Modeling of Variance and Volatility Swaps for Financial Markets with Stochastic Volatility Anatoliy Swishchuk Department of Mathematics & Statistics, York.

Slides:



Advertisements
Similar presentations
Explicit Option Pricing Formula for Mean-Reverting Asset Anatoliy Swishchuk Math & Comp Finance Lab Dept of Math & Stat, U of C MITACS Project Meeting.
Advertisements

Change of Time Method in Mathematical Finance Anatoliy Swishchuk Mathematical & Computational Finance Lab Department of Mathematics & Statistics University.
Mean-Reverting Models in Financial and Energy Markets Anatoliy Swishchuk Mathematical and Computational Finance Laboratory, Department of Mathematics and.
Mean-Reverting Models in Financial and Energy Markets Anatoliy Swishchuk Mathematical and Computational Finance Laboratory, Department of Mathematics and.
Change of Time Method in Mathematical Finance Anatoliy Swishchuk Mathematical & Computational Finance Lab Department of Mathematics & Statistics University.
Stability of Financial Models Anatoliy Swishchuk Mathematical and Computational Finance Laboratory Department of Mathematics and Statistics University.
Change of Time Method: Applications to Mathematical Finance. II. Anatoliy Swishchuk Math & Comp Finance Lab Dept of Math & Stat, U of C “Lunch at the Lab”
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
Paper Review: “On the Pricing and Hedging of Volatility Derivatives” by S. Howison, A. Rafailidis and H. Rasmussen (Applied Mathematical Finance J., 2004)
Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.
Chapter 21 Value at Risk Options, Futures, and Other Derivatives, 8th Edition, Copyright © John C. Hull 2012.
Nonparametric estimation of conditional VaR and expected shortfall.
Chapter 21 Value at Risk Options, Futures, and Other Derivatives, 8th Edition, Copyright © John C. Hull 2012.
Some Financial Mathematics. The one-period rate of return of an asset at time t. where p t = the asset price at time t. Note: Also if there was continuous.
Paper Review:"New Insight into Smile, Mispricing, and Value at Risk: The Hyperbolic Model" by E. Eberlein, U. Keller and K. Prause (1998). Anatoliy Swishchuk.
Pricing Derivative Financial Products: Linear Programming (LP) Formulation Donald C. Williams Doctoral Candidate Department of Computational and Applied.
Derivation of Black - Scholes Formula by Change of Time Method Anatoliy Swishchuk Mathematical and Computational Finance Laboratory, Department of Mathematics.
Financial Markets with Stochastic Volatilities Anatoliy Swishchuk Mathematical and Computational Finance Lab Department of Mathematics & Statistics University.
CAPM and the Characteristic Line. The Characteristic Line  Total risk of any asset can be assessed by measuring variability of its returns  Total risk.
1 Def: Let and be random variables of the discrete type with the joint p.m.f. on the space S. (1) is called the mean of (2) is called the variance of (3)
AILEEN WANG PERIOD 5 An Analysis of Dynamic Applications of Black-Scholes.
Numerical Methods for Option Pricing
Introduction to Volatility Models From Ruey. S. Tsay’s slides.
Chrif YOUSSFI Global Equity Linked Products
Modelling and Pricing of Variance Swaps for Stochastic Volatility with Delay Anatoliy Swishchuk Mathematical and Computational Finance Laboratory Department.
Empirical Financial Economics 4. Asset pricing and Mean Variance Efficiency Stephen Brown NYU Stern School of Business UNSW PhD Seminar, June
Volatility Models Fin250f: Lecture 5.2 Fall 2005 Reading: Taylor, chapter 9.
Empirical Evidence on Security Returns
Volatility Chapter 9 Risk Management and Financial Institutions 2e, Chapter 9, Copyright © John C. Hull
1 CHAPTER 14 FORECASTING VOLATILITY II Figure 14.1 Autocorrelograms of the Squared Returns González-Rivera: Forecasting for Economics and Business, Copyright.
Change of Time Method: Application to Mathematical Finance. I. Anatoliy Swishchuk Math & Comp Finance Lab Dept of Math & Stat, U of C ‘Lunch at the Lab’
Estimating High Dimensional Covariance Matrix and Volatility Index by making Use of Factor Models Celine Sun R/Finance 2013.
Financial Risk Management of Insurance Enterprises Stochastic Interest Rate Models.
Problem With Volatility MMA 707 Analytical Finance I Lecturer: Jan Röman Members : Bo He Xinyan Lin.
Risk Management and Financial Institutions 2e, Chapter 13, Copyright © John C. Hull 2009 Chapter 13 Market Risk VaR: Model- Building Approach 1.
Ewa Lukasik - Jakub Lawik - Juan Mojica - Xiaodong Xu.
Financial Risk Management of Insurance Enterprises
18th Inter-Institute Seminar, September 2011, Budapest, Hungary 1 J. Lógó, D. B. Merczel and L. Nagy Department of Structural Mechanics Budapest.
Pricing the Convexity Adjustment Eric Benhamou a Wiener Chaos approach.
Survey of Local Volatility Models Lunch at the lab Greg Orosi University of Calgary November 1, 2006.
Option Pricing Montecarlo Method, Path Integrals A.Chiarugi, G. Cipriani, M.Rosa-Clot, S.Taddei Firenze E. Bennati Dip Scienze Econ. (Pisa) G.Lotti Dip.
Book Review: ‘Energy Derivatives: Pricing and Risk Management’ by Clewlow and Strickland, 2000 Chapter 3: Volatility Estimation in Energy Markets Anatoliy.
Testing Distributions of Stochastically Generated Yield Curves Gary G Venter AFIR Seminar September 2003.
A 1/n strategy and Markowitz' problem in continuous time Carl Lindberg
Mortality Regimes and Pricing Samuel H. Cox University of Manitoba Yijia Lin University of Nebraska - Lincoln Andreas Milidonis University of Cyprus &
Fundamentals of Futures and Options Markets, 5 th Edition, Copyright © John C. Hull Value at Risk Chapter 18.
Tutorial th Nov. Outline Hints for assignment 3 Score of assignment 2 (distributed in class)
Determinants of Capital Structure Choice: A Structural Equation Modeling Approach Cheng F. Lee Distinguished Professor of Finance Rutgers, The State University.
Actuarial Applications of Multifractal Modeling
CHAPTER SEVEN Risk, Return, and Portfolio Theory J.D. Han.
Some calculations with exponential martingales Wojciech Szatzschneider School of Actuarial Sciences Universidad Anáhuac México Norte Mexico.
© The MathWorks, Inc. ® ® Monte Carlo Simulations using MATLAB Vincent Leclercq, Application engineer
© K.Cuthbertson, D. Nitzsche FINANCIAL ENGINEERING: DERIVATIVES AND RISK MANAGEMENT (J. Wiley, 2001) K. Cuthbertson and D. Nitzsche Lecture Asset Price.
Aileen Wang Period 5 An Analysis of Dynamic Applications of Black-Scholes.
Fang-Bo Yeh, Dept. of Mathematics, Tunghai Univ.2004.Jun.29 1 Financial Derivatives The Mathematics Fang-Bo Yeh Mathematics Department System and Control.
Explicit Option Pricing Formula for A Mean-Reverting Asset Anatoliy Swishchuk “Lunch at the Lab” Talk March 10, 2005.
Aileen Wang Period 5 Computer Systems Lab 2010 TJSTAR June 3, 2010 An Analysis of Dynamic Applications of Black-Scholes.
Monte-Carlo Simulations Seminar Project. Task  To Build an application in Excel/VBA to solve option prices.  Use a stochastic volatility in the model.
 Asset Models in Life Assurance - Views from the Stochastic Accreditation Working Party 17 November 2003 Faculty of Actuaries Sessional Meeting.
Chapter 23 Volatility. Copyright © 2006 Pearson Addison-Wesley. All rights reserved Introduction Implied volatility Volatility estimation Volatility.
© K. Cuthbertson and D. Nitzsche Chapter 9 Measuring Asset Returns Investments.
1 Application of Moment Expansion Method to Options Square Root Model Yun Zhou Advisor: Dr. Heston.
Applications of Stochastic Processes in Asset Price Modeling Preetam D’Souza.
5. Volatility, sensitivity and VaR
Value at Risk and Expected Shortfall
Anatoliy Swishchuk Mathematical and Computational Finance Laboratory
Financial Risk Management of Insurance Enterprises
Market Risk VaR: Model-Building Approach
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
Figure 6.1 Risk as Function of Number of Stocks in Portfolio
Presentation transcript:

Modeling of Variance and Volatility Swaps for Financial Markets with Stochastic Volatility Anatoliy Swishchuk Department of Mathematics & Statistics, York University, Toronto, ON, Canada Seminar-April 15, 2004 Department of Statistics, University of Toronto

Outline Introduction Stochastic Volatility Model: Heston (1993) Model Solution of the Volatility Equation Property of the Solution Variance and Volatility Swaps Calculation of Expectation and Variance Covariance and Correlation Swaps Numerical Example: S&P60 Canada Index

Introduction Cox, Ingersoll &Ross (CIR) (1985)-stochastic variance model; Heston (1993)-asset price has variance that follows a CIR model; Brockhaus & Long (2000)-calculation expectation and variance for volatility swap using analytical approach; He & Wang (RBC Financial Group) (2002)- proposed deterministic volatility for variance and volatility swaps: Query Note for the 6 th IPSW PIMS, Vancouver, UBC, May 2002

Stochastic Volatility Model

Explicit Solution for Variance

Properties of the Process

Properties of Variance

Variance Swaps

Volatility Swaps

Calculation E[V]

Calculation of Var[V]

Calculation of Var[V] (continuation)

Calculation of E[V] and Var[V] in Discrete Case (sketch)

Calculation of E[V] and Var[V] in Discrete Case (sketch) (continuation )

Covariance and Correlation Swaps

Pricing Covariance and Correlation Swaps

Valuing of Covariance Swap

Calculation Covariance for S1 and S2

Calculation Covariance for S1 and S2 (continuation I)

Calculation Covariance for S1 and S2 (continuation II)

Calculation Covariance Swap for S1 and S2

Numerical Example: S&P60 Canada Index

Statistics on Log-Returns of S&P60 Canada Index for 5 years ( )

Estimation of the GARCH(1,1) Process

Generating Different Input Variables for the Volatility Swap Model

Continuation (Numerical Example )

Figure 1: Convexity Adjustment

Figure 2: S&P60 Canada Index Volatility Swap

Some References

Some References (continuation)