The CGMYmodel Finance seminar by Mari Hodnekvam supervised by Prof.Korn.

Slides:



Advertisements
Similar presentations
Chapter 3 Properties of Random Variables
Advertisements

Gibbs Sampling Methods for Stick-Breaking priors Hemant Ishwaran and Lancelot F. James 2001 Presented by Yuting Qi ECE Dept., Duke Univ. 03/03/06.
Statistics review of basic probability and statistics.
Chapter 5 Discrete Random Variables and Probability Distributions
© 2002 Prentice-Hall, Inc.Chap 5-1 Basic Business Statistics (8 th Edition) Chapter 5 Some Important Discrete Probability Distributions.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Statistics.
Continuous Probability Distributions.  Experiments can lead to continuous responses i.e. values that do not have to be whole numbers. For example: height.
CPSC 531:Random-Variate Generation
Chapter 8 Random-Variate Generation
Chapter 8 Random-Variate Generation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation.
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.
Chapter 4 Discrete Random Variables and Probability Distributions
Jump to first page STATISTICAL INFERENCE Statistical Inference uses sample data and statistical procedures to: n Estimate population parameters; or n Test.
Random-Variate Generation. Need for Random-Variates We, usually, model uncertainty and unpredictability with statistical distributions Thereby, in order.
Relationship Between Sample Data and Population Values You will encounter many situations in business where a sample will be taken from a population, and.
Probability Densities
Introduction to Volatility Models From Ruey. S. Tsay’s slides.
Statistics Are Fun! Analysis of Variance
1 Module 9 Modeling Uncertainty: THEORETICAL PROBABILITY MODELS Topics Binomial Distribution Poisson Distribution Exponential Distribution Normal Distribution.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Statistics.
Lec 6, Ch.5, pp90-105: Statistics (Objectives) Understand basic principles of statistics through reading these pages, especially… Know well about the normal.
Probability and Statistics Review
The Lognormal Distribution MGT 4850 Spring 2008 University of Lethbridge.
Random-Variate Generation. 2 Purpose & Overview Develop understanding of generating samples from a specified distribution as input to a simulation model.
A Summary of Random Variable Simulation Ideas for Today and Tomorrow.
OMS 201 Review. Range The range of a data set is the difference between the largest and smallest data values. It is the simplest measure of dispersion.
The moment generating function of random variable X is given by Moment generating function.
© 2001 Prentice-Hall, Inc.Chap 5-1 BA 201 Lecture 8 Some Important Discrete Probability Distributions.
1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.
Multivariate Probability Distributions. Multivariate Random Variables In many settings, we are interested in 2 or more characteristics observed in experiments.
Lecture 28 Dr. MUMTAZ AHMED MTH 161: Introduction To Statistics.
INTRODUCTION TO MAXIMUM LIKELIHOOD METHODS FOR ECOLOGY MIDIS NUMÉRIQUES APRIL 3 RD 2014 ALYSSA BUTLER.
Diffusion Processes and Ito’s Lemma
Describing Data: Numerical
Chapter 5 Statistical Models in Simulation
PROBABILITY & STATISTICAL INFERENCE LECTURE 3 MSc in Computing (Data Analytics)
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 8-1 Confidence Interval Estimation.
Continuous Distributions The Uniform distribution from a to b.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Basic Business Statistics.
IE 429, Parisay, January 2010 What you need to know from Probability and Statistics: Experiment outcome: constant, random variable Random variable: discrete,
Advanced Risk Management I Lecture 7 Non normal returns and historical simulation.
Simulation Example: Generate a distribution for the random variate: What is the approximate probability that you will draw X ≤ 1.5?
Lévy copulas: Basic ideas and a new estimation method J L van Velsen, EC Modelling, ABN Amro TopQuants, November 2013.
Selecting Input Probability Distribution. Simulation Machine Simulation can be considered as an Engine with input and output as follows: Simulation Engine.
ETM 607 – Random-Variate Generation
Expectation. Let X denote a discrete random variable with probability function p(x) (probability density function f(x) if X is continuous) then the expected.
ENGR 610 Applied Statistics Fall Week 2 Marshall University CITE Jack Smith.
Monte-Carlo Simulations Seminar Project. Task  To Build an application in Excel/VBA to solve option prices.  Use a stochastic volatility in the model.
School of Information Technologies Poisson-1 The Poisson Process Poisson process, rate parameter e.g. packets/second Three equivalent viewpoints of the.
CONTINUOUS RANDOM VARIABLES
Chapter 8 Random-Variate Generation Banks, Carson, Nelson & Nicol Discrete-Event System Simulation.
Psychology 202a Advanced Psychological Statistics September 10, 2015.
ISMT253a Tutorial 1 By Kris PAN Skewness:  a measure of the asymmetry of the probability distribution of a real-valued random variable 
Section 5 – Expectation and Other Distribution Parameters.
1 Day 1 Quantitative Methods for Investment Management by Binam Ghimire.
Lecture 3 Types of Probability Distributions Dr Peter Wheale.
© 2007 Thomson Brooks/Cole, a part of The Thomson Corporation. FIGURES FOR CHAPTER 8 ESTIMATION OF PARAMETERS AND FITTING OF PROBABILITY DISTRIBUTIONS.
Chap 5-1 Discrete and Continuous Probability Distributions.
1 Opinionated in Statistics by Bill Press Lessons #15.5 Poisson Processes and Order Statistics Professor William H. Press, Department of Computer Science,
Chapter 4 Discrete Random Variables and Probability Distributions
Selecting Input Probability Distributions. 2 Introduction Part of modeling—what input probability distributions to use as input to simulation for: –Interarrival.
Random number generation
MAT 446 Supplementary Note for Ch 3
The Normal Curve and Sampling Error
CONTINUOUS RANDOM VARIABLES
Statistics Branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. Practice or science of.
Initial Analysis Siyu Zheng.
Chapter 3 Statistical Concepts.
MSFT GE About the Stocks April 16, 1997 – January 25, days
Random Variables A random variable is a rule that assigns exactly one value to each point in a sample space for an experiment. A random variable can be.
Presentation transcript:

The CGMYmodel Finance seminar by Mari Hodnekvam supervised by Prof.Korn

Introduction of the process; parameters, characteristic function, moments, distribution etc. Simulation of the process Application in finance Extended version Today

Introduction of the process; parameters, characteristic function, moments, distribution etc. Simulation of the process Application in finance Extended version Today

VG CGMY Relationship to VG

The parameter Y Y < 0Finite activity 0 ≤ Y ≤ 1Infinte activity, finite variation 1 ≤ Y < 2Infinte activity, infintite variation

Interpretation of parameters CMeasure of averall level of activity GMeasure of skewness M YMeasure of fine structure

Density of the CGMY-model

The characteristic function

Moments – variance = – skewness = – kurtosis =

Introduction of the process; parameters, characteristic function, moments, distribution etc. Simulation of the process Application in finance Extended version Today

Simulation of the CGMY-process Idea: treat the jumps as compound Poisson process and sample from its Lévy density Problem: for infintite activity Lévy processes the jump arrival rate is infinite

Simulation of the CGMY-process Divide the simulation into three parts: – Negative large jumps, x < -ε – Positive large jumps, x > ε – Small jumps, -ε < x < ε

Simulation of the CGMY-process The algorithm Simulate the number of positive and negative jumps in the time interval by a Poisson process Simulate the large jumps by using the acceptance-rejection method

Simulation of the CGMY-process Acceptance-rejection method: Find a function f(x) whose value is close to those of the Lévy density function for every x Draw samples from the probability distribution function of f(x); F(x) The samples are then either accepted or rejected, when you test them towards a restriction

Simulation of the CGMY-process The algorithm Simulate the number of positive and negative jumps in the time interval by a Poisson process Simulate the large jumps by using the acceptance- rejection method Simulate the small jumps by

Simulation of the CGMY-process The algorithm Simulate the number of positive and negative jumps in the time interval by a poisson process Simulate the large jumps by using the acceptance-rejection method Simulate the small jumps by Return the simulated jumps

Simulation of the CGMY-process

Introduction of the process; parameters, characteristic function, moments, distribution etc. Simulation of the process Application in finance Extended version Today

The CGMY stock price process – stock price process: – extended stock price process: – extended CGMY model:

Diffusion term

Density fit

Introduction of the process; parameters, characteristic function, moments, distribution etc. Simulation of the process Application in finance Extended version Today

Summary Pure jump process Parameter Y Kurtosis, skewness Time change, volatility clustering