From: John Conover <john@email.johncon.com>

Subject: CIFE conferences

Date: Tue, 25 Feb 1997 03:20:20 -0800

The IEEE is sponsoring a lot of financial engineering conferences this year. Attached is for the spring ... John BTW, most of the stuff is based on remedial stochastic calculus, and optimization theory. The reason for IEEE's involvement is that most of the stuff came from communications and information theory, ie., Bell Labs from the time of Hartley Information through Shannon Information, circa the 1920's through the late 1940's, (and Kelly's seminal 1957 interpretation of information and uncertainty and how it applied to the financial markets and gambling, which created the field of quantitative financial analysis, ie., programmed trading, or PT[1].) BTW, (I can't resist,) Hartley and Shannon information have a peculiar relation to electronics. The reason that the 2's number system works, (you are reading this on a 2's compliment Boolean algebra machine,) can be explained by Hartley information. (Hartley formalized the fact that information is simply log-base-two of data states.) Shannon's Master's thesis established the isomorphism between switches and the Boolean algebras, of which Hartley information operating on data states is one such. (Probably the most important Master's thesis ever written-it is the theory of digital electronics. You see, your computer can't really add, or subtract, or count-its all done through Boolean algebra processes, which is a set of formulas that gives a "prescription," telling the computer how to add, or subtract, etc.[2]) Right there is the theory of modern computing. And, also, why data and information are different, and why computation and calculation are different. The first machine proposed to do computation, (algorithmic calculation, ie., a process of calculations, can be traced back to the dark ages,) was Turing, in the late 1930's. It was a mathematical machine, and although used in algorithm theory, (because it is the most general information machine known,) but is not very practical for other than theoretical purposes. Von Neumann proposed the first practical implementation of an information machine, (the ENIAC was not a computer, as first designed-it was a programmable calculator. Later modifications made it an information machine, and the EDVAC was the first true specifically designed machine to handle information, as opposed to data.) [1] The concept is simple. If you knew what the stock market was going to do tomorrow, then you could make a mint. You have some concept of what it is going to do, but there is some uncertainty about the future, ie., you do not have exact information about the future. So, how do you handle things in the face of information uncertainty? How about like the error correction modem that you probably received this message on? Same drama, only the cast of characters are different. (No pun intended.) Your disk drive probably had a couple of errors while you were reading this, too. (They are common, but the information machine corrects them.) It too has to retrieve exact data with information uncertainty. (Typically, a Reed-Solomon variant information algorithm is used for this. Astonishingly, a hard disk drive, or a modem, does not have to have all of the data to reconstruct the data in its entirety. The Internet also uses the same information technology-when running hot and heavy, only about a third of the information on the Internet is not corrupt, packet collisions being the main source of the information uncertainty. The study of such things is technically termed Informatics, which is the study of Claude Shannon's information theory.) [2] For example, suppose you want to add two numbers. If you logically OR (ie., logical operation, X or Y,) the two numbers together, (except when both bits are true, in which case you set the bit to zero, and generate a carry,) you have added the two numbers. (The technical term for such a gizmo is a half adder, or an EXCLUSIVE OR operation.) If you want to subtract two numbers, you invert all the bits, (called COMPLEMENTING,) in one number, increment it, and then add the two resulting numbers as above. The result is the subtraction of the numbers. And you have the basic operations of an information machine. Note that it was all done with algebraic operations-with no arithmetic, per se. We can thank Claude Shannon for having the intuition and insight that such things could be done electronically. BTW, a good example of an EXCLUSIVE OR is the light switches on stairs. If booth switches are off the light is off. If both are on, the light is off. Otherwise, it is on, (ie, switch positions different.) It might be just the opposite at your house, depending on how your electrician did things, but you get the idea. If you make a Boolean function that either increments, or decrements, (ie., adds one or subtracts one,) a number, in a random fashion, and plot the history of the number, it is a Brownian walk, fixed increment fractal, that produces information uncertainty-what economists call an ARCH, or GARCH model of financial uncertainty in the markets. For theoretical arguments as to why markets operate that way, see the stuff by Brian Arthur, "Complexity in Economic and Financial Markets," Complexity, 1, pp. 20-25, 1995. Also available from http://www.santafe.edu/arthur. And that is why the electrical engineers and quantitative economists, called "quants," are getting in bed together. ------- start of digest (6 messages) (RFC 934 encapsulation) ------- From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on Models for Stochastic Volatility - NY, March 23, 1997 Date: 25 Feb 1997 01:57:36 GMT Message-ID: <5etgug$g9e@nntp1.u.washington.edu> IEEE/IAFE Computational Intelligence in Financial Engineering Conference CIFEr'97 March 23-25, 1997 Crowne Plaza Manhattan, New York City http://www.ieee.org/nnc/cifer97 Registration information: Barbara Klemm CIFEr'97 Secretariat Meeting Management 2603 Main Street, Suite # 690 Irvine, California 92714 Tel: (714) 752-8205 or (800) 321-6338 Fax: (714) 752-7444 Email: Meetingmgt@aol.com Tutorial 6 of 6 - ---------------------------------------------------------------------------- Models for Stochastic Volatility: Some Recent Developments Nuno Cato Professor, New Jersey Institute of Technology, Newark Pedro J. F. de Lima Professor, The Johns Hopkins University, Baltimore In this tutorial, we will firstly discuss the importance of modeling stock market's volatility. Secondly, we will review the basic properties of GARCH- type and SV-type models and some of their most successful extensions, namely the SWitching ARCH (SWARCH) models. The performance of these models will be illustrated with some real data examples. Thirdly, we will discuss some problems with the estimation of these models and with their use for risk forecasting. Fourthly, we will describe some recent research and some novel extensions to these models, such as the Long-Memory Stochastic Volatility (LMSV) and the SWitching Stochastic Volatility (SWSV) models. By using examples from recent stock market behavior we illustrate the capabilities and shortcomings of these new modeling and forecasting tools. - ---------------------------------------------------------------------------- ------------------------------ From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on Time Series Tools for Finance - NY, March 23, 1997 Date: 25 Feb 1997 01:56:55 GMT Message-ID: <5etgt7$g7a@nntp1.u.washington.edu> IEEE/IAFE 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@aol.com Tutorial 4 of 6 - ---------------------------------------------------------------------------- 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. - ---------------------------------------------------------------------------- ------------------------------ From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on GARCH Time Series Modeling - NY, March 23, 1997 Date: 25 Feb 1997 01:56:33 GMT Message-ID: <5etgsh$g78@nntp1.u.washington.edu> IEEE/IAFE Computational Intelligence in Financial Engineering Conference CIFEr'97 March 23-25, 1997 Crowne Plaza Manhattan, New York City http://www.ieee.org/nnc/cifer97 Registration information: Barbara Klemm CIFEr'97 Secretariat Meeting Management 2603 Main Street, Suite # 690 Irvine, California 92714 Tel: (714) 752-8205 or (800) 321-6338 Fax: (714) 752-7444 Email: Meetingmgt@aol.com Tutorial 3 of 6 - ---------------------------------------------------------------------------- GARCH Modeling of Financial Time Series R. Douglas Martin, Ph.D. Professor of Statistics, University of Washington Chief Scientist, Data Analysis Products Division of MathSoft, Inc. This tutorial provides an introduction to univariate and multivariate generalized autoregressive heteroscedastic (GARCH) modeling of financial returns time series data, with a focus on modeling conditional volatilities and correlations. Basic aspects of the various models are discussed, including: conditions for stationarity, optimization techniques for maximum likelihood estimation of the models, use of the estimated conditional standard deviations for value-at-risk calculations and options pricing, use of conditional correlations in obtaining conditional volatilities for portfolios. Examples are provided using the S+GARCH object-oriented toolkit for GARCH modeling. - ---------------------------------------------------------------------------- ------------------------------ From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on Evolutionary Computation - NY, March 23, 1997 Date: 25 Feb 1997 01:57:13 GMT Message-ID: <5etgtp$g9c@nntp1.u.washington.edu> IEEE/IAFE 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@aol.com Tutorial 5 of 6 - ---------------------------------------------------------------------------- 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. - ---------------------------------------------------------------------------- ------------------------------ From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on OTC Derivatives - NY, March 23, 1997 Date: 25 Feb 1997 01:56:02 GMT Message-ID: <5etgri$g72@nntp1.u.washington.edu> IEEE/IAFE Computational Intelligence in Financial Engineering Conference CIFEr'97 March 23-25, 1997 Crowne Plaza Manhattan, New York City http://www.ieee.org/nnc/cifer97 Registration information: Barbara Klemm CIFEr'97 Secretariat Meeting Management 2603 Main Street, Suite # 690 Irvine, California 92714 Tel: (714) 752-8205 or (800) 321-6338 Fax: (714) 752-7444 Email: Meetingmgt@aol.com Tutorial 2 of 6 - ---------------------------------------------------------------------------- An Introduction to OTC Derivatives and Their Applications John F. Marshall, Ph.D. Executive Director International Association of Financial Engineers This tutorial is for persons with little prior exposure to derivative instruments. It will focus on the basic products, how they trade, and how they are used. It will be largely non-quantitative. The tutorial will examine how derivatives are used by financial engineers for risk management purposes, investment purposes, cash flow management, and creating structured securities. The use of derivatives to circumvent market imperfections, such as asymmetric taxes and transaction costs, will also be demonstrated. The primary emphasis of the tutorial will be swaps (including interest rate swaps, currency swaps, commodity swaps, equity swaps, and macroeconomic swaps). Applications of OTC options, including caps and floors and digital options will also be examined, but to a lesser extent. - ---------------------------------------------------------------------------- ------------------------------ From: payman@u.washington.edu (Payman Arabshahi) To: John Conover <john@email.johncon.com> Subject: CIFEr'97 Tutorial on Risk Management - NY, March 23, 1997 Date: 25 Feb 1997 01:55:15 GMT Message-ID: <5etgq3$g6e@nntp1.u.washington.edu> IEEE/IAFE 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@aol.com Tutorial 1 of 6 - ---------------------------------------------------------------------------- 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. - ---------------------------------------------------------------------------- ------- end ------- -- John Conover, john@email.johncon.com, http://www.johncon.com/

Copyright © 1997 John Conover, john@email.johncon.com. All Rights Reserved. Last modified: Fri Mar 26 18:55:07 PST 1999 $Id: 970225032112.9860.html,v 1.0 2001/11/17 23:05:50 conover Exp $