Lecture 15: Exotic Options: Barriers

Size: px
Start display at page:

Download "Lecture 15: Exotic Options: Barriers"

Transcription

1 Lecture 15: Exotic Options: Barriers Dr. Hanqing Jin Mathematical Institute University of Oxford Lecture 15: Exotic Options: Barriers p. 1/10

2 Barrier features For any options with payoff ξ at exercise time τ, barrier features can be applied Set one barrier B, (or multi-barrier) Option activated/disactivated when the underlying asset price hits the barrier B before expiry Knock-ins: activated when the barrier is hit. Up-and-in: the option is activated when the barrier is hit from below Down-and-in: activated when the barrier is hit from above. Knock-outs: Up-and-out; Down-and-out Lecture 15: Exotic Options: Barriers p. 2/10

3 Barrier call options Example 1: Given a European call option with K = 100, and a barrier B = 90. The terminal payoff is max{s T K, 0} if it is active at time T, and 0 if not. Up-and-in option: if S 0 = 80, the option will be activated when S t goes up to 90 before T. Down-and-in option: if S 0 = 95, the option will be activated when S t goes down to 90 before T. Payoffs of barriers: path dependent Prices of barriers: for a European option, price is a function of (t,s t ) before the barrier hitting. Weakly path dependent. Lecture 15: Exotic Options: Barriers p. 3/10

4 Why barriers Barrier options are cheaper than vanilla options Example 2: An investor holds 1000 shares of a stock with price S 0 = 100, he wants to hedge the risk of lower price at 6 months later by put option with strike K = 90. If the price goes up to 110, he may not need the option any more. He can buy a put option with knock-out barrier B = 110, which reduces the premium of the option. Reverse knock-out option: the option will be knocked-out at some barrier where the option is in-the-money In E.g. 2, when the knock-out barrier B = 80, the option is a reverse knock-out put option. Lecture 15: Exotic Options: Barriers p. 4/10

5 Other barrier options Multiple barriers: E.g., A double knock-out option has two barriers B 1,B 2, The option is deactivated when either of the barriers was hit. Time dependent barrier: the barrier B changes with time, e.g. B(t) = Be rt. American digital: the strike price K can be regarded as a barrier. Barrier with rebate: when the option knocked out, a rebate will be paid immediately. Lecture 15: Exotic Options: Barriers p. 5/10

6 Pricing of barriers V knock-in + V knock-out = V no-barrier. For a down-and-out European call option (d/o for short) with strike K, barrier B, denote its price by C d/o (t,s t ) when it is not knocked out. Suppose K > B. Black-Scholes PDE hold for S t > B, L BS C d/o (t,s t ) = 0 on (t,s t ) [0,T) (B, + ) Boundary condition: C d/o (T,S T ) = max{s T K, 0} C d/o (t,b) = 0 Lecture 15: Exotic Options: Barriers p. 6/10

7 Pricing of barriers Denote V 1 (t,s t ) = C(t,S t ) C d/o (t,s t ), then L BS V 1 = 0 and V 1 (T,S T ) = 0 when S T > B V 1 (t,b) = C(t,B) Check that U(t,S t ) = ( ) S t 1 2r/σ 2 B C(t, B 2 S t ) also satisfies Black-Scholes PDE Notice U(t,B) = C(t,B), and U(T,S T ) = 0 for S T > B. So V 1 (t,s t ) = U(t,S t ). C d/o (t,s t ) = C(t,S t ) ( St B ) 1 2r/σ 2 C(t, B2 S t ) Lecture 15: Exotic Options: Barriers p. 7/10

8 Pricing of barriers Suppose K < B. Then at time T, if not knocked out, then S T > B, the payoff max{s T K, 0}1 ST >B = max{s T B, 0}+(B K)1 ST >B a d/o on call option with strike B + (B K) d/o on cash-or-nothing call with strike B. By the same way, we can calculate the value of d/o on cash-or-nothing call with strike B is C c/n (t,s t ) ( S t B ) 1 2r/σ 2 C c/n (t, B2 S t ) Value of the d/o on call option with strike K is C d/o (t, S t ) = C(t, S t ; B)+(B K)C c/n (t, S t ; B) ( S t B )1 2r σ 2 [C(t, B2 S t ; B)+(B K)C c/n (t, B2 S t ; B)] Lecture 15: Exotic Options: Barriers p. 8/10

9 Risk-neutral pricing For a stochastic process X t, denote M X t 1,t 2 = sup t [t1,t 2 ] X(s), m X t 1,t 2 = inf t [t1,t 2 ] X(s) By risk neutral pricing formula, given m S 0,t > B, C d/o (t,s t ) = e r(t t) E[max{ S T K, 0}1 m St,T >B S t = S t ] = e r(t t) E[( S T K)1 ST >K,m S t,t >B S t = S t ] = e r(t t) E[(S t e X T t K)1 St e X T t>k,m S t ex 0,T t >B] = e r(t t) E[(S t e X T t K)1 XT t >ln(k/s t ),m X 0,T t >ln(b/s t)] where X t = (r σ 2 /2)t + σw t for some standard Brownian motion W. Lecture 15: Exotic Options: Barriers p. 9/10

10 Risk-neutral pricing Distributions of X t,m X 0,t For Y t = a + µt + σw t, Distribution of M Y t ( x a µt F M Y t (x) = Φ σ t is required. for any x a is ) e 2µ(x a) σ 2 Φ Distribution of m Y t for any x a is ( ) x a µt F m Y t (x) = Φ σ t + e 2µ(x a) σ 2 Joint density function of (Y t,m Y t ) at x y,y > a, f Yt,M Y t (x,y) = 2(2y (x + a)) 2πσ6 t 3 e (2y (x+a))2 2σ 2 t ( ) x + a µt σ t ( ) x a + µt Φ σ t + µ(x a) σ 2 µ2 t 2σ 2 Lecture 15: Exotic Options: Barriers p. 10/10

Definition Pricing Risk management Second generation barrier options. Barrier Options. Arfima Financial Solutions

Definition Pricing Risk management Second generation barrier options. Barrier Options. Arfima Financial Solutions Arfima Financial Solutions Contents Definition 1 Definition 2 3 4 Contenido Definition 1 Definition 2 3 4 Definition Definition: A barrier option is an option on the underlying asset that is activated

More information

Monte Carlo Simulations

Monte 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 information

NEWCASTLE 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 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 information

Pricing Barrier Options under Local Volatility

Pricing Barrier Options under Local Volatility Abstract Pricing Barrier Options under Local Volatility Artur Sepp Mail: artursepp@hotmail.com, Web: www.hot.ee/seppar 16 November 2002 We study pricing under the local volatility. Our research is mainly

More information

Barrier 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. 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 information

M5MF6. Advanced Methods in Derivatives Pricing

M5MF6. 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 information

Keywords: Digital options, Barrier options, Path dependent options, Lookback options, Asian options.

Keywords: Digital options, Barrier options, Path dependent options, Lookback options, Asian options. FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 Exotic Options These notes describe the payoffs to some of the so-called exotic options. There are a variety of different types of exotic options. Some of these

More information

Path Dependent British Options

Path Dependent British Options Path Dependent British Options Kristoffer J Glover (Joint work with G. Peskir and F. Samee) School of Finance and Economics University of Technology, Sydney 18th August 2009 (PDE & Mathematical Finance

More information

FE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology

FE610 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 information

Lecture Notes for Chapter 6. 1 Prototype model: a one-step binomial tree

Lecture Notes for Chapter 6. 1 Prototype model: a one-step binomial tree Lecture Notes for Chapter 6 This is the chapter that brings together the mathematical tools (Brownian motion, Itô calculus) and the financial justifications (no-arbitrage pricing) to produce the derivative

More information

Math Computational Finance Barrier option pricing using Finite Difference Methods (FDM)

Math Computational Finance Barrier option pricing using Finite Difference Methods (FDM) . Math 623 - Computational Finance Barrier option pricing using Finite Difference Methods (FDM) Pratik Mehta pbmehta@eden.rutgers.edu Masters of Science in Mathematical Finance Department of Mathematics,

More information

The discounted portfolio value of a selffinancing strategy in discrete time was given by. δ tj 1 (s tj s tj 1 ) (9.1) j=1

The discounted portfolio value of a selffinancing strategy in discrete time was given by. δ tj 1 (s tj s tj 1 ) (9.1) j=1 Chapter 9 The isk Neutral Pricing Measure for the Black-Scholes Model The discounted portfolio value of a selffinancing strategy in discrete time was given by v tk = v 0 + k δ tj (s tj s tj ) (9.) where

More information

Internet Appendix to Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market

Internet Appendix to Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market Internet Appendix to Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market not to be included for publication) Xindan Li, Avanidhar Subrahmanyam,

More information

Stochastic Differential equations as applied to pricing of options

Stochastic Differential equations as applied to pricing of options Stochastic Differential equations as applied to pricing of options By Yasin LUT Supevisor:Prof. Tuomo Kauranne December 2010 Introduction Pricing an European call option Conclusion INTRODUCTION A stochastic

More information

MATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS

MATH3075/3975 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 information

SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives

SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives SYSM 6304: Risk and Decision Analysis Lecture 6: Pricing and Hedging Financial Derivatives M. Vidyasagar Cecil & Ida Green Chair The University of Texas at Dallas Email: M.Vidyasagar@utdallas.edu October

More information

Computational Finance. Computational Finance p. 1

Computational Finance. Computational Finance p. 1 Computational Finance Computational Finance p. 1 Outline Binomial model: option pricing and optimal investment Monte Carlo techniques for pricing of options pricing of non-standard options improving accuracy

More information

Lecture 17. The model is parametrized by the time period, δt, and three fixed constant parameters, v, σ and the riskless rate r.

Lecture 17. The model is parametrized by the time period, δt, and three fixed constant parameters, v, σ and the riskless rate r. Lecture 7 Overture to continuous models Before rigorously deriving the acclaimed Black-Scholes pricing formula for the value of a European option, we developed a substantial body of material, in continuous

More information

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

Economathematics. 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 information

The British Russian Option

The British Russian Option The British Russian Option Kristoffer J Glover (Joint work with G. Peskir and F. Samee) School of Finance and Economics University of Technology, Sydney 25th June 2010 (6th World Congress of the BFS, Toronto)

More information

Financial Risk Management

Financial Risk Management Risk-neutrality in derivatives pricing University of Oulu - Department of Finance Spring 2018 Portfolio of two assets Value at time t = 0 Expected return Value at time t = 1 Asset A Asset B 10.00 30.00

More information

KØBENHAVNS UNIVERSITET (Blok 2, 2011/2012) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours

KØBENHAVNS UNIVERSITET (Blok 2, 2011/2012) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours This question paper consists of 3 printed pages FinKont KØBENHAVNS UNIVERSITET (Blok 2, 211/212) Naturvidenskabelig kandidateksamen Continuous time finance (FinKont) TIME ALLOWED : 3 hours This exam paper

More information

1 Implied Volatility from Local Volatility

1 Implied Volatility from Local Volatility Abstract We try to understand the Berestycki, Busca, and Florent () (BBF) result in the context of the work presented in Lectures and. Implied Volatility from Local Volatility. Current Plan as of March

More information

From Discrete Time to Continuous Time Modeling

From Discrete Time to Continuous Time Modeling From Discrete Time to Continuous Time Modeling Prof. S. Jaimungal, Department of Statistics, University of Toronto 2004 Arrow-Debreu Securities 2004 Prof. S. Jaimungal 2 Consider a simple one-period economy

More information

Arbitrage Bounds for Volatility Derivatives as Free Boundary Problem. Bruno Dupire Bloomberg L.P. NY

Arbitrage Bounds for Volatility Derivatives as Free Boundary Problem. Bruno Dupire Bloomberg L.P. NY Arbitrage Bounds for Volatility Derivatives as Free Boundary Problem Bruno Dupire Bloomberg L.P. NY bdupire@bloomberg.net PDE and Mathematical Finance, KTH, Stockholm August 16, 25 Variance Swaps Vanilla

More information

MAS452/MAS6052. MAS452/MAS Turn Over SCHOOL OF MATHEMATICS AND STATISTICS. Stochastic Processes and Financial Mathematics

MAS452/MAS6052. MAS452/MAS Turn Over SCHOOL OF MATHEMATICS AND STATISTICS. Stochastic Processes and Financial Mathematics t r t r2 r t SCHOOL OF MATHEMATICS AND STATISTICS Stochastic Processes and Financial Mathematics Spring Semester 2017 2018 3 hours t s s tt t q st s 1 r s r t r s rts t q st s r t r r t Please leave this

More information

The Black-Scholes Equation using Heat Equation

The Black-Scholes Equation using Heat Equation The Black-Scholes Equation using Heat Equation Peter Cassar May 0, 05 Assumptions of the Black-Scholes Model We have a risk free asset given by the price process, dbt = rbt The asset price follows a geometric

More information

Pricing of Discrete Barrier Options. MSc in Mathematical Finance Trinity Abstract

Pricing of Discrete Barrier Options. MSc in Mathematical Finance Trinity Abstract Pricing of Discrete Barrier Options Kellogg College University of Oxford A thesis submitted in partial fulfilment of the requirements for the MSc in Mathematical Finance October 22, 2003 Pricing of Discrete

More information

1 Geometric Brownian motion

1 Geometric Brownian motion Copyright c 05 by Karl Sigman Geometric Brownian motion Note that since BM can take on negative values, using it directly for modeling stock prices is questionable. There are other reasons too why BM is

More information

Valuing Stock Options: The Black-Scholes-Merton Model. Chapter 13

Valuing Stock Options: The Black-Scholes-Merton Model. Chapter 13 Valuing Stock Options: The Black-Scholes-Merton Model Chapter 13 1 The Black-Scholes-Merton Random Walk Assumption l Consider a stock whose price is S l In a short period of time of length t the return

More information

Lecture 4. Finite difference and finite element methods

Lecture 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 information

1.1 Basic Financial Derivatives: Forward Contracts and Options

1.1 Basic Financial Derivatives: Forward Contracts and Options Chapter 1 Preliminaries 1.1 Basic Financial Derivatives: Forward Contracts and Options A derivative is a financial instrument whose value depends on the values of other, more basic underlying variables

More information

Black-Scholes-Merton Model

Black-Scholes-Merton Model Black-Scholes-Merton Model Weerachart Kilenthong University of the Thai Chamber of Commerce c Kilenthong 2017 Weerachart Kilenthong University of the Thai Chamber Black-Scholes-Merton of Commerce Model

More information

Non-semimartingales in finance

Non-semimartingales in finance Non-semimartingales in finance Pricing and Hedging Options with Quadratic Variation Tommi Sottinen University of Vaasa 1st Northern Triangular Seminar 9-11 March 2009, Helsinki University of Technology

More information

Constructing Markov models for barrier options

Constructing 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 information

Credit Risk : Firm Value Model

Credit Risk : Firm Value Model Credit Risk : Firm Value Model Prof. Dr. Svetlozar Rachev Institute for Statistics and Mathematical Economics University of Karlsruhe and Karlsruhe Institute of Technology (KIT) Prof. Dr. Svetlozar Rachev

More information

A 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 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 information

EFFICIENT MONTE CARLO ALGORITHM FOR PRICING BARRIER OPTIONS

EFFICIENT 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 information

Stochastic Volatility Effects on Defaultable Bonds

Stochastic Volatility Effects on Defaultable Bonds Stochastic Volatility Effects on Defaultable Bonds Jean-Pierre Fouque Ronnie Sircar Knut Sølna December 24; revised October 24, 25 Abstract We study the effect of introducing stochastic volatility in the

More information

Structural Models of Credit Risk and Some Applications

Structural Models of Credit Risk and Some Applications Structural Models of Credit Risk and Some Applications Albert Cohen Actuarial Science Program Department of Mathematics Department of Statistics and Probability albert@math.msu.edu August 29, 2018 Outline

More information

Financial derivatives exam Winter term 2014/2015

Financial derivatives exam Winter term 2014/2015 Financial derivatives exam Winter term 2014/2015 Problem 1: [max. 13 points] Determine whether the following assertions are true or false. Write your answers, without explanations. Grading: correct answer

More information

Hedging. MATH 472 Financial Mathematics. J. Robert Buchanan

Hedging. MATH 472 Financial Mathematics. J. Robert Buchanan Hedging MATH 472 Financial Mathematics J. Robert Buchanan 2018 Introduction Definition Hedging is the practice of making a portfolio of investments less sensitive to changes in market variables. There

More information

The Black-Scholes Model

The Black-Scholes Model IEOR E4706: Foundations of Financial Engineering c 2016 by Martin Haugh The Black-Scholes Model In these notes we will use Itô s Lemma and a replicating argument to derive the famous Black-Scholes formula

More information

Computational Finance

Computational 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 information

Asymptotic Method for Singularity in Path-Dependent Option Pricing

Asymptotic Method for Singularity in Path-Dependent Option Pricing Asymptotic Method for Singularity in Path-Dependent Option Pricing Sang-Hyeon Park, Jeong-Hoon Kim Dept. Math. Yonsei University June 2010 Singularity in Path-Dependent June 2010 Option Pricing 1 / 21

More information

Financial Derivatives Section 5

Financial Derivatives Section 5 Financial Derivatives Section 5 The Black and Scholes Model Michail Anthropelos anthropel@unipi.gr http://web.xrh.unipi.gr/faculty/anthropelos/ University of Piraeus Spring 2018 M. Anthropelos (Un. of

More information

EARLY EXERCISE OPTIONS WITH DISCONTINUOUS PAYOFF

EARLY EXERCISE OPTIONS WITH DISCONTINUOUS PAYOFF EARLY EXERCISE OPTIONS WITH DISCONTINUOUS PAYOFF A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Science and Engineering 217 Min Gao Candidate

More information

The Uncertain Volatility Model

The Uncertain Volatility Model The Uncertain Volatility Model Claude Martini, Antoine Jacquier July 14, 008 1 Black-Scholes and realised volatility What happens when a trader uses the Black-Scholes (BS in the sequel) formula to sell

More information

Copyright Emanuel Derman 2008

Copyright Emanuel Derman 2008 E4718 Spring 28: Derman: Lecture 5:Static Hedging and Implied Distributions Page 1 of 34 Lecture 5: Static Hedging and Implied Distributions Recapitulation of Lecture 4: Plotting the smile against Δ is

More information

Replication and Absence of Arbitrage in Non-Semimartingale Models

Replication and Absence of Arbitrage in Non-Semimartingale Models Replication and Absence of Arbitrage in Non-Semimartingale Models Matematiikan päivät, Tampere, 4-5. January 2006 Tommi Sottinen University of Helsinki 4.1.2006 Outline 1. The classical pricing model:

More information

Hedging Credit Derivatives in Intensity Based Models

Hedging 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 information

Basic Concepts in Mathematical Finance

Basic Concepts in Mathematical Finance Chapter 1 Basic Concepts in Mathematical Finance In this chapter, we give an overview of basic concepts in mathematical finance theory, and then explain those concepts in very simple cases, namely in the

More information

Exercises for Mathematical Models of Financial Derivatives

Exercises for Mathematical Models of Financial Derivatives Exercises for Mathematical Models of Financial Derivatives January 24, 2 1. It is customary for shares in the UK to have prices between 1p and 1,p (in the US, between $1 and $1), perhaps because then typical

More information

MASM006 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. 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 information

Forwards and Futures. Chapter Basics of forwards and futures Forwards

Forwards and Futures. Chapter Basics of forwards and futures Forwards Chapter 7 Forwards and Futures Copyright c 2008 2011 Hyeong In Choi, All rights reserved. 7.1 Basics of forwards and futures The financial assets typically stocks we have been dealing with so far are the

More information

Lecture 11: Ito Calculus. Tuesday, October 23, 12

Lecture 11: Ito Calculus. Tuesday, October 23, 12 Lecture 11: Ito Calculus Continuous time models We start with the model from Chapter 3 log S j log S j 1 = µ t + p tz j Sum it over j: log S N log S 0 = NX µ t + NX p tzj j=1 j=1 Can we take the limit

More information

Lecture 1: Lévy processes

Lecture 1: Lévy processes Lecture 1: Lévy processes A. E. Kyprianou Department of Mathematical Sciences, University of Bath 1/ 22 Lévy processes 2/ 22 Lévy processes A process X = {X t : t 0} defined on a probability space (Ω,

More information

Probability in Options Pricing

Probability in Options Pricing Probability in Options Pricing Mark Cohen and Luke Skon Kenyon College cohenmj@kenyon.edu December 14, 2012 Mark Cohen and Luke Skon (Kenyon college) Probability Presentation December 14, 2012 1 / 16 What

More information

Valuation of derivative assets Lecture 6

Valuation of derivative assets Lecture 6 Valuation of derivative assets Lecture 6 Magnus Wiktorsson September 14, 2017 Magnus Wiktorsson L6 September 14, 2017 1 / 13 Feynman-Kac representation This is the link between a class of Partial Differential

More information

M.I.T Fall Practice Problems

M.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 information

Chapter 14. Exotic Options: I. Question Question Question Question The geometric averages for stocks will always be lower.

Chapter 14. Exotic Options: I. Question Question Question Question The geometric averages for stocks will always be lower. Chapter 14 Exotic Options: I Question 14.1 The geometric averages for stocks will always be lower. Question 14.2 The arithmetic average is 5 (three 5s, one 4, and one 6) and the geometric average is (5

More information

King s College London

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 information

Lecture 8: The Black-Scholes theory

Lecture 8: The Black-Scholes theory Lecture 8: The Black-Scholes theory Dr. Roman V Belavkin MSO4112 Contents 1 Geometric Brownian motion 1 2 The Black-Scholes pricing 2 3 The Black-Scholes equation 3 References 5 1 Geometric Brownian motion

More information

STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL

STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL YOUNGGEUN YOO Abstract. Ito s lemma is often used in Ito calculus to find the differentials of a stochastic process that depends on time. This paper will introduce

More information

The Black-Scholes Equation

The 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 information

Math Computational Finance Double barrier option pricing using Quasi Monte Carlo and Brownian Bridge methods

Math Computational Finance Double barrier option pricing using Quasi Monte Carlo and Brownian Bridge methods . Math 623 - Computational Finance Double barrier option pricing using Quasi Monte Carlo and Brownian Bridge methods Pratik Mehta pbmehta@eden.rutgers.edu Masters of Science in Mathematical Finance Department

More information

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011

Brownian Motion. Richard Lockhart. Simon Fraser University. STAT 870 Summer 2011 Brownian Motion Richard Lockhart Simon Fraser University STAT 870 Summer 2011 Richard Lockhart (Simon Fraser University) Brownian Motion STAT 870 Summer 2011 1 / 33 Purposes of Today s Lecture Describe

More information

Lecture 3: Review of mathematical finance and derivative pricing models

Lecture 3: Review of mathematical finance and derivative pricing models Lecture 3: Review of mathematical finance and derivative pricing models Xiaoguang Wang STAT 598W January 21th, 2014 (STAT 598W) Lecture 3 1 / 51 Outline 1 Some model independent definitions and principals

More information

Asset-or-nothing digitals

Asset-or-nothing digitals School of Education, Culture and Communication Division of Applied Mathematics MMA707 Analytical Finance I Asset-or-nothing digitals 202-0-9 Mahamadi Ouoba Amina El Gaabiiy David Johansson Examinator:

More information

Hedging under arbitrage

Hedging under arbitrage Hedging under arbitrage Johannes Ruf Columbia University, Department of Statistics AnStAp10 August 12, 2010 Motivation Usually, there are several trading strategies at one s disposal to obtain a given

More information

Drawdowns, Drawups, their joint distributions, detection and financial risk management

Drawdowns, Drawups, their joint distributions, detection and financial risk management Drawdowns, Drawups, their joint distributions, detection and financial risk management June 2, 2010 The cases a = b The cases a > b The cases a < b Insuring against drawing down before drawing up Robust

More information

Binomial model: numerical algorithm

Binomial 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 information

CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES

CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES CONTINUOUS TIME PRICING AND TRADING: A REVIEW, WITH SOME EXTRA PIECES THE SOURCE OF A PRICE IS ALWAYS A TRADING STRATEGY SPECIAL CASES WHERE TRADING STRATEGY IS INDEPENDENT OF PROBABILITY MEASURE COMPLETENESS,

More information

No-Arbitrage Bounds on Two One-Touch Options

No-Arbitrage Bounds on Two One-Touch Options No-Arbitrage Bounds on Two One-Touch Options Yukihiro Tsuzuki March 30, 04 Abstract This paper investigates the pricing bounds of two one-touch options with the same maturity but different barrier levels,

More information

Deterministic Income under a Stochastic Interest Rate

Deterministic Income under a Stochastic Interest Rate Deterministic Income under a Stochastic Interest Rate Julia Eisenberg, TU Vienna Scientic Day, 1 Agenda 1 Classical Problem: Maximizing Discounted Dividends in a Brownian Risk Model 2 Maximizing Discounted

More information

Chapter 14 Exotic Options: I

Chapter 14 Exotic Options: I Chapter 14 Exotic Options: I Question 14.1. The geometric averages for stocks will always be lower. Question 14.2. The arithmetic average is 5 (three 5 s, one 4, and one 6) and the geometric average is

More information

Lecture 1 Definitions from finance

Lecture 1 Definitions from finance Lecture 1 s from finance Financial market instruments can be divided into two types. There are the underlying stocks shares, bonds, commodities, foreign currencies; and their derivatives, claims that promise

More information

Lecture 6: Option Pricing Using a One-step Binomial Tree. Thursday, September 12, 13

Lecture 6: Option Pricing Using a One-step Binomial Tree. Thursday, September 12, 13 Lecture 6: Option Pricing Using a One-step Binomial Tree An over-simplified model with surprisingly general extensions a single time step from 0 to T two types of traded securities: stock S and a bond

More information

Pricing theory of financial derivatives

Pricing theory of financial derivatives Pricing theory of financial derivatives One-period securities model S denotes the price process {S(t) : t = 0, 1}, where S(t) = (S 1 (t) S 2 (t) S M (t)). Here, M is the number of securities. At t = 1,

More information

An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price under Stochastic Volatility Model

An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price under Stochastic Volatility Model An Asymptotic Expansion Formula for Up-and-Out Option Price under Stochastic Volatility Model Takashi Kato Akihiko Takahashi Toshihiro Yamada arxiv:32.336v [q-fin.cp] 4 Feb 23 December 3, 22 Abstract This

More information

Journal of Mathematical Analysis and Applications

Journal of Mathematical Analysis and Applications J Math Anal Appl 389 (01 968 978 Contents lists available at SciVerse Scienceirect Journal of Mathematical Analysis and Applications wwwelseviercom/locate/jmaa Cross a barrier to reach barrier options

More information

Hull, Options, Futures & Other Derivatives Exotic Options

Hull, Options, Futures & Other Derivatives Exotic Options P1.T3. Financial Markets & Products Hull, Options, Futures & Other Derivatives Exotic Options Bionic Turtle FRM Video Tutorials By David Harper, CFA FRM 1 Exotic Options Define and contrast exotic derivatives

More information

Risk Neutral Pricing Black-Scholes Formula Lecture 19. Dr. Vasily Strela (Morgan Stanley and MIT)

Risk Neutral Pricing Black-Scholes Formula Lecture 19. Dr. Vasily Strela (Morgan Stanley and MIT) Risk Neutral Pricing Black-Scholes Formula Lecture 19 Dr. Vasily Strela (Morgan Stanley and MIT) Risk Neutral Valuation: Two-Horse Race Example One horse has 20% chance to win another has 80% chance $10000

More information

A Continuity Correction under Jump-Diffusion Models with Applications in Finance

A Continuity Correction under Jump-Diffusion Models with Applications in Finance A Continuity Correction under Jump-Diffusion Models with Applications in Finance Cheng-Der Fuh 1, Sheng-Feng Luo 2 and Ju-Fang Yen 3 1 Institute of Statistical Science, Academia Sinica, and Graduate Institute

More information

FNCE 302, Investments H Guy Williams, 2008

FNCE 302, Investments H Guy Williams, 2008 Sources http://finance.bi.no/~bernt/gcc_prog/recipes/recipes/node7.html It's all Greek to me, Chris McMahon Futures; Jun 2007; 36, 7 http://www.quantnotes.com Put Call Parity THIS IS THE CALL-PUT PARITY

More information

An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price under Stochastic Volatility Model

An Asymptotic Expansion Formula for Up-and-Out Barrier Option Price under Stochastic Volatility Model CIRJE-F-873 An Asymptotic Expansion Formula for Up-and-Out Option Price under Stochastic Volatility Model Takashi Kato Osaka University Akihiko Takahashi University of Tokyo Toshihiro Yamada Graduate School

More information

Continuous time; continuous variable stochastic process. We assume that stock prices follow Markov processes. That is, the future movements in a

Continuous time; continuous variable stochastic process. We assume that stock prices follow Markov processes. That is, the future movements in a Continuous time; continuous variable stochastic process. We assume that stock prices follow Markov processes. That is, the future movements in a variable depend only on the present, and not the history

More information

Martingale Measure TA

Martingale Measure TA Martingale Measure TA Martingale Measure a) What is a martingale? b) Groundwork c) Definition of a martingale d) Super- and Submartingale e) Example of a martingale Table of Content Connection between

More information

Barrier Options and Lumpy Dividends

Barrier Options and Lumpy Dividends Barrier Options and Lumpy Dividends Johannes Vitalis Siven Michael Suchanecki Rolf Poulsen February 4, 009 Abstract We study the pricing of barrier options on stocks with lumpy dividends. By extending

More information

Module 10:Application of stochastic processes in areas like finance Lecture 36:Black-Scholes Model. Stochastic Differential Equation.

Module 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 information

The British Binary Option

The British Binary Option The British Binary Option Min Gao First version: 7 October 215 Research Report No. 9, 215, Probability and Statistics Group School of Mathematics, The University of Manchester The British Binary Option

More information

Hedging under Arbitrage

Hedging under Arbitrage Hedging under Arbitrage Johannes Ruf Columbia University, Department of Statistics Modeling and Managing Financial Risks January 12, 2011 Motivation Given: a frictionless market of stocks with continuous

More information

AMH4 - ADVANCED OPTION PRICING. Contents

AMH4 - 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 information

last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends.

last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends. 224 10 Arbitrage and SDEs last problem outlines how the Black Scholes PDE (and its derivation) may be modified to account for the payment of stock dividends. 10.1 (Calculation of Delta First and Finest

More information

Monte Carlo Methods for Uncertainty Quantification

Monte 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 information

Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard

Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard Indifference pricing and the minimal entropy martingale measure Fred Espen Benth Centre of Mathematics for Applications

More information

Numerical schemes for SDEs

Numerical 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 information

On the Cost of Delayed Currency Fixing Announcements

On the Cost of Delayed Currency Fixing Announcements On the Cost of Delayed Currency Fixing Announcements Uwe Wystup and Christoph Becker HfB - Business School of Finance and Management Frankfurt am Main mailto:uwe.wystup@mathfinance.de June 8, 2005 Abstract

More information

Calibration Lecture 4: LSV and Model Uncertainty

Calibration Lecture 4: LSV and Model Uncertainty Calibration Lecture 4: LSV and Model Uncertainty March 2017 Recap: Heston model Recall the Heston stochastic volatility model ds t = rs t dt + Y t S t dw 1 t, dy t = κ(θ Y t ) dt + ξ Y t dw 2 t, where

More information

Pricing and Hedging of European Plain Vanilla Options under Jump Uncertainty

Pricing and Hedging of European Plain Vanilla Options under Jump Uncertainty Pricing and Hedging of European Plain Vanilla Options under Jump Uncertainty by Olaf Menkens School of Mathematical Sciences Dublin City University (DCU) Financial Engineering Workshop Cass Business School,

More information