University Finance Seminar - Lent Term, 2000
Friday, 3 March 2000
Held jointly by the Faculties of Mathematics and of Economics and
Politics
and the Judge Institute of Management Studies in the University of
Cambridge.
Professor Terry Lyons and Dr William Shaw will be speaking on Implied
Volatility between 4.15 and 6.15 pm in Lecture Theatre 1 at the Judge
Institute of Management Studies in the University of Cambridge.
All welcome
PROGRAMME
4.00 pm Tea
4.15 pm Professor Terry Lyons, Imperial College & Oxford University
Volatility of implied volatility
It seems common sense that the optimal portfolio designed to hedge an
exotic
derivative contract should contain freely traded call options as well as
the
underlying stock and the numeraire. However, standard stochastic
volatility
models are not complete, and so do not in general give hedging
information.
We point out that there are quite reasonable models for the "volatility
of
implied volatility" and that such models are complete providing they
satisfy
a consistency condition. Experimental data does not contradict, and in
some
parts confirms this approach. We hope that in the long term it will lead
to
effective approaches to protecting portfolios from volatility risk.
5.15 pm Dr William Shaw, Nomura, London and Balliol College, Oxford
Implied volatility and numerical instability
In any modelling process the calculation of an observable effect is a
mapping
from the space of parameters associated with the theory to the space of
observable parameters. The form of the mapping may range from an
explicit
formula through to an intensive numerical calculation. The inference of
theoretical parameters from observations represents an inversion of such
a
mapping and it is necessary to be careful to establish when the
inversion
represents a process that is both well-defined and stable.
The inverse function theorem is a critical element of the inversion
process
when the mapping is non-linear.
In option pricing an inversion of common interest is the computation of
implied volatility from market price data. This talk will explore the
consequences of the failure and near-failure of the inverse function
theorem
as applied to volatility for some simple options of interest. I will
argue
that except in very limited circumstances, the implied volatility may
well be
meaningless.
6.15 pm Drinks
Please refer any queries to 01223 339641 or by fax on 01223 339652
Mary Jane Jerkins
PA to Professor M A H Dempster
Judge Institute of Management Studies
Trumpington Road
Cambridge CB2 1AG
Tel: 01223 339641
Fax: 01223 339652
-------------------------------
NEW BOOK
Option Valuation under Stochastic Volatility : with Mathematica Code
by Alan L. Lewis / Published February 1, 2000
Book Description
This book provides an advanced treatment of option pricing for traders,
money
managers, and researchers. Providing largely original research not
available
elsewhere, it covers the latest generation of option models where both
the
stock price and its volatility follow diffusion processes. These new
models
help explain important features of real-world option pricing, including
the
"volatility smile" pattern. The book includes Mathematica code and 37
illustrations.
Table of Contents
Preface
Historical Volatility of the S&P 500 Index
1. Introduction and Summary of Results
Summary of Results
The Hedging Argument of Black and Scholes
The Drift Cancellation and Option Sensitivities
The Hedging Argument under Stochastic Volatility
The Martingale Approach
App. 1.1 Parameter Estimators for the GARCH Diffusion Model
App. 1.2 Solutions to PDEs
2. The Fundamental Transform
Assumptions
The Transform-based Solution
Some Models with Closed-form Solutions
Analytic Characteristic Functions
A Bond Price Analogy and Option Price Bound
App. 2.1 Recovery of the Black and Scholes Solution
App. 2.2 Mathematica Code for Chapter 2
App. 2.3 General Properties of Option Prices
3. The Volatility of Volatility Series Expansion
Assumptions
General Steps in the expansion
The Two Series for a Parameterized Model
App. 3.1 Details of the Volatility of Volatility Expansion
4. Mixing Solutions and Applications
The Basic Mixing Solution
Connection between Mixing Densities and the Fundamental Transform
A Monte Carlo Application
Arbitrary Payoff Functions
A More General Model without Correlation
5. The Smile
Introduction and Summary of Results
The Symmetric Case
The Correlated Case
Deducing the Risk-adjusted Volatility Process from Option Prices
App. 5.1 Calculating Volatility Moments
App. 5.2 Working with Differential Operators in Mathematica
App. 5.3 Additional Mathematica Code for Chapter 5
App. 5.4 Calculating with the Mixing Theorem
6. The Term Structure of Implied Volatility
Deterministic Volatility
Deterministic Volatility II: a Transform Perspective
Stochastic Volatility—The Eigenvalue Connection
Example I: The Square Root Model
Example II: The 3/2 Model
Example III: The GARCH Diffusion Model
A Variational Principle Method
A Differential Equation (Dsolve) Method
App. 6.1 Mathematica Code for Chapter 6
7. Utility-based Equilibrium Models
A Representative Agent Economy
Examples
The Pure Investment Problem with a Distant Planning Horizon
Preference Adjustments to the Volatility of Volatility Series
Expansion
The Effect of Risk Attitudes on Option Prices
8. Duality and Changes of Numeraire
Put-Call Duality
Introduction to the Change of Numeraire
Mathematics of the Change of Numeraire
Implications for the Term Structure
9. Volatility Explosions and the
Failure of the Martingale Pricing Formula
Introduction
The Feller Boundary Classifications
Volatility Explosions I
Volatility Explosions II. Failure of the Martingale Pricing Formula
When Martingale Pricing Fails: Generalized Pricing Formulas
Generalized Pricing Formulas and the Transform-based Solutions
Generalized Pricing Formulas. Example I: the 3/2 Model
Generalized Pricing Formulas. Example II: the CEV Model
10. Option Prices at Large Volatility
Introduction
Asymptotica for the Fundamental Transform
11. Solutions to Models
The Square Root Model
The 3/2 Model
Geometric Brownian Motion
References
Index
Frequent Notations and Abbreviations
About the Author
Alan Lewis has been active in option valuation and financial research
for
over 20 years. He served as the Director of Research, Chief Investment
Officer, and President of the mutual fund family for a money manager
specializing in derivative securities. He has published articles in many
of
the leading financial journals including: The Journal of Business, The
Journal of Finance, The Financial Analysts Journal, and Mathematical
Finance.
He received a Ph.D. in physics from the University of California at
Berkeley
and a B.S. from Caltech.
---------------
Many other new books on Mathematical Finance you'll find at the web site
FinMath.com @ Chicago
Financial Engineering & Risk Management Workshop
Alexander Adamchuk
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