Write legibly. Unreadable answers are worthless.
|
|
- May Joseph
- 5 years ago
- Views:
Transcription
1 MMF 2021 Final Exam 1 December This is a closed-book exam: no books, no notes, no calculators, no phones, no tablets, no computers (of any kind) allowed. Do NOT turn this page over until you are TOLD to start. Duration of the exam: 3 hours. The exam consists of 8 pages, including this one. Make sure you have all 8 pages. The exam consists of 5 questions. Answer all 5 questions in the exam booklets provided. The mark for each question is listed at the start of the question. Please fill-in ALL the information requested on the front cover of EACH exam booklet that you use. The exam was written with the intention that you would have ample time to complete it. You will be rewarded for concise well-thought-out answers, rather than long rambling ones. We seek quality rather than quantity. Moreover, an answer that contains relevant and correct information as well as irrelevant or incorrect information will be awarded fewer marks than one that contains the same relevant and correct information only. Write legibly. Unreadable answers are worthless. Page 1 of 8 pages.
2 1. [10 marks: 5 marks for each part] Consider the two expression where log is the natural logarithm to the base e. x + 1 x for x > 0 (1) log(x 2 + x) log(x 2 ) for x > 0 (2) Both of these expressions suffer from catastrophic cancellation when x is large. (a) For each expression, (1) and (2), find a mathematically equivalent expression that does not suffer from catastrophic cancellation. (b) Consider the two expressions you found in part (a) above. In each case, either i. explain why you believe your new expression in part (a) is accurate in a relative error sense when x is large (i.e., when the corresponding expression (1) or (2) is not accurate), or ii. briefly explain why you believe your new expression in part (a) is not accurate in a relative error sense when x is large and give a series that you can use to approximate the expression (1) or (2) that is accurate in a relative error sense when x is large. Briefly explain why you believe your series solution is accurate. Your explanations in parts (a) and (b) do not need to be a proofs just give a convincing explanation of why you believe your expressions are accurate or inaccurate in a relative error sense when x is large and specify what you mean by x is large in this context. Page 2 of 8 pages.
3 2. [5 marks] Let X Poisson(λ), a Poisson random variable with rate λ. Hence, λ λk P(X = k) = e k! for k = 0, 1, 2,... There are several ways to generate a Poisson random variable, but suppose we choose to use the inverse transform method (sometimes called the inverse CDF method). Write a MatLab-like program segment that uses the inverse transform method to generate a X Poisson(λ). You can use the MatLab function rand to generate U Unif[0, 1], a uniform random variable in the interval [0, 1]. Note: your program does not have to be syntactically correct MatLab. It just has to be close enough to MatLab so that it is clear what you want your program to do. Page 3 of 8 pages.
4 3. [5 marks] In the course lectures, we discussed the acceptance-rejection method for continuous random varaibles. However, you can extend the acceptance-rejection method to discrete random variables as follows. Suppose X is a discrete random variable with probability mass function (pmf) P(X = k) = p(k) for k = 0, 1, 2,... Assume there is another discrete random variable Y with pmf P(Y = k) = q(k) for k = 0, 1, 2,... In addition, assume you can generate a Y with pmf q and that there is a constant c < such that p(k) c q(k) for k = 0, 1, 2,... The acceptance-rejection method to generate a discrete random variable X with pmf p is as follows. 1. Generate a discrete random variable Y with pmf q. 2. Generate U Unif[0, 1] independent from Y. 3. If U p(y ) c q(y ) then Set X = Y and return X else Go to step 1 and repeat the procedure end if Prove that the random variable X returned by the algorithm above has pmf p. That is, prove P(X = k) = p(k) for k = 0, 1, 2,... Page 4 of 8 pages.
5 4. [10 marks: 5 marks for each part] Consider a European down-and-in call option for a stock with price S t which satisfies the SDE ds t = r S t dt + σ S t dw t (3) in the risk neutral world, where S 0 = 100 is the initial stock price at time t = 0, r = 0.02 is the risk-free interest rate, σ = 0.2 is the volatility, W t is a standard Brownian motion, T = 0.5 is the time to expiry, K = 100 is the strike price, and B = 80 is the barrier. The option payoff at time T is where and (S T K) + = max(s T K, 0). 1 {St B} (S T K) + { 1 if St B for some t [0, T ] 1 {St B} = 0 otherwise Standard Monte Carlo simulation will not work very well for pricing this option, because most of the sample paths will result in a payoff of 0. To improve the efficiency of the Monte Carlo simulation, consider using importance sampling. In particular, consider changing the SDE that generates the sample path S t from (3) to ds t = µ 1 S t dt + σs t dw t (4) for t [0, 0.25] and for t (0.25, T ], where, as specified above, T = 0.5. ds t = µ 2 S t dt + σs t dw t (5) (a) Discuss how you would choose µ 1 and µ 2 in the SDEs (4) and (5), respectively, so that a significant portion of the the Monte Carlo sample paths generated from (4) and (5) are in the money (i.e., have a positive payoff at time T ). (b) When we talked about importance sampling in class, we used the notation E f [h(x)] = h(x)f(x) dx = h(x) f(x) g(x) dx g(x) [ = E g h(x) f(x) ] g(x) For the importance sampling method described above, what are the functions h(x), f(x) and g(x)? Hint: x is a vector in this case, not a scalar as it is in Question 2 on Assignment 4. Suggestion: if there are some details of your method that you cannot work out, say what is required at that point. Page 5 of 8 pages.
6 5. [15 marks: 5 marks for each part] In this course this year, we considered numerical methods for PDEs in time and one space dimension. However, PDEs in time and several space dimensions arise frequently in mathematical finance. In previous years, we considered how to generalize our results for numerical methods for PDEs in time and one space dimension to PDEs in time and two space dimensions. We consider an example of this generalization briefly in this question. To this end, consider the Heat Equation in time and two space dimensions: u t (t, x, y) = u xx (t, x, y) + u yy (t, x, y) (6) for t (0, T ], x (0, 1) and y (0, 1), with initial condition u(0, x, y) = u 0 (x, y) (7) for x [0, 1] and y [0, 1] and homogeneous (i.e., zero) boundary conditions for t (0, T ] and y [0, 1] and u(t, 0, y) = u(t, 1, y) = 0 (8) u(t, x, 0) = u(t, x, 1) = 0 (9) for t (0, T ] and x [0, 1]. To ensure that the initial condition matches the boundary conditions, we also assume that for x [0, 1] and y [0, 1]. u 0 (0, y) = u 0 (1, y) = 0 u 0 (x, 0) = u 0 (x, 1) = 0 We can discretize the PDE (6) by letting t = T/K, x = 1/(M + 1) and y = 1/(N + 1), where K, M and N are positive integers. Then we can set u k m,n = u(k t, m x, n y) for k = 0, 1,..., K, m = 0, 1,..., M + 1, n = 0, 1,..., N + 1, and let ũ k m,n be our approximation to u k m,n computed by the Fully Implicit Method ũ k m,n ũ k 1 m,n t = ũk m+1,n 2ũ k m,n + ũ k m 1,n ( x) 2 + ũk m,n+1 2ũ k m,n + ũ k m,n 1 ( y) 2 (10) for k = 1, 2,..., K, m = 1, 2,..., M and n = 1, 2,..., N, where ũ 0 m,n = u 0 (m x, n y) (11) Page 6 of 8 pages.
7 for m = 0, 1,..., M + 1 and n = 0, 1,..., N + 1, for k = 1, 2,..., K and n = 0, 1,..., N + 1, for k = 1, 2,..., K and m = 0, 1,..., M + 1. ũ k 0,n = ũ k M+1,n = 0 (12) ũ k m,0 = ũ k m,n+1 = 0 (13) (a) Show that the Fully Implicit Method (10) with initial and boundary conditions (11) (13) is consistent with the Heat Equation (6) with initial and boundary conditions (7) (9) of order 1 in t and order 2 in x and y. That is, show that the truncation error associated with (10) is O( t) + O(( x) 2 ) + O(( y) 2 ). To show that the Fully Implicit Method (10) is stable, we first introduce some notation. The tensor product of two matrices A and B is defined to be the block matrix a 1,1 B a 1,2 B a 1,n B a 2,1 B a 2,2 B a 2,n B A B = (14)... a m,1 B a m,2 B... a m,n B where A is an m n matrix with elements a i,j for i = 1, 2,..., m and j = 1, 2,..., n. If B is an m n matrix with elements b i,j for i = 1, 2,..., m and j = 1, 2,..., n, then each a i,j B in A B is a block of elements of size m n (i.e., the same size as B) with element (i, j ) in this block being a i,j b i,j. Note that A B is of dimension m m n n. It is a straightforward computation to verify that, if A is an m n matrix, B is an m n matrix, C is an n p matrix and D is an n p matrix, then (A B)(C D) = (AC) (BD) (15) Since we can consider vectors to be matrices of dimension n 1, the result (15) can be extended to (A B)(u v) = (Au) (Bv) (16) where u is an n-vector and v is an n -vector. You don t have to prove either (15) or (16). Just accept that they are true. Page 7 of 8 pages.
8 (b) For each k = 0, 1,..., K, let ũ k = [ũ k 1,1, ũ k 2,1,..., ũ k M,1, ũ k 1,2, ũ k 2,2,..., ũ k M,2,..., ũ k 1,N, ũ k 2,N,..., ũ k M,N] T be the MN-vector we get by ordering the points in the first row of the x y grid from left to right, followed by the points in the second row of the x y grid ordered from left to right, and so on. Show that the Fully Implicit Method (10) can be written as ( I N I M t ( x) 2 I N A M t ( y) 2 A N I M ) ũ k = ũ k 1 where I M is the M M identity matrix, I N is the N N identity matrix, A M is the M M tridiagonal matrix with 2 on the main diagonal and 1 on the diagonal above the main diagonal as well as 1 on the diagonal below the main diagonal, and A N is the N N tridiagonal matrix with 2 on the main diagonal and 1 on the diagonal above the main diagonal as well as 1 on the diagonal below the main diagonal. In MatLab notation, e M = ones(m, 1); A M = spdiags([e M, 2 e M, e M ], 1 : 1, M, M); e N = ones(n, 1); A N = spdiags([e N, 2 e N, e N ], 1 : 1, N, N); (c) Use (17) (even if you were not able to prove it) as well as (16) to show that the Fully Implicit Method is stable for all t > 0, x > 0 and y > 0. [Hint: recall that the eigenvalues of A M and A N are all negative real numbers. (17) Have a Happy Holiday Page 8 of 8 pages.
King s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More informationKing s College London
King s College London University Of London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority
More informationComputational Finance
Path Dependent Options Computational Finance School of Mathematics 2018 The Random Walk One of the main assumption of the Black-Scholes framework is that the underlying stock price follows a random walk
More informationIEOR E4703: Monte-Carlo Simulation
IEOR E4703: Monte-Carlo Simulation Generating Random Variables and Stochastic Processes Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com
More informationNEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 MAS3904. Stochastic Financial Modelling. Time allowed: 2 hours
NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 Stochastic Financial Modelling Time allowed: 2 hours Candidates should attempt all questions. Marks for each question
More informationMonte Carlo Methods for Uncertainty Quantification
Monte Carlo Methods for Uncertainty Quantification Abdul-Lateef Haji-Ali Based on slides by: Mike Giles Mathematical Institute, University of Oxford Contemporary Numerical Techniques Haji-Ali (Oxford)
More informationUQ, STAT2201, 2017, Lectures 3 and 4 Unit 3 Probability Distributions.
UQ, STAT2201, 2017, Lectures 3 and 4 Unit 3 Probability Distributions. Random Variables 2 A random variable X is a numerical (integer, real, complex, vector etc.) summary of the outcome of the random experiment.
More information- 1 - **** d(lns) = (µ (1/2)σ 2 )dt + σdw t
- 1 - **** These answers indicate the solutions to the 2014 exam questions. Obviously you should plot graphs where I have simply described the key features. It is important when plotting graphs to label
More informationAMH4 - ADVANCED OPTION PRICING. Contents
AMH4 - ADVANCED OPTION PRICING ANDREW TULLOCH Contents 1. Theory of Option Pricing 2 2. Black-Scholes PDE Method 4 3. Martingale method 4 4. Monte Carlo methods 5 4.1. Method of antithetic variances 5
More informationLecture 4. Finite difference and finite element methods
Finite difference and finite element methods Lecture 4 Outline Black-Scholes equation From expectation to PDE Goal: compute the value of European option with payoff g which is the conditional expectation
More informationExam Quantitative Finance (35V5A1)
Exam Quantitative Finance (35V5A1) Part I: Discrete-time finance Exercise 1 (20 points) a. Provide the definition of the pricing kernel k q. Relate this pricing kernel to the set of discount factors D
More informationIEOR E4703: Monte-Carlo Simulation
IEOR E4703: Monte-Carlo Simulation Simulating Stochastic Differential Equations Martin Haugh Department of Industrial Engineering and Operations Research Columbia University Email: martin.b.haugh@gmail.com
More informationNumerical Methods in Option Pricing (Part III)
Numerical Methods in Option Pricing (Part III) E. Explicit Finite Differences. Use of the Forward, Central, and Symmetric Central a. In order to obtain an explicit solution for the price of the derivative,
More informationMonte Carlo Simulations
Monte Carlo Simulations Lecture 1 December 7, 2014 Outline Monte Carlo Methods Monte Carlo methods simulate the random behavior underlying the financial models Remember: When pricing you must simulate
More informationMASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS.
MASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS May/June 2006 Time allowed: 2 HOURS. Examiner: Dr N.P. Byott This is a CLOSED
More informationQuasi-Monte Carlo for Finance
Quasi-Monte Carlo for Finance Peter Kritzer Johann Radon Institute for Computational and Applied Mathematics (RICAM) Austrian Academy of Sciences Linz, Austria NCTS, Taipei, November 2016 Peter Kritzer
More informationSimulating Stochastic Differential Equations
IEOR E4603: Monte-Carlo Simulation c 2017 by Martin Haugh Columbia University Simulating Stochastic Differential Equations In these lecture notes we discuss the simulation of stochastic differential equations
More informationMOUNTAIN RANGE OPTIONS
MOUNTAIN RANGE OPTIONS Paolo Pirruccio Copyright Arkus Financial Services - 2014 Mountain Range options Page 1 Mountain Range options Introduction Originally marketed by Société Générale in 1998. Traded
More informationLecture Notes 6. Assume F belongs to a family of distributions, (e.g. F is Normal), indexed by some parameter θ.
Sufficient Statistics Lecture Notes 6 Sufficiency Data reduction in terms of a particular statistic can be thought of as a partition of the sample space X. Definition T is sufficient for θ if the conditional
More informationM.I.T Fall Practice Problems
M.I.T. 15.450-Fall 2010 Sloan School of Management Professor Leonid Kogan Practice Problems 1. Consider a 3-period model with t = 0, 1, 2, 3. There are a stock and a risk-free asset. The initial stock
More informationMATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS
MATH307/37 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS School of Mathematics and Statistics Semester, 04 Tutorial problems should be used to test your mathematical skills and understanding of the lecture material.
More informationGenerating Random Variables and Stochastic Processes
IEOR E4703: Monte Carlo Simulation Columbia University c 2017 by Martin Haugh Generating Random Variables and Stochastic Processes In these lecture notes we describe the principal methods that are used
More informationLecture 3. Sergei Fedotov Introduction to Financial Mathematics. Sergei Fedotov (University of Manchester) / 6
Lecture 3 Sergei Fedotov 091 - Introduction to Financial Mathematics Sergei Fedotov (University of Manchester) 091 010 1 / 6 Lecture 3 1 Distribution for lns(t) Solution to Stochastic Differential Equation
More informationComputer Exercise 2 Simulation
Lund University with Lund Institute of Technology Valuation of Derivative Assets Centre for Mathematical Sciences, Mathematical Statistics Fall 2017 Computer Exercise 2 Simulation This lab deals with pricing
More information2.1 Mathematical Basis: Risk-Neutral Pricing
Chapter Monte-Carlo Simulation.1 Mathematical Basis: Risk-Neutral Pricing Suppose that F T is the payoff at T for a European-type derivative f. Then the price at times t before T is given by f t = e r(t
More informationDRAFT. 1 exercise in state (S, t), π(s, t) = 0 do not exercise in state (S, t) Review of the Risk Neutral Stock Dynamics
Chapter 12 American Put Option Recall that the American option has strike K and maturity T and gives the holder the right to exercise at any time in [0, T ]. The American option is not straightforward
More informationM5MF6. Advanced Methods in Derivatives Pricing
Course: Setter: M5MF6 Dr Antoine Jacquier MSc EXAMINATIONS IN MATHEMATICS AND FINANCE DEPARTMENT OF MATHEMATICS April 2016 M5MF6 Advanced Methods in Derivatives Pricing Setter s signature...........................................
More informationEconomathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t
Economathematics Problem Sheet 1 Zbigniew Palmowski 1. Calculate Ee X where X is a gaussian random variable with mean µ and volatility σ >.. Verify that where W is a Wiener process. Ws dw s = 1 3 W t 3
More informationMonte Carlo Methods for Uncertainty Quantification
Monte Carlo Methods for Uncertainty Quantification Abdul-Lateef Haji-Ali Based on slides by: Mike Giles Mathematical Institute, University of Oxford Contemporary Numerical Techniques Haji-Ali (Oxford)
More informationMonte Carlo Methods. Prof. Mike Giles. Oxford University Mathematical Institute. Lecture 1 p. 1.
Monte Carlo Methods Prof. Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Lecture 1 p. 1 Geometric Brownian Motion In the case of Geometric Brownian Motion ds t = rs t dt+σs
More informationCS134: Networks Spring Random Variables and Independence. 1.2 Probability Distribution Function (PDF) Number of heads Probability 2 0.
CS134: Networks Spring 2017 Prof. Yaron Singer Section 0 1 Probability 1.1 Random Variables and Independence A real-valued random variable is a variable that can take each of a set of possible values in
More informationNumerical schemes for SDEs
Lecture 5 Numerical schemes for SDEs Lecture Notes by Jan Palczewski Computational Finance p. 1 A Stochastic Differential Equation (SDE) is an object of the following type dx t = a(t,x t )dt + b(t,x t
More informationLecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing
Lecture Note 8 of Bus 41202, Spring 2017: Stochastic Diffusion Equation & Option Pricing We shall go over this note quickly due to time constraints. Key concept: Ito s lemma Stock Options: A contract giving
More informationImportance Sampling for Option Pricing. Steven R. Dunbar. Put Options. Monte Carlo Method. Importance. Sampling. Examples.
for for January 25, 2016 1 / 26 Outline for 1 2 3 4 2 / 26 Put Option for A put option is the right to sell an asset at an established price at a certain time. The established price is the strike price,
More informationModule 10:Application of stochastic processes in areas like finance Lecture 36:Black-Scholes Model. Stochastic Differential Equation.
Stochastic Differential Equation Consider. Moreover partition the interval into and define, where. Now by Rieman Integral we know that, where. Moreover. Using the fundamentals mentioned above we can easily
More informationLattice (Binomial Trees) Version 1.2
Lattice (Binomial Trees) Version 1. 1 Introduction This plug-in implements different binomial trees approximations for pricing contingent claims and allows Fairmat to use some of the most popular binomial
More informationModule 4: Monte Carlo path simulation
Module 4: Monte Carlo path simulation Prof. Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Module 4: Monte Carlo p. 1 SDE Path Simulation In Module 2, looked at the case
More informationStatistical Tables Compiled by Alan J. Terry
Statistical Tables Compiled by Alan J. Terry School of Science and Sport University of the West of Scotland Paisley, Scotland Contents Table 1: Cumulative binomial probabilities Page 1 Table 2: Cumulative
More informationNumerical Simulation of Stochastic Differential Equations: Lecture 2, Part 2
Numerical Simulation of Stochastic Differential Equations: Lecture 2, Part 2 Des Higham Department of Mathematics University of Strathclyde Montreal, Feb. 2006 p.1/17 Lecture 2, Part 2: Mean Exit Times
More informationPDE Methods for the Maximum Drawdown
PDE Methods for the Maximum Drawdown Libor Pospisil, Jan Vecer Columbia University, Department of Statistics, New York, NY 127, USA April 1, 28 Abstract Maximum drawdown is a risk measure that plays an
More informationOptimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing
Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing Prof. Chuan-Ju Wang Department of Computer Science University of Taipei Joint work with Prof. Ming-Yang Kao March 28, 2014
More informationOptions. An Undergraduate Introduction to Financial Mathematics. J. Robert Buchanan. J. Robert Buchanan Options
Options An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2014 Definitions and Terminology Definition An option is the right, but not the obligation, to buy or sell a security such
More informationChapter 3 Common Families of Distributions. Definition 3.4.1: A family of pmfs or pdfs is called exponential family if it can be expressed as
Lecture 0 on BST 63: Statistical Theory I Kui Zhang, 09/9/008 Review for the previous lecture Definition: Several continuous distributions, including uniform, gamma, normal, Beta, Cauchy, double exponential
More informationValuation of performance-dependent options in a Black- Scholes framework
Valuation of performance-dependent options in a Black- Scholes framework Thomas Gerstner, Markus Holtz Institut für Numerische Simulation, Universität Bonn, Germany Ralf Korn Fachbereich Mathematik, TU
More informationJDEP 384H: Numerical Methods in Business
Chapter 4: Numerical Integration: Deterministic and Monte Carlo Methods Chapter 8: Option Pricing by Monte Carlo Methods JDEP 384H: Numerical Methods in Business Instructor: Thomas Shores Department of
More informationThe Black-Scholes Equation
The Black-Scholes Equation MATH 472 Financial Mathematics J. Robert Buchanan 2018 Objectives In this lesson we will: derive the Black-Scholes partial differential equation using Itô s Lemma and no-arbitrage
More informationChapter 14. The Multi-Underlying Black-Scholes Model and Correlation
Chapter 4 The Multi-Underlying Black-Scholes Model and Correlation So far we have discussed single asset options, the payoff function depended only on one underlying. Now we want to allow multiple underlyings.
More informationDr. Maddah ENMG 625 Financial Eng g II 10/16/06
Dr. Maddah ENMG 65 Financial Eng g II 10/16/06 Chapter 11 Models of Asset Dynamics () Random Walk A random process, z, is an additive process defined over times t 0, t 1,, t k, t k+1,, such that z( t )
More informationSimulating Continuous Time Rating Transitions
Bus 864 1 Simulating Continuous Time Rating Transitions Robert A. Jones 17 March 2003 This note describes how to simulate state changes in continuous time Markov chains. An important application to credit
More informationValuation of Asian Option. Qi An Jingjing Guo
Valuation of Asian Option Qi An Jingjing Guo CONTENT Asian option Pricing Monte Carlo simulation Conclusion ASIAN OPTION Definition of Asian option always emphasizes the gist that the payoff depends on
More informationHand and Spreadsheet Simulations
1 / 34 Hand and Spreadsheet Simulations Christos Alexopoulos and Dave Goldsman Georgia Institute of Technology, Atlanta, GA, USA 9/8/16 2 / 34 Outline 1 Stepping Through a Differential Equation 2 Monte
More informationMonte Carlo Methods for Uncertainty Quantification
Monte Carlo Methods for Uncertainty Quantification Mike Giles Mathematical Institute, University of Oxford Contemporary Numerical Techniques Mike Giles (Oxford) Monte Carlo methods 2 1 / 24 Lecture outline
More informationWeak Reflection Principle and Static Hedging of Barrier Options
Weak Reflection Principle and Static Hedging of Barrier Options Sergey Nadtochiy Department of Mathematics University of Michigan Apr 2013 Fields Quantitative Finance Seminar Fields Institute, Toronto
More informationNUMERICAL METHODS OF PARTIAL INTEGRO-DIFFERENTIAL EQUATIONS FOR OPTION PRICE
Trends in Mathematics - New Series Information Center for Mathematical Sciences Volume 13, Number 1, 011, pages 1 5 NUMERICAL METHODS OF PARTIAL INTEGRO-DIFFERENTIAL EQUATIONS FOR OPTION PRICE YONGHOON
More informationPAPER 27 STOCHASTIC CALCULUS AND APPLICATIONS
MATHEMATICAL TRIPOS Part III Thursday, 5 June, 214 1:3 pm to 4:3 pm PAPER 27 STOCHASTIC CALCULUS AND APPLICATIONS Attempt no more than FOUR questions. There are SIX questions in total. The questions carry
More informationExact Sampling of Jump-Diffusion Processes
1 Exact Sampling of Jump-Diffusion Processes and Dmitry Smelov Management Science & Engineering Stanford University Exact Sampling of Jump-Diffusion Processes 2 Jump-Diffusion Processes Ubiquitous in finance
More informationELEMENTS OF MONTE CARLO SIMULATION
APPENDIX B ELEMENTS OF MONTE CARLO SIMULATION B. GENERAL CONCEPT The basic idea of Monte Carlo simulation is to create a series of experimental samples using a random number sequence. According to the
More informationLimit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies
Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies George Tauchen Duke University Viktor Todorov Northwestern University 2013 Motivation
More information3.1 Itô s Lemma for Continuous Stochastic Variables
Lecture 3 Log Normal Distribution 3.1 Itô s Lemma for Continuous Stochastic Variables Mathematical Finance is about pricing (or valuing) financial contracts, and in particular those contracts which depend
More informationCS 774 Project: Fall 2009 Version: November 27, 2009
CS 774 Project: Fall 2009 Version: November 27, 2009 Instructors: Peter Forsyth, paforsyt@uwaterloo.ca Office Hours: Tues: 4:00-5:00; Thurs: 11:00-12:00 Lectures:MWF 3:30-4:20 MC2036 Office: DC3631 CS
More informationMartingale Pricing Theory in Discrete-Time and Discrete-Space Models
IEOR E4707: Foundations of Financial Engineering c 206 by Martin Haugh Martingale Pricing Theory in Discrete-Time and Discrete-Space Models These notes develop the theory of martingale pricing in a discrete-time,
More informationBinomial model: numerical algorithm
Binomial model: numerical algorithm S / 0 C \ 0 S0 u / C \ 1,1 S0 d / S u 0 /, S u 3 0 / 3,3 C \ S0 u d /,1 S u 5 0 4 0 / C 5 5,5 max X S0 u,0 S u C \ 4 4,4 C \ 3 S u d / 0 3, C \ S u d 0 S u d 0 / C 4
More informationThe Black-Scholes PDE from Scratch
The Black-Scholes PDE from Scratch chris bemis November 27, 2006 0-0 Goal: Derive the Black-Scholes PDE To do this, we will need to: Come up with some dynamics for the stock returns Discuss Brownian motion
More informationIEOR E4703: Monte-Carlo Simulation
IEOR E4703: Monte-Carlo Simulation Simulation Efficiency and an Introduction to Variance Reduction Methods Martin Haugh Department of Industrial Engineering and Operations Research Columbia University
More informationDiscrete Random Variables and Probability Distributions. Stat 4570/5570 Based on Devore s book (Ed 8)
3 Discrete Random Variables and Probability Distributions Stat 4570/5570 Based on Devore s book (Ed 8) Random Variables We can associate each single outcome of an experiment with a real number: We refer
More informationStochastic Dynamical Systems and SDE s. An Informal Introduction
Stochastic Dynamical Systems and SDE s An Informal Introduction Olav Kallenberg Graduate Student Seminar, April 18, 2012 1 / 33 2 / 33 Simple recursion: Deterministic system, discrete time x n+1 = f (x
More informationOption Pricing Models for European Options
Chapter 2 Option Pricing Models for European Options 2.1 Continuous-time Model: Black-Scholes Model 2.1.1 Black-Scholes Assumptions We list the assumptions that we make for most of this notes. 1. The underlying
More informationSolving the Black-Scholes Equation
Solving the Black-Scholes Equation An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2014 Initial Value Problem for the European Call The main objective of this lesson is solving
More informationAdvanced Numerical Methods
Advanced Numerical Methods Solution to Homework One Course instructor: Prof. Y.K. Kwok. When the asset pays continuous dividend yield at the rate q the expected rate of return of the asset is r q under
More informationMath 623 (IOE 623), Winter 2008: Final exam
Math 623 (IOE 623), Winter 2008: Final exam Name: Student ID: This is a closed book exam. You may bring up to ten one sided A4 pages of notes to the exam. You may also use a calculator but not its memory
More informationLIBOR models, multi-curve extensions, and the pricing of callable structured derivatives
Weierstrass Institute for Applied Analysis and Stochastics LIBOR models, multi-curve extensions, and the pricing of callable structured derivatives John Schoenmakers 9th Summer School in Mathematical Finance
More informationA new approach for scenario generation in risk management
A new approach for scenario generation in risk management Josef Teichmann TU Wien Vienna, March 2009 Scenario generators Scenarios of risk factors are needed for the daily risk analysis (1D and 10D ahead)
More informationConstructing Markov models for barrier options
Constructing Markov models for barrier options Gerard Brunick joint work with Steven Shreve Department of Mathematics University of Texas at Austin Nov. 14 th, 2009 3 rd Western Conference on Mathematical
More informationAdvanced Topics in Derivative Pricing Models. Topic 4 - Variance products and volatility derivatives
Advanced Topics in Derivative Pricing Models Topic 4 - Variance products and volatility derivatives 4.1 Volatility trading and replication of variance swaps 4.2 Volatility swaps 4.3 Pricing of discrete
More informationBarrier options. In options only come into being if S t reaches B for some 0 t T, at which point they become an ordinary option.
Barrier options A typical barrier option contract changes if the asset hits a specified level, the barrier. Barrier options are therefore path-dependent. Out options expire worthless if S t reaches the
More informationEFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS
Commun. Korean Math. Soc. 23 (2008), No. 2, pp. 285 294 EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS Kyoung-Sook Moon Reprinted from the Communications of the Korean Mathematical Society
More informationEvaluating the Longstaff-Schwartz method for pricing of American options
U.U.D.M. Project Report 2015:13 Evaluating the Longstaff-Schwartz method for pricing of American options William Gustafsson Examensarbete i matematik, 15 hp Handledare: Josef Höök, Institutionen för informationsteknologi
More informationTheory and practice of option pricing
Theory and practice of option pricing Juliusz Jabłecki Department of Quantitative Finance Faculty of Economic Sciences University of Warsaw jjablecki@wne.uw.edu.pl and Head of Monetary Policy Analysis
More informationStochastic Local Volatility: Excursions in Finite Differences
Stochastic Local Volatility: Excursions in Finite Differences ICBI Global Derivatives Paris April 0 Jesper Andreasen Danske Markets, Copenhagen kwant.daddy@danskebank.dk Outline Motivation: Part A & B.
More informationShort-time-to-expiry expansion for a digital European put option under the CEV model. November 1, 2017
Short-time-to-expiry expansion for a digital European put option under the CEV model November 1, 2017 Abstract In this paper I present a short-time-to-expiry asymptotic series expansion for a digital European
More informationMSc Financial Engineering CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL. To be handed in by monday January 28, 2013
MSc Financial Engineering 2012-13 CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL To be handed in by monday January 28, 2013 Department EMS, Birkbeck Introduction The assignment consists of Reading
More information10. Monte Carlo Methods
10. Monte Carlo Methods 1. Introduction. Monte Carlo simulation is an important tool in computational finance. It may be used to evaluate portfolio management rules, to price options, to simulate hedging
More informationFINANCIAL OPTION ANALYSIS HANDOUTS
FINANCIAL OPTION ANALYSIS HANDOUTS 1 2 FAIR PRICING There is a market for an object called S. The prevailing price today is S 0 = 100. At this price the object S can be bought or sold by anyone for any
More informationHedging Credit Derivatives in Intensity Based Models
Hedging Credit Derivatives in Intensity Based Models PETER CARR Head of Quantitative Financial Research, Bloomberg LP, New York Director of the Masters Program in Math Finance, Courant Institute, NYU Stanford
More informationGenetics and/of basket options
Genetics and/of basket options Wolfgang Karl Härdle Elena Silyakova Ladislaus von Bortkiewicz Chair of Statistics Humboldt-Universität zu Berlin http://lvb.wiwi.hu-berlin.de Motivation 1-1 Basket derivatives
More informationNumerical Simulation of Stochastic Differential Equations: Lecture 1, Part 1. Overview of Lecture 1, Part 1: Background Mater.
Numerical Simulation of Stochastic Differential Equations: Lecture, Part Des Higham Department of Mathematics University of Strathclyde Course Aim: Give an accessible intro. to SDEs and their numerical
More information25857 Interest Rate Modelling
25857 UTS Business School University of Technology Sydney Chapter 20. Change of Numeraire May 15, 2014 1/36 Chapter 20. Change of Numeraire 1 The Radon-Nikodym Derivative 2 Option Pricing under Stochastic
More informationSimulating more interesting stochastic processes
Chapter 7 Simulating more interesting stochastic processes 7. Generating correlated random variables The lectures contained a lot of motivation and pictures. We'll boil everything down to pure algebra
More informationStochastic Modelling in Finance
in Finance Department of Mathematics and Statistics University of Strathclyde Glasgow, G1 1XH April 2010 Outline and Probability 1 and Probability 2 Linear modelling Nonlinear modelling 3 The Black Scholes
More informationA Moment Matching Approach To The Valuation Of A Volume Weighted Average Price Option
A Moment Matching Approach To The Valuation Of A Volume Weighted Average Price Option Antony Stace Department of Mathematics and MASCOS University of Queensland 15th October 2004 AUSTRALIAN RESEARCH COUNCIL
More informationModule 2: Monte Carlo Methods
Module 2: Monte Carlo Methods Prof. Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute MC Lecture 2 p. 1 Greeks In Monte Carlo applications we don t just want to know the expected
More informationComputer labs. May 10, A list of matlab tutorials can be found under
Computer labs May 10, 2018 A list of matlab tutorials can be found under http://snovit.math.umu.se/personal/cohen_david/teachlinks.html Task 1: The following MATLAB code generates (pseudo) uniform random
More informationQuasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction
Quasi-Monte Carlo Methods in Financial Engineering: An Equivalence Principle and Dimension Reduction Xiaoqun Wang,2, and Ian H. Sloan 2,3 Department of Mathematical Sciences, Tsinghua University, Beijing
More informationMonte Carlo Methods in Option Pricing. UiO-STK4510 Autumn 2015
Monte Carlo Methods in Option Pricing UiO-STK4510 Autumn 015 The Basics of Monte Carlo Method Goal: Estimate the expectation θ = E[g(X)], where g is a measurable function and X is a random variable such
More informationFE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology
FE610 Stochastic Calculus for Financial Engineers Lecture 13. The Black-Scholes PDE Steve Yang Stevens Institute of Technology 04/25/2013 Outline 1 The Black-Scholes PDE 2 PDEs in Asset Pricing 3 Exotic
More informationThe Use of Importance Sampling to Speed Up Stochastic Volatility Simulations
The Use of Importance Sampling to Speed Up Stochastic Volatility Simulations Stan Stilger June 6, 1 Fouque and Tullie use importance sampling for variance reduction in stochastic volatility simulations.
More informationFinancial Risk Forecasting Chapter 7 Simulation methods for VaR for options and bonds
Financial Risk Forecasting Chapter 7 Simulation methods for VaR for options and bonds Jon Danielsson 2017 London School of Economics To accompany Financial Risk Forecasting www.financialriskforecasting.com
More informationBarrier Option. 2 of 33 3/13/2014
FPGA-based Reconfigurable Computing for Pricing Multi-Asset Barrier Options RAHUL SRIDHARAN, GEORGE COOKE, KENNETH HILL, HERMAN LAM, ALAN GEORGE, SAAHPC '12, PROCEEDINGS OF THE 2012 SYMPOSIUM ON APPLICATION
More informationStochastic Differential Equations in Finance and Monte Carlo Simulations
Stochastic Differential Equations in Finance and Department of Statistics and Modelling Science University of Strathclyde Glasgow, G1 1XH China 2009 Outline Stochastic Modelling in Asset Prices 1 Stochastic
More informationSolving the Black-Scholes Equation
Solving the Black-Scholes Equation An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Initial Value Problem for the European Call rf = F t + rsf S + 1 2 σ2 S 2 F SS for (S,
More information