk with Young Shin (Aaron) Kim, Frank Fabozzi and Boryana Racheva-Yotova.
Prof. Dr. Svetlozar (Zari) Rachev
Frey Family Foundation Chair-Professor,
Applied Mathematics and Statistics,
Stony Brook University
Chief Scientist, FinAnalytica Inc.
Dr. habil Young Shin (Aaron) Kim
Department of Statistics, Econometrics and Mathematical Finance
School of Economics and Business Engineering
Karlsruhe Institute of Technology (KIT), Germany
Prof. Dr. Frank Fabozzi
EDHEC Business School
New York, NY, USA
Dr. Boryana Racheva-Yotova
Lecture 1 (January 12, 2012, 7:00-9:30 pm)
Risk Management with non-Gaussian fat-tailed models
This lecture focuses on quantitative methods for risk management. The lecture will cover regression analysis, applications to the Capital Asset Pricing Model, multifactor pricing models, principal components analysis, statistical methods for financial time series, value at risk, average value at risk, and modeling of stochastic volatilities. Various non-Gaussian fat-tailed models applied to measuring risk will be discussed with numerical methods. Moreover, we will discuss risk monitoring, early warning signals for market structural breaks (crashes), risk budgeting, portfolio optimization and hedging.
Lecture 2 (January 13, 2012, 7:00-9:30 pm)
Option pricing under the non-Gaussian Levy process model
In this lecture, we will present derivative pricing theory under the non-Gaussian Levy process model. The equivalent martingale measures, the generalized Girsanov Theorem, the Radon-Nikodym Derivative, and more general understanding of the Arbitrage Theorem will be discussed. Numerical implementation for those models will be also provided.
Lecture 3 (January 14, 2012, 10:00 am – 12:00 pm)
Portfolio Optimization with non-Gaussian fat-tailed models
In this lecture, we will discuss portfolio optimization under a non-Gaussian fat-tailed multivariate model with stochastic volatility. The multivariate model can be defined by a mixture of the multivariate normal distribution and the tempered stable subordinator. The GARCH model with fat-tailed innovations is also applied to the multivariate model. We will discuss the portfolio optimization minimizing value at risk and average value at risk.
Lecture 4 (January 14, 2012, 1:00 pm – 3:00 pm)
Option pricing under the non-Gaussian Levy process model with stochastic volatility
We will provide further developments of derivative pricing theory in this lecture. We will review with under the non-Gaussian Levy process model discussed in Lecture 2. After then we will discuss the stochastic volatility model together with the non-Gaussian Levy process model. The three classes of stochastic volatility models will be presented: (1) GARCH model with tempered stable innovation, (2) Time changed Levy process with tempered stable subordinators, (3) Regime switching model with tempered stable driving process.
We will give examples based on our work on market crashes, the math theory will be hidden: we will talk on investments and finance in volatilile (non-Gaussain) markets, namely
(a) risk management ;
(b) asset valuation ;
(c) factor models and forecasting market crashes;
(d) risk monitoring and risk budgeting;
(e) portfolio optimization;