The Lognormal Distribution MGT 4850 Spring 2008 University of Lethbridge.

Slides:



Advertisements
Similar presentations
Modeling of Data. Basic Bayes theorem Bayes theorem relates the conditional probabilities of two events A, and B: A might be a hypothesis and B might.
Advertisements

The Normal Distribution
JMB Chapter 6 Part 1 v4 EGR 252 Spring 2012 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
Chapter 14 The Black-Scholes-Merton Model
Valuing Stock Options: The Black-Scholes-Merton Model.
MGT 821/ECON 873 Volatility Smiles & Extension of Models
Statistical Review for Chapters 3 and 4 ISE 327 Fall 2008 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including:
Continuous Random Variable (1). Discrete Random Variables Probability Mass Function (PMF)
Engineering Probability and Statistics - SE-205 -Chap 3 By S. O. Duffuaa.
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
Chapter 14 The Black-Scholes-Merton Model Options, Futures, and Other Derivatives, 8th Edition, Copyright © John C. Hull
Discrete and Continuous Distributions G. V. Narayanan.
The Lognormal Distribution
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
CIS 2033 based on Dekking et al. A Modern Introduction to Probability and Statistics, 2007 Instructor Longin Jan Latecki Chapter 7: Expectation and variance.
Valuing Stock Options:The Black-Scholes Model
1 The Black-Scholes-Merton Model MGT 821/ECON 873 The Black-Scholes-Merton Model.
The Pricing of Stock Options Using Black- Scholes Chapter 12.
Exercises of computational methods in finance
Simulating the value of Asian Options Vladimir Kozak.
Chapter 5 Statistical Models in Simulation
CH12- WIENER PROCESSES AND ITÔ'S LEMMA
Chapter 13 Wiener Processes and Itô’s Lemma
Black Scholes Option Pricing Model Finance (Derivative Securities) 312 Tuesday, 10 October 2006 Readings: Chapter 12.
JMB Ch6 Lecture2 Review EGR 252 Spring 2011 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
Continuous Distributions The Uniform distribution from a to b.
Valuing Stock Options: The Black- Scholes Model Chapter 11.
MONTE CARLO SIMULATION. Topics History of Monte Carlo Simulation GBM process How to simulate the Stock Path in Excel, Monte Carlo simulation and VaR.
1 Chapter 19 Monte Carlo Valuation. 2 Simulation of future stock prices and using these simulated prices to compute the discounted expected payoff of.
Valuation of Asian Option Qi An Jingjing Guo. CONTENT Asian option Pricing Monte Carlo simulation Conclusion.
Discrete Probability Distributions. Random Variable Random variable is a variable whose value is subject to variations due to chance. A random variable.
Cox, Ross & Rubenstein (1979) Option Price Theory Option price is the expected discounted value of the cash flows from an option on a stock having the.
“ Building Strong “ Delivering Integrated, Sustainable, Water Resources Solutions Statistics 101 Robert C. Patev NAD Regional Technical Specialist (978)
Probability & Statistics I IE 254 Summer 1999 Chapter 4  Continuous Random Variables  What is the difference between a discrete & a continuous R.V.?
11.1 Introduction to Futures and Options Markets, 3rd Edition © 1997 by John C. Hull The Pricing of Stock Options Using Black- Scholes Chapter 11.
Fundamentals of Futures and Options Markets, 7th Ed, Ch 13, Copyright © John C. Hull 2010 Valuing Stock Options: The Black-Scholes-Merton Model Chapter.
Option Pricing BA 543 Aoyang Long. Agenda Binomial pricing model Black—Scholes model.
More Continuous Distributions
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Binomial Distribution Derivation of the Estimating Formula for u an d ESTIMATING u AND d.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Statistics. A two-dimensional random variable with a uniform distribution.
Monte-Carlo Simulations Seminar Project. Task  To Build an application in Excel/VBA to solve option prices.  Use a stochastic volatility in the model.
IE 300, Fall 2012 Richard Sowers IESE. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples.
Chapter 18 The Lognormal Distribution. Copyright © 2006 Pearson Addison-Wesley. All rights reserved The Normal Distribution Normal distribution.
Valuing Stock Options:The Black-Scholes Model
Chapter 19 Monte Carlo Valuation. Copyright © 2006 Pearson Addison-Wesley. All rights reserved Monte Carlo Valuation Simulation of future stock.
6.4 Application of the Normal Distribution: Example 6.7: Reading assignment Example 6.8: Reading assignment Example 6.9: In an industrial process, the.
Lecture 3 Types of Probability Distributions Dr Peter Wheale.
Unit 4 Review. Starter Write the characteristics of the binomial setting. What is the difference between the binomial setting and the geometric setting?
Chap 5-1 Chapter 5 Discrete Random Variables and Probability Distributions Statistics for Business and Economics 6 th Edition.
Chapter 14 The Black-Scholes-Merton Model 1. The Stock Price Assumption Consider a stock whose price is S In a short period of time of length  t, the.
Chapter 13 Wiener Processes and Itô’s Lemma 1. Stochastic Processes Describes the way in which a variable such as a stock price, exchange rate or interest.
Statistics -Continuous probability distribution 2013/11/18.
Chapter 14 The Black-Scholes-Merton Model
Introduction to Probability - III John Rundle Econophysics PHYS 250
4-1 Continuous Random Variables 4-2 Probability Distributions and Probability Density Functions Figure 4-1 Density function of a loading on a long,
Chapter 19 Monte Carlo Valuation.
Wiener Processes and Itô’s Lemma
The normal distribution
The Pricing of Stock Options Using Black-Scholes Chapter 12
Random Variables and Probability Distribution (2)
Chapter 15 The Black-Scholes-Merton Model
Valuing Stock Options: The Black-Scholes-Merton Model
Monte Carlo Valuation Bahattin Buyuksahin, Celso Brunetti 12/8/2018.
Distributions and Densities: Gamma-Family and Beta Distributions
Sampling Distributions
The lognormal distribution
Valuing Stock Options:The Black-Scholes Model
Presentation transcript:

The Lognormal Distribution MGT 4850 Spring 2008 University of Lethbridge

Binomial Option Pricing Computational, not analytic closed-form solution – solution can be expressed analytically in terms of certain "well-known" functions (e.g. BSOPM) To develop a formula we need assumptions in this case about the statistical properties of the underlying stock prices.

Overview What constitute “reasonable” assumptions about stock prices Lognormal distribution as a reasonable distribution Simulation of lognormal prices

Stock Price Characteristics The Stock Price is uncertain Changes are continuous The stock price is never 0 or negative The average return tends to increase Uncertainty increases with time

Stock Price Paths Wiggly lines Lines are continuous solid with no jumps Lines are positive Average increases with time Standard deviation increases with time

examples

Definition the log-normal distribution is the probability distribution of any random variable whose logarithm is normally distributed. If X is a random variable with a normal distribution, then exp(X) or e X has a log-normal distribution; likewise, if Y is log-normally distributed, then log(Y) is normally distributed.

Lognormal Distribution probability density function (pdf)probability density function

Lognormal Distribution

lognormal The expected value isexpected value – and the variance isvariance –

Lognormal distribution

Normal distribution pdf

Random number Generation

Simulating lognormal prices Requires VBA skills (optional) Also skip 18.3 Geometric diffusions Calculating the parameters of the lognormal distribution

Lognormal mean and sigma