BLACK SCHOLES THE MARTINGALE APPROACH
|
|
- Poppy Goodman
- 5 years ago
- Views:
Transcription
1 BLACK SCHOLES HE MARINGALE APPROACH JOHN HICKSUN. Introduction hi paper etablihe the Black Schole formula in the martingale, rik-neutral valuation framework. he intent i two-fold. One, to erve a an introduction to expectation pricing and two, to examine thi framework in explicit mathematical detail. he reader i aumed to have fluent background in the mathematical theory of tochatic procee and calculu, but i not aumed to have background in finance. he relevant financial definition are given in ection 2. Section 2 alo etablihe the main reult (propoition 2.3) that link equivalent martingale meaure (EMM) to expectation. he key mathematical tool at work here i the martingale repreentation theorem, which guarantee the exitence of a hedging trategy under an EMM. In ection 3, we exploit thi reult by explicitly finding an EMM for geometric brownian motion (propoition 3.3). Here the key mathematical tool i Giranov theorem, which tell u how to convert to and from an EMM. Reader with financial background will recognize thi converion a ubtracting the market price of rik. For general contingent claim we of coure cannot reduce valuation to a imple formula. But the technique ued here indicate how we ought to proceed numerically. It i a two-tep proce. Firt, we back out an EMM from underlying market price. hen we compute the value of the claim a it dicounted expectation under the rik neutral meaure. here i a potential obtacle to thi procedure: an EMM might not exit. For thi we appeal to a powerful guarantee called the fundamental theorem of aet pricing, which aert the exitence of an EMM if and only if the market i arbitrage-free. 2. heory Let S be a continuouly traded aet and B be a rik free bond, often referred to a the numeraire. We aume throughout that the bond B i governed (under the phyical meaure P ) by db t = rb t dt. We ay that r i the pot rate of interet, alo called the hort rate. If we hold X amount of S and Y amount of B over a period from [, t] then the gain of our portfolio over [, t] are given by G t = X ds + Y db.
2 2 JOHN HICKSUN We call (X, Y ) a trading trategy. he value of thi trading trategy at time t i jut a linear combination V t = X t S t + Y t B t. We ay that a trategy i elf-financing if V t = V + G t for all t, and we define an arbitrage opportunity to be a elf-financing trading trategy (X, Y ) for which V =, V t, and E(V t ) > for ome t >. For any proce H we define H = B H and we ay that H ha been dicounted by the numeraire. It can be hown that if a trading trategy i elf-financing then the dicounted value proce equal the initial value plu the gain in the dicounted aet proce. We will not need thi reult but we will make ue of the convere: Propoition 2.. Suppoe that Ṽ t = Ṽ + hen the trading trategy (X, Y ) i elf-financing. X d S. Proof. Oberve that B i continuou with locally bounded variation and therefore it quadratic covariation with any other proce vanihe. Combining thi with integration by part, S t = B t St = And it follow that S db + X ds = B d S + [B, S] = B X d S + Applying integration by part to the value proce, = V t = B t Ṽ t = = B X d S + B dṽ + B X d S + herefore (X, Y ) i elf-financing. B X S db + Ṽ db = X S db + Y db = S db + X S db. B X d S + B Y B db X ds + B d S B V db Y db = G t. Suppoe there i ome meaure Q P and a Q quare-integrable martingale M uch that ds t = rs t dt + dm t. hen we ay that Q i a pot martingale meaure (for S). Likewie, if there i ome Q P uch that S i a Q martingale then we ay that Q i an equivalent martingale meaure (for S). hee definition are roughly equivalent and we will how in the next propoition that the firt implie the econd. he convere i morally true, but we don t need it and it require additionally the quare-integrability of S.
3 BLACK SCHOLES HE MARINGALE APPROACH 3 Propoition 2.2. If Q P i a pot martingale meaure then it i an equivalent martingale meaure. Proof. By claical calculu, db t Integrating by part with thi reult give u S t = B t S t = = rbt dt. B ds + S db. From our hypothei that Q i a pot martingale meaure we therefore have S t = r S d + B dm r S d = B dm. Becaue M i quare-integrable it follow that S t i a Q (quare-integrable) martingale, i.e. Q i an equivalent martingale meaure. Let C be a terminal claim on S at time. hat i C : [, ] R and C = f(s ). We ay that a elf-financing trategy replicate, or hedge, a terminal claim if V = C. If we can replicate the value of a claim at time then, to prevent arbitrage, the value of the claim at an earlier time mut equal the value of the replicating portfolio. We will now demontrate uch a hedging trategy. he following tatement form the bai for martingale pricing. Propoition 2.3. Suppoe S admit an equivalent martingale meaure Q. In the abence of arbitrage opportunitie, C t = B t E Q {B C F t }. Proof. Define M t = B t E Q {B C F t } and oberve that B M i a Q martingale. If W denote Q Brownian motion then by martingale repreentation, M t = M + H d W. By hypothei S i a martingale and therefore, for ome predictable K, we have the martingale repreentation S t = S + And by Ito lemma, uing f( S t ) = B t St = S t, S t = S + B d S = S + K d W. B K d W. Let X = H/K and Y = B (M XS) where G denote the gain of the trading trategy (X, Y ). By definition of the value proce, V t = X t S t + Y t B t = X t S t + M t X t S t B t B t = M t.
4 4 JOHN HICKSUN And in particular, at time, V = M = B E Q {B C F } = B B C E Q { F } = C. herefore (X, Y ) i a replicating portfolio for C. Oberve that V + X d S = V + X K d W = V + H d W = V + Bt M t M = Ṽt. And by propoition we ee that (X, Y ) i elf-financing. We may conclude that M t = C t, or ele there i an arbitrage opportunity. In particular, if M < C then the trategy (X, Y, M /C ) in the extended market coniting of S, B, and C ha V = M (M /C )C =, but V = M (M /C )C = C (M /C )C = C ( M /C ) >. 3. example Suppoe C i a european call with trike K and expiry on an underlying aet S. We aume that S walk like geometric brownian motion under the phyical meaure P. hat i, for contant µ and σ, and W t N (, t), ds t = S t (µdt + σdw t ). he payoff of the call i given by C = max(s K, ). By propoition 2.2, the preent value of C i given by the dicounted expectation of it payoff under an equivalent martingale meaure Q: C = e r E Q {max(s K, )}. Writing thi expreion in term of the max function make it difficult to work with; with ome imple algebra we can tranform the equation into a more ueful form: Propoition 3.. Under an equivalent martingale meaure Q, the price of a european call i given by C = e r E Q {S {S >K}} e r KQ(S > K). Proof. C = e r E Q {max(s K, )} = e r E Q {(S K) {S >K}} = e r E Q {S {S >K} K {S >K}} = e r E Q {S {S >K}} e r KE Q { {S >K}} = e r E Q {S {S >K}} e r KQ(S > K). It i clear from thi expreion that we have two difficult term to evaluate: E Q {S {S >K}} and Q(S > K). Our work will proceed a follow. Firt, we will decribe the evolution of the aet S (propoition 2). Next we will etablih the exitence of an equivalent martingale meaure Q and characterize the evolution of S under Q (propoition 3). We will then ue our reult about S to characterize the pace {S > K} (propoition 4). Finally, we will tackle the difficult term individually (propoition 5 and 6). Putting thee reult together will yield the Black-Schole formula (theorem 7).
5 BLACK SCHOLES HE MARINGALE APPROACH 5 Propoition 3.2. Let W t N (, t). If a proce S walk like geometric brownian motion then S t = S exp ( (µ σ 2 /2)t + σw t ). Proof. Firt, oberve that the given differential dynamic of S t are properly undertood a a notational horthand for the integral S t = S + µ S t dt + σ S t dw t. he former integral i newtonian and eaily handled. he latter i tochatic and require more care. By Ito lemma, f(s t ) f(s ) = Subtituting f(x) = log(x) we have log(s t /S ) = f (S t )ds t + 2 ds t S t 2 f (S t )d[s, S] t. d[s, S] t St 2. And by the integral dynamic of S t, linearity of the tochatic integral, and aociativity of the tochatic integral repectively ds t ( t t ) = d µ S t dt + σ S t dw t S t S t ( t ) ( t ) = d µ S t dt + d σ S t dw t = µ dt + σ dw t = µt + σw t. S t S t A an aide, oberve how formally ubtituting the (formal) differential dynamic for S t yield the ame reult. We can alo view aociativity a a formal cancellation of differential and integral operator. By propertie of quadratic variation, the above calculation, and Levy characterization We therefore have d[s, S] t S 2 t [ = ds t t, S t ] ds t S t = [µt, µt] + 2[µt, σw t ] + [σw t, σw t ] = σ 2 [W t, W t ] = σ 2 t. log(s t /S ) = µt + σw t σ2 2 t = And from thi the reult trivially follow. ) (µ σ2 t + σw t. 2 Propoition 3.3. Suppoe S walk like geometric Brownian motion under P. hen S admit an equivalent martingale meaure Q. Specifically, let W t = W t r µ σ t. hen W t i Q Brownian motion and ds t = rs t dt + σs t d W t.
6 6 JOHN HICKSUN Proof. Let Q be equivalent to P and define Z = dq/dp, Z t = E{Z F t }. Clearly Z t i a P martingale and by martingale repreentation, we may write Z t a an integral againt Brownian motion for ome predictable proce J t : Z t = + J dw. Auming Z i well-behaved, we can find H uch that Z H = J. herefore Z t = + Z H dw. Letting N = H W, by definition of the tochatic exponential, Z t = E(N) t. By Giranov heorem, the following expreion i a Q local martingale: [ ] σs dw d Z, σs τ dw τ. Z Our earlier work how that [ ] Z, σs τ dw τ [ = Z τ H τ dw τ, σs τ dw τ ] And by propertie of quadratic variation and Levy theorem, [ ] d Z, σs τ dw τ = Z H σs d[w, W ] = Z Z Setting H t = r µ, we ee that the following i a Q local martingale: σ σs dw H σs d = µs dt + σs dw H σs d. rs d = S t We refer to H a the market price of rik, or the Sharpe ratio. Oberve that t [ ] d[z, W ] = d Z τ H τ dw τ, dw τ = H d[w, W ]. Z Z And by Giranov theorem we proceed to define another Q local martingale W t = W t + Z d[z, W ] = W t + H t dt = W t + r µ σ t. rs d. Clearly W t i continuou, [ W t, W t ] = [W t, W t ] = t, and o by Levy characterization W t i Brownian motion under Q. Furthermore, by linearity σs t d W t + rs d = hi of coure can be expreed differentially a σs t dw t + ds t = rs t dt + σs t d W t. µs t d = S t. Brownian motion i quare-integrable and therefore Q i a pot martingale meaure. he reult follow by propoition 2.2.
7 BLACK SCHOLES HE MARINGALE APPROACH 7 Propoition 3.4. Let d = ( σ log(k/s ) (µ σ 2 /2) ). hen { } W {S > K} = > d. Proof. By Propoition 2, {S > K} = {S exp ( (µ σ 2 /2) + σw ) > K}. And olving for the random variable W give u { {S > K} = W > ( log(k/s ) (µ σ 2 /2) )}. σ he reult follow, dividing by (it i more convenient to work with a unit-variance random variable). Propoition 3.5. Let d 2 = ( σ log(s /K) + (r σ 2 /2) ). hen Q(S > K) = N (d 2 ;, ). Proof. By propoition 4 and 3 repectively we have ( ) ( W W + ( r µ ) ) Q(S > K) = Q σ > d = Q > d = Q ( ) ( ) W (r µ) W > d + σ = Q > d 2 = N (d 2 ;, ). Propoition 3.6. Let d = d 2 + σ, where d 2 i defined a in Propoition 5. hen Proof. Expanding the expectation, E Q {S {S >K}} = E Q {S {S >K}} = S e r N (d 2 + σ ;, ). S >K S dq = {S >K}S dq. By propoition 4 we can ubtitute above with {S >K}S dq = {W > σ (log(k/s ) (µ σ 2 /2) )} S exp ( (µ σ 2 ) /2) + σw dq Let U = W /. hen changing variable we have {S >K}S dq = {U >d2}s exp ((r σ 2 /2) + σ ) U dq. By the law of the unconciou tatitician (d(u Q)/dx denote the denity of U under Q) ( {S >K}S dq = {x>d2} S exp (r σ 2 /2) + σ ) d(u Q) x dx. dx R
8 8 JOHN HICKSUN And becaue U N (, ) under Q we have {S >K}S dq = S exp d 2 = S e r exp 2π Let u = x σ. hen d 2 E Q {S {S >K}} = S e r ( (r σ 2 /2) + σ x (σ ) x x2 2 σ2 2 dx. d 2 σ 2π e u2 2 du ( = S e r N ( d 2 σ ) ;, ) = S e r N (d 2 + σ ;, ) ) e x2 /2 dx 2π heorem 3.7. (Black-Schole) Let d 2 and d be defined a in Propoition 4 and 6 repectively. hen P = S N (d ;, ) e r KN (d 2 ;, ). Proof. hi reult follow trivially from Propoition, 5, and 6: P = e r E Q {S {S >K}} e r KQ(S > K) = e r S e r N (d 2 + σ ;, ) e r KN (d 2 ;, ) = S N (d 2 + σ ;, ) e r KN (d 2 ;, ).
9 BLACK SCHOLES HE MARINGALE APPROACH 9 Reference [] Protter, Philip. Stochatic integration and differential equation. Springer, 99. [2] Muiela, M, and Rutkowki, M. Martingale method in financial modelling. Vol. 36. Springer, 26. [3] [4] frey/skript-fimaii.pdf [5] zd.pdf [6] mh278/tochatic calculu.pdf
Itô-Skorohod stochastic equations and applications to finance
Itô-Skorohod tochatic equation and application to finance Ciprian A. Tudor Laboratoire de Probabilité et Modèle Aléatoire Univerité de Pari 6 4, Place Juieu F-755 Pari Cedex 5, France Abtract We prove
More informationPricing 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 informationMartingale Approach to Pricing and Hedging
Introduction and echniques Lecture 9 in Financial Mathematics UiO-SK451 Autumn 15 eacher:s. Ortiz-Latorre Martingale Approach to Pricing and Hedging 1 Risk Neutral Pricing Assume that we are in the basic
More information1.1 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 informationBasic Arbitrage Theory KTH Tomas Björk
Basic Arbitrage Theory KTH 2010 Tomas Björk Tomas Björk, 2010 Contents 1. Mathematics recap. (Ch 10-12) 2. Recap of the martingale approach. (Ch 10-12) 3. Change of numeraire. (Ch 26) Björk,T. Arbitrage
More informationBluff Your Way Through Black-Scholes
Bluff our Way Through Black-Scholes Saurav Sen December 000 Contents What is Black-Scholes?.............................. 1 The Classical Black-Scholes Model....................... 1 Some Useful Background
More informationNon-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 informationStochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models
Stochastic Processes and Stochastic Calculus - 9 Complete and Incomplete Market Models Eni Musta Università degli studi di Pisa San Miniato - 16 September 2016 Overview 1 Self-financing portfolio 2 Complete
More informationLecture 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 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 informationChange of Measure (Cameron-Martin-Girsanov Theorem)
Change of Measure Cameron-Martin-Girsanov Theorem Radon-Nikodym derivative: Taking again our intuition from the discrete world, we know that, in the context of option pricing, we need to price the claim
More informationLecture 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 informationRisk Neutral Valuation
copyright 2012 Christian Fries 1 / 51 Risk Neutral Valuation Christian Fries Version 2.2 http://www.christian-fries.de/finmath April 19-20, 2012 copyright 2012 Christian Fries 2 / 51 Outline Notation Differential
More informationBasic Concepts and Examples in Finance
Basic Concepts and Examples in Finance Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 5, 2017 ICMAT / UC3M The Financial Market The Financial Market We assume there are
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 informationHedging 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 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 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 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 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 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 informationQueens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane.
Queens College, CUNY, Department of Computer Science Computational Finance CSCI 365 / 765 Fall 2017 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2017 14 Lecture 14 November 15, 2017 Derivation of the
More informationSTOCHASTIC 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 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 informationLecture 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 informationReplication 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 informationEnlargement of filtration
Enlargement of filtration Bernardo D Auria email: bernardo.dauria@uc3m.es web: www.est.uc3m.es/bdauria July 6, 2017 ICMAT / UC3M Enlargement of Filtration Enlargement of Filtration ([1] 5.9) If G is a
More informationPartial differential approach for continuous models. Closed form pricing formulas for discretely monitored models
Advanced Topics in Derivative Pricing Models Topic 3 - Derivatives with averaging style payoffs 3.1 Pricing models of Asian options Partial differential approach for continuous models Closed form pricing
More informationLévy models in finance
Lévy models in finance Ernesto Mordecki Universidad de la República, Montevideo, Uruguay PASI - Guanajuato - June 2010 Summary General aim: describe jummp modelling in finace through some relevant issues.
More informationSTOCHASTIC INTEGRALS
Stat 391/FinMath 346 Lecture 8 STOCHASTIC INTEGRALS X t = CONTINUOUS PROCESS θ t = PORTFOLIO: #X t HELD AT t { St : STOCK PRICE M t : MG W t : BROWNIAN MOTION DISCRETE TIME: = t < t 1
More informationCONTINUOUS 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 informationStochastic Calculus - An Introduction
Stochastic Calculus - An Introduction M. Kazim Khan Kent State University. UET, Taxila August 15-16, 17 Outline 1 From R.W. to B.M. B.M. 3 Stochastic Integration 4 Ito s Formula 5 Recap Random Walk Consider
More informationFinancial 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 informationRMSC 4005 Stochastic Calculus for Finance and Risk. 1 Exercises. (c) Let X = {X n } n=0 be a {F n }-supermartingale. Show that.
1. EXERCISES RMSC 45 Stochastic Calculus for Finance and Risk Exercises 1 Exercises 1. (a) Let X = {X n } n= be a {F n }-martingale. Show that E(X n ) = E(X ) n N (b) Let X = {X n } n= be a {F n }-submartingale.
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 informationBasic 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 informationHelp Session 2. David Sovich. Washington University in St. Louis
Help Session 2 David Sovich Washington University in St. Louis TODAY S AGENDA Today we will cover the Change of Numeraire toolkit We will go over the Fundamental Theorem of Asset Pricing as well EXISTENCE
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 informationSTOCHASTIC VOLATILITY AND OPTION PRICING
STOCHASTIC VOLATILITY AND OPTION PRICING Daniel Dufresne Centre for Actuarial Studies University of Melbourne November 29 (To appear in Risks and Rewards, the Society of Actuaries Investment Section Newsletter)
More informationOption Pricing. 1 Introduction. Mrinal K. Ghosh
Option Pricing Mrinal K. Ghosh 1 Introduction We first introduce the basic terminology in option pricing. Option: An option is the right, but not the obligation to buy (or sell) an asset under specified
More informationBIRKBECK (University of London) MSc EXAMINATION FOR INTERNAL STUDENTS MSc FINANCIAL ENGINEERING DEPARTMENT OF ECONOMICS, MATHEMATICS AND STATIS- TICS
BIRKBECK (University of London) MSc EXAMINATION FOR INTERNAL STUDENTS MSc FINANCIAL ENGINEERING DEPARTMENT OF ECONOMICS, MATHEMATICS AND STATIS- TICS PRICING EMMS014S7 Tuesday, May 31 2011, 10:00am-13.15pm
More informationThe Black-Scholes Model
The Black-Scholes Model Liuren Wu Options Markets (Hull chapter: 12, 13, 14) Liuren Wu ( c ) The Black-Scholes Model colorhmoptions Markets 1 / 17 The Black-Scholes-Merton (BSM) model Black and Scholes
More informationMath 416/516: Stochastic Simulation
Math 416/516: Stochastic Simulation Haijun Li lih@math.wsu.edu Department of Mathematics Washington State University Week 13 Haijun Li Math 416/516: Stochastic Simulation Week 13 1 / 28 Outline 1 Simulation
More informationHedging of Contingent Claims under Incomplete Information
Projektbereich B Discussion Paper No. B 166 Hedging of Contingent Claims under Incomplete Information by Hans Föllmer ) Martin Schweizer ) October 199 ) Financial support by Deutsche Forschungsgemeinschaft,
More informationKØBENHAVNS 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 informationRisk Neutral Measures
CHPTER 4 Risk Neutral Measures Our aim in this section is to show how risk neutral measures can be used to price derivative securities. The key advantage is that under a risk neutral measure the discounted
More information************* with µ, σ, and r all constant. We are also interested in more sophisticated models, such as:
Continuous Time Finance Notes, Spring 2004 Section 1. 1/21/04 Notes by Robert V. Kohn, Courant Institute of Mathematical Sciences. For use in connection with the NYU course Continuous Time Finance. This
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 informationLecture 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 informationHow to hedge Asian options in fractional Black-Scholes model
How to hedge Asian options in fractional Black-Scholes model Heikki ikanmäki Jena, March 29, 211 Fractional Lévy processes 1/36 Outline of the talk 1. Introduction 2. Main results 3. Methodology 4. Conclusions
More information1 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 informationDerivative Securities
Derivative Securities he Black-Scholes formula and its applications. his Section deduces the Black- Scholes formula for a European call or put, as a consequence of risk-neutral valuation in the continuous
More information4. Black-Scholes Models and PDEs. Math6911 S08, HM Zhu
4. Black-Scholes Models and PDEs Math6911 S08, HM Zhu References 1. Chapter 13, J. Hull. Section.6, P. Brandimarte Outline Derivation of Black-Scholes equation Black-Scholes models for options Implied
More informationThe Black-Scholes Model
The Black-Scholes Model Liuren Wu Options Markets Liuren Wu ( c ) The Black-Merton-Scholes Model colorhmoptions Markets 1 / 18 The Black-Merton-Scholes-Merton (BMS) model Black and Scholes (1973) and Merton
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 informationContinuous Time Finance. Tomas Björk
Continuous Time Finance Tomas Björk 1 II Stochastic Calculus Tomas Björk 2 Typical Setup Take as given the market price process, S(t), of some underlying asset. S(t) = price, at t, per unit of underlying
More informationMARTINGALES AND LOCAL MARTINGALES
MARINGALES AND LOCAL MARINGALES If S t is a (discounted) securtity, the discounted P/L V t = need not be a martingale. t θ u ds u Can V t be a valid P/L? When? Winter 25 1 Per A. Mykland ARBIRAGE WIH SOCHASIC
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 informationThe stochastic calculus
Gdansk A schedule of the lecture Stochastic differential equations Ito calculus, Ito process Ornstein - Uhlenbeck (OU) process Heston model Stopping time for OU process Stochastic differential equations
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 19 11/20/2013. Applications of Ito calculus to finance
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.7J Fall 213 Lecture 19 11/2/213 Applications of Ito calculus to finance Content. 1. Trading strategies 2. Black-Scholes option pricing formula 1 Security
More information2.3 Mathematical Finance: Option pricing
CHAPTR 2. CONTINUUM MODL 8 2.3 Mathematical Finance: Option pricing Options are some of the commonest examples of derivative securities (also termed financial derivatives or simply derivatives). A uropean
More informationHow Much Should You Pay For a Financial Derivative?
City University of New York (CUNY) CUNY Academic Works Publications and Research New York City College of Technology Winter 2-26-2016 How Much Should You Pay For a Financial Derivative? Boyan Kostadinov
More informationPractical Hedging: From Theory to Practice. OSU Financial Mathematics Seminar May 5, 2008
Practical Hedging: From Theory to Practice OSU Financial Mathematics Seminar May 5, 008 Background Dynamic replication is a risk management technique used to mitigate market risk We hope to spend a certain
More informationWe can also develop useful upper bounds. The value of the arithmetic Asian option is
Aian option Introduction Aian option are popular in currency and coodity arket becaue they offer a cheaper ethod of hedging expoure to regular periodic cah flow they are le uceptible to anipulation of
More informationTHE KELLY PORTFOLIO RULE DOMINATES
THE KELLY PORTFOLIO RULE DOMINATES ÇISEM BEKTUR Abtract We tudy an evolutionary market model with long-lived aet Invetor are allowed to ue general dynamic invetment trategie We find ufficient condition
More informationThe Derivation and Discussion of Standard Black-Scholes Formula
The Derivation and Discussion of Standard Black-Scholes Formula Yiqian Lu October 25, 2013 In this article, we will introduce the concept of Arbitrage Pricing Theory and consequently deduce the standard
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 informationPath 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 informationProbability 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 informationEuropean call option with inflation-linked strike
Mathematical Statistics Stockholm University European call option with inflation-linked strike Ola Hammarlid Research Report 2010:2 ISSN 1650-0377 Postal address: Mathematical Statistics Dept. of Mathematics
More informationHedging 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 informationVolatility Smiles and Yield Frowns
Volatility Smile and Yield Frown Peter Carr pcarr@nyc.rr.com Initial verion: September 1, 015 Current verion: December 13, 016 File reference: Analogy Vol Smile Yield Frown.te Abtract A volatility mile
More informationBlack-Scholes Option Pricing
Black-Scholes Option Pricing The pricing kernel furnishes an alternate derivation of the Black-Scholes formula for the price of a call option. Arbitrage is again the foundation for the theory. 1 Risk-Free
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 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 informationConfidence Intervals for One Variance using Relative Error
Chapter 653 Confidence Interval for One Variance uing Relative Error Introduction Thi routine calculate the neceary ample ize uch that a ample variance etimate will achieve a pecified relative ditance
More informationOPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE
DOI: 1.1214/ECP.v7-149 Elect. Comm. in Probab. 7 (22) 79 83 ELECTRONIC COMMUNICATIONS in PROBABILITY OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE FIMA KLEBANER Department of Mathematics & Statistics,
More informationThe 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 informationLecture 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 informationAre stylized facts irrelevant in option-pricing?
Are stylized facts irrelevant in option-pricing? Kyiv, June 19-23, 2006 Tommi Sottinen, University of Helsinki Based on a joint work No-arbitrage pricing beyond semimartingales with C. Bender, Weierstrass
More informationThe 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 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 informationCHAPTER 12. Hedging. hedging strategy = replicating strategy. Question : How to find a hedging strategy? In other words, for an attainable contingent
CHAPTER 12 Hedging hedging dddddddddddddd ddd hedging strategy = replicating strategy hedgingdd) ddd Question : How to find a hedging strategy? In other words, for an attainable contingent claim, find
More informationRisk-Neutral Valuation
N.H. Bingham and Rüdiger Kiesel Risk-Neutral Valuation Pricing and Hedging of Financial Derivatives W) Springer Contents 1. Derivative Background 1 1.1 Financial Markets and Instruments 2 1.1.1 Derivative
More informationMAS452/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 informationChapter 15: Jump Processes and Incomplete Markets. 1 Jumps as One Explanation of Incomplete Markets
Chapter 5: Jump Processes and Incomplete Markets Jumps as One Explanation of Incomplete Markets It is easy to argue that Brownian motion paths cannot model actual stock price movements properly in reality,
More informationBlack-Scholes Model and Risk Neutral Pricing
Inroducion echniques Exercises in Financial Mahemaics Lis 3 UiO-SK45 Soluions Hins Auumn 5 eacher: S Oriz-Laorre Black-Scholes Model Risk Neural Pricing See Benh s book: Exercise 44, page 37 See Benh s
More information1 The continuous time limit
Derivative Securities, Courant Institute, Fall 2008 http://www.math.nyu.edu/faculty/goodman/teaching/derivsec08/index.html Jonathan Goodman and Keith Lewis Supplementary notes and comments, Section 3 1
More informationThe British Lookback Option with Fixed Strike
The Britih Lookback Option with Fixed Strike Yerkin Kitapbayev Firt verion: 14 February 2014 Reearch Report No. 2, 2014, Probability and Statitic Group School of Mathematic, The Univerity of Mancheter
More informationAdvanced Stochastic Processes.
Advanced Stochastic Processes. David Gamarnik LECTURE 16 Applications of Ito calculus to finance Lecture outline Trading strategies Black Scholes option pricing formula 16.1. Security price processes,
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 information1 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 informationLECTURE 4: BID AND ASK HEDGING
LECTURE 4: BID AND ASK HEDGING 1. Introduction One of the consequences of incompleteness is that the price of derivatives is no longer unique. Various strategies for dealing with this exist, but a useful
More informationStochastic Calculus, Application of Real Analysis in Finance
, Application of Real Analysis in Finance Workshop for Young Mathematicians in Korea Seungkyu Lee Pohang University of Science and Technology August 4th, 2010 Contents 1 BINOMIAL ASSET PRICING MODEL Contents
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 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 informationOptimal stopping problems for a Brownian motion with a disorder on a finite interval
Optimal stopping problems for a Brownian motion with a disorder on a finite interval A. N. Shiryaev M. V. Zhitlukhin arxiv:1212.379v1 [math.st] 15 Dec 212 December 18, 212 Abstract We consider optimal
More informationLecture: Continuous Time Finance Lecturer: o. Univ. Prof. Dr. phil. Helmut Strasser
Lecture: Continuous Time Finance Lecturer: o. Univ. Prof. Dr. phil. Helmut Strasser Part 1: Introduction Chapter 1: Review of discrete time finance Part 2: Stochastic analysis Chapter 2: Stochastic processes
More informationGreek parameters of nonlinear Black-Scholes equation
International Journal of Mathematics and Soft Computing Vol.5, No.2 (2015), 69-74. ISSN Print : 2249-3328 ISSN Online: 2319-5215 Greek parameters of nonlinear Black-Scholes equation Purity J. Kiptum 1,
More informationContinuous 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