CIFEr'97 Tutorials - New York, March 23, 1997

[Payman Arabshahi]

     Computational Intelligence in Financial Engineering Conference

                                CIFEr'97
                           March 23-25, 1997
                  Crowne Plaza Manhattan, New York City

                    http://www.ieee.org/nnc/cifer97


Registration information:

Barbara Klemm
CIFEr'97 Secretariat
Meeting Management
2603 Main Street, Suite # 690
Irvine, California 92714

Tel: (714) 752-8205 or
     (800) 321-6338
Fax: (714) 752-7444
Email: Meetingmgt at aol.com


								   TUTORIALS
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     Risk Management

     Jan W. Dash, Ph.D.
     Director
     Quantitative Analysis
     Global Risk Management
     Smith Barney

This tutorial will cover 1) characterization of risks in finance: market
risk (interest rates, FX rates, equity indices, spreads), trading risk,
systems risk (software, hardware, vendors), model risk, and 2)
quantitative measurement of risk: the Greeks (Delta, Gamma, Vega), the
partial Greeks (Ladders), the new Greeks (Exotics), dollars at risk
(n-Sigma analysis), correlations, static scenario analysis, dynamic
scenario analysis, Monte Carlo risk analysis, beginnings of risk
standards, DPG, Risk Metrics, and 3)  case study of risk: the Viacom CVR
Options and 4) pricing and hedging for interest rate derivatives. 

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     An Introduction to OTC Derivatives and Their Applications

     John F. Marshall, Ph.D.
     Executive Director
     International Association of Financial Engineers

This tutorial is for persons with little prior exposure to derivative
instruments.  It will focus on the basic products, how they trade, and how
they are used.  It will be largely non-quantitative.  The tutorial will
examine how derivatives are used by financial engineers for risk
management purposes, investment purposes, cash flow management, and
creating structured securities.  The use of derivatives to circumvent
market imperfections, such as asymmetric taxes and transaction costs, will
also be demonstrated.  The primary emphasis of the tutorial will be swaps
(including interest rate swaps, currency swaps, commodity swaps, equity
swaps, and macroeconomic swaps).  Applications of OTC options, including
caps and floors and digital options will also be examined, but to a lesser
extent.

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     GARCH Modeling of Financial Time Series


     R. Douglas Martin, Ph.D.
     Professor of Statistics, University of Washington
     Chief Scientist, Data Analysis Products Division of MathSoft, Inc.


This tutorial provides an introduction to univariate and multivariate
generalized autoregressive heteroscedastic (GARCH) modeling of financial
returns time series data, with a focus on modeling conditional
volatilities and correlations.  Basic aspects of the various models are
discussed, including: conditions for stationarity, optimization techniques
for maximum likelihood estimation of the models, use of the estimated
conditional standard deviations for value-at-risk calculations and options
pricing, use of conditional correlations in obtaining conditional
volatilities for portfolios.  Examples are provided using the S+GARCH
object-oriented toolkit for GARCH modeling. 

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     Time Series Tools for Finance

     Andreas Wiegend, Ph.D.
     Professor, Stern School of Business, New York University

This tutorial presents a unifying view of the recent advances of
neuro-fuzzy, and other machine learning techniques for time series and
finance.  It is given jointly by Prof. Andreas Wiegend (Stern School of
Business, NYU), and Dr. Georg Zimmerman (Siemens AG, Munich), and presents
both conceptual aspects of time series modeling, specific tricks for
financial engineering problems, and software engineering aspects for
building a trading system. 

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     An Introduction to Evolutionary Computation

     David B. Fogel, PhD
     Chief Scientist, Natural Selection, Inc., La Jolla

Evolutionary computation encompasses a broad field of optimization
algorithms that can be applied to diverse, difficult real-world problems. 
It is particularly useful in addressing stochastic, nonlinear, and
time-varying optimization problems, including those arising in financial
engineering.  This tutorial will provide background on the inspiration,
history, and the practical application of evolutionary computation to
problems typical of those encountered in financial engineering. 

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     Models for Stochastic Volatility:  Some Recent Developments

     Nuno Cato
     Professor, New Jersey Institute of Technology, Newark

     Pedro J. F. de Lima
     Professor, The Johns Hopkins University, Baltimore

In this tutorial, we will firstly discuss the importance of modeling stock
market's volatility.  Secondly, we will review the basic properties of
GARCH- type and SV-type models and some of their most successful
extensions, namely the SWitching ARCH (SWARCH) models.  The performance of
these models will be illustrated with some real data examples.  Thirdly,
we will discuss some problems with the estimation of these models and with
their use for risk forecasting.  Fourthly, we will describe some recent
research and some novel extensions to these models, such as the
Long-Memory Stochastic Volatility (LMSV) and the SWitching Stochastic
Volatility (SWSV) models.  By using examples from recent stock market
behavior we illustrate the capabilities and shortcomings of these new
modeling and forecasting tools. 

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