The Lognormal Interest Rate Model and Eurodollar Futures
|
|
- Silas Long
- 6 years ago
- Views:
Transcription
1 GLOBAL RESEARCH ANALYTICS The Lognormal Interest Rate Model and Eurodollar Futures CITICORP SECURITIES,INC. 399 Park Avenue New York, NY 143 Keith Weintraub Director, Analytics Michael Hogan Ex Citibank May 1998 The Eurodollar futures contract is basic to a large class of interest rate derivative instruments and failure to assign reasonable values to it is a serious flaw. We will use the lognormal short-term interest rate model as it is set up and explicitly solved in Dothan 1978 to show that the lognormal model assigns an infinite value to the Eurodollar futures contract. There are two reasons for this peculiar behavior. The first is the long positive tail of the lognormal distribution. Rate processes that are bounded by some absolute rate do not have this problem. The square root diffusion in which futures and forward prices can be calculated explicitly has a mild case of it. For normal values of the parameters, futures that expire within the first 1 years or so will have finite prices. The other reason is the design of the Eurodollar futures contract itself. At expiration, the Eurodollar futures contract settles at the prevailing value of the 9-day LIBOR rate. The LIBOR rate is quoted as an add-on or money market rate. A typical quote for a 9-day LIBOR rate is 4%; this means that $1 will get you $ in 9 days. If r L is the 9-day LIBOR rate and Z is a 9-day zero-coupon bond price on a nominal $1 of principal, then the following relationship exists: 1 r L 4 1 Z/Z. Eurodollar futures are quoted on price, that is a LIBOR rate of 4 would be quoted as a futures price of The T-bill Treasury bill futures contract is quoted on the same basis, although Treasury bill rates themselves are not quoted on a money market basis, so what we say about the Eurodollar futures contract applies to them as well. For analytical purposes, Eurodollar futures rates; are often used as forward rates, that is, if the Eurodollar futures rate for the contract maturing at time t is r, then it is treated as if it is the forward rate for purchase at time t of a zero-coupon bond maturing at time t + 9 days. This approach is wrong for two reasons. First, Cox, Ingersoll, and Ross 1981 discuss the difference between futures prices and forwards prices. They show that in the limit of instantaneous resettlement the futures price for the contract that settles at X at time t is E [X where E is expectation under the familiar pricing measure, while the value of the forward contract is t / [ t 3 E [X exp rsds E exp rsds
2 where rt is the path of instantaneous interest rates. Thus a technical adjustment is almost always made for the difference between forwards and futures due to the periodic resettlement feature. This adjustment is as large as possible when the terminal payoff is most highly correlated with the path of short-term rates, and Eurodollar futures are probably as extreme as possible in this respect. Second, Cox, Ingersoll, and Ross 1981 work out the case of instantaneously settled bill futures in detail. There is a key difference, however, between the Eurodollar futures contract and the contract that they very reasonably treat as a bill future. In effect, the Eurodollar futures pay at the wrong time. A forward contract on a zero-coupon bond expiring at time t would pay its rate at time t + 9 days, not at time t when the rate is set. The rate is earned over the period between time t and time t + 9 days. Meanwhile, the futures contract settles at the same rate at time t, which may be quite a strain. The degree to which it is a strain is reflected in the presence of Z in the denominator of 1. A forward rate agreement FRA also pays when the rate is set, but a 9-day FRA properly discounts the cash flows, and it would settle for an amount 1 Z, rather than 1 Z/Z. This discounting is entirely natural, and the price of such a product depends only on the yield curve, not on any assumptions about the rate process. STATEMENT AND PROOF OF RESULTS The analyses below closely follow Dothan It is useful to begin by considering the conditional discounting function. We will work with the following two instantaneous short-rate processes: 4 5 dr αrdt + σrdb t, d log r κθ log rdt + σdb t, where κ and θ are non-negative. Equation 5 is the model of Black and Karasinsky Proposition 1: If the short-term rate process r follows 4 we have [ cdr, r t, t E exp /π t rs ds r r, rt r t µsinhπµ K iµρ K iµ ρ t ρ ρ t exp 1 + µ t /4 dµ [ σ / logrt r t /σ /r σ t/ t φ [ / cd logrt /r σ t/ r, r t, t r t φ, where ρ s δr s, δx 8x/σ, K iµ is a parabolic cylinder function or Bessel function of the third kind, and φx 1/ π exp x / is the normal density function. Remark: The function name cd is a mnemonic for conditional discounting. Proof: Conditional on the terminal points, the process is a log Brownian bridge process between r and r t, and in particular, its distribution does not depend on α. We therefore compute Dothan s base case of α. Let f a,b u 1 if a < u < b and otherwise. Let [ t F a,b r, s E exp rx dx f a,b rt t s rt s r.
3 According to the Feynman-Kac formula Durrett 1984 this expectation solves the differential equation σ r F rr rf F s, subject to F r, f a,b r. This is equivalent to equation 5 in Dothan 1978 with a small change in notation. As in Dothan 1978, let z δr and τ σ s/. Let dz zσ /8 be the inverse transformation to δ. Then hz, τ F a,b dz, s satisfies 6 z h zz + zh z z + 1h 4h τ subject to the terminal boundary condition hz, f δa,δb z. According to sec 5.14 of Lebedev 197 hz, /π µsinhπµ K iµz hζ, K iµζ dµ dζ. z ζ Then following Dothan, we have hz, τ /π µsinhπµ K iµz z exp 1 + µ τ/4 hζ, K iµζ ζ dµ dζ. To obtain the conditional expectation, we divide by the probability that rt a, b and take the limit as a b, yielding /π µ sinhπµ K iµzk iµ β zβ exp 1 + µ t/4 dµ [ σ / r t σ logb/r σ t/ / t φ. σ t Substituting the definition of z in terms of r completes the calculation. We now show that the instantaneously resettled futures price is. Let Zt, τ, rt be the price at time t of a zero-coupon bond that pays $1 at time τ. Z is a function of the short-term rate rt at time t. From 1 and we see that it suffices to show Proposition : E[1/Zt, τ, rt. Remark: Because there is no restriction on α, Proposition holds even in the case of linear mean reversion as in the Courtadon model Hull Proof: The proof will proceed in two parts. First assume that the short-term rate process r follows 4. Let 7 η α σ //σ where α is defined in 4. By unconditioning we have Zt, τ, r cdr, ρ, τ t φ logρ/r α σ /τ t 1 σ τ t ρ σ τ t dρ 3
4 cd r, ρ, τ t exp η logρ/r η τ t/ 1 ρ σ τ t dρ cd r, ρ, τ tρ/r η exp η τ t/ 1 ρ σ τ t dρ. According to 8.43 of Gradshteyn and Ryzhik So, it is easy show that 9 K iµ z cosµt exp z coshtdt. K iµ z exp z /πz and that equality holds asymptotically. Substituting this into the formula for cd it is therefore easy to show that 1 Zt, τ, r C exp δrr η for some constant C >. Let r m be such that for r > r m. Then [ E 1/Zt, τ, rt r r 1. 1 C σ t Zt, τ, r Cr η exp δr 1/Zt, τ, u φ r m exp 8u/σ u η φ logu/r αt + σ t/ du u logu/r αt + σ t/ du u Now assume that the short-term rate process r follows 5. Without loss of generality we can assume θ. Then Z satisfies the differential equation σ Z t Z rr r Z r κ log r + σ r rz. Let then G satisfies G s G [ κ 1 κ log rt Z G exp σ, κ log r r σ σ r + G rr rσ G r, with boundary value Gτ, τ, rτ exp κ log rτ σ. 4
5 By the Feynman-Kac formula Gt, τ, r E r exp exp τ t [ κ 1 κ log rτ σ κ log rs σ rs ds where r evolves according to process 4 with α σ /. This implies that τ Gt, τ, r e κt/ E r κ log rτ exp rs ds exp t σ e κt/ logρ/r + σ τ t 1 cdr, ρ, τ t φ σ τ t ρ σ τ t κ log ρ exp σ dρ. Using an argument similar to that for process 4 we get and Gt, τ, r Cr exp δr, Zt, τ, r Cr exp δr exp which as shown above is sufficient for our result.. κ log r σ, When there is no resettlement we have a forward contract. For such contracts, if the short-rate follows process 4 with α >, Proposition still holds. Equations 1 and 3 imply that it suffices to show Proposition 3 for forward contracts to have infinite value. Proposition 3: If the short-rate process r follows 4, then [ t E exp rs ds 1/Zt, τ, t if and only if α >. Proof: Let η be as in 7. Then [ E exp t rs ds 1/Zt, τ, t r r cd r, r, t/zt, τ, r r/r η exp η t/ dr. As in Proposition we have as r, for some constant C >. cd r, r, t C expδr 5
6 Thus, cd r, r, t/zt, τ, r r/r η exp η t/ dr is finite if and only if r m r η dr is, hence, if and only if η > 1. Working through the definition yields the proposition. 6
7 REFERENCES 1. Black, F., and P. Karasinski, Bond and Option Pricing when Short Rates are Lognormal, Financial Analysts Journal, July/August 1991, pp Cox, J. C., J. E. Ingersoll Jr., and S. A. Ross, The Relation Between Forward Prices and Futures Prices, Journal of Financial Economics , pp Dothan, L. U., On the Term Structure of Interest Rates, Journal of Financial Economics , pp Durrett, R., 1984, Brownian Motion and Martingales in Analysis, Wadsworth Advanced Books and Software, Belmont, CA. 5. Gradshteyn, I.S., and I.S. Ryzhik, 198, Table of Integrals, Series, and Products, Corrected and Enlarged Edition, Academic Press, New York, NY. 6. Hull, J., 1989, Options, Futures, and Other Derivative Securities, Prentice-Hall, Englewood Cliffs, NJ. 7. Lebedev, N. N., 197, Special Functions and Their Applications, Dover Publications, Inc., New York, NY. 7
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 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 information( ) since this is the benefit of buying the asset at the strike price rather
Review of some financial models for MAT 483 Parity and Other Option Relationships The basic parity relationship for European options with the same strike price and the same time to expiration is: C( KT
More informationMultiname and Multiscale Default Modeling
Multiname and Multiscale Default Modeling Jean-Pierre Fouque University of California Santa Barbara Joint work with R. Sircar (Princeton) and K. Sølna (UC Irvine) Special Semester on Stochastics with Emphasis
More informationInterest Rate Volatility
Interest Rate Volatility III. Working with SABR Andrew Lesniewski Baruch College and Posnania Inc First Baruch Volatility Workshop New York June 16-18, 2015 Outline Arbitrage free SABR 1 Arbitrage free
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 217 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 217 13 Lecture 13 November 15, 217 Derivation of the Black-Scholes-Merton
More informationLinear-Rational Term-Structure Models
Linear-Rational Term-Structure Models Anders Trolle (joint with Damir Filipović and Martin Larsson) Ecole Polytechnique Fédérale de Lausanne Swiss Finance Institute AMaMeF and Swissquote Conference, September
More informationA Simple Approach to CAPM and Option Pricing. Riccardo Cesari and Carlo D Adda (University of Bologna)
A imple Approach to CA and Option ricing Riccardo Cesari and Carlo D Adda (University of Bologna) rcesari@economia.unibo.it dadda@spbo.unibo.it eptember, 001 eywords: asset pricing, CA, option pricing.
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 informationShape of the Yield Curve Under CIR Single Factor Model: A Note
Shape of the Yield Curve Under CIR Single Factor Model: A Note Raymond Kan University of Chicago June, 1992 Abstract This note derives the shapes of the yield curve as a function of the current spot rate
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 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 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 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 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 informationHomework Assignments
Homework Assignments Week 1 (p 57) #4.1, 4., 4.3 Week (pp 58-6) #4.5, 4.6, 4.8(a), 4.13, 4.0, 4.6(b), 4.8, 4.31, 4.34 Week 3 (pp 15-19) #1.9, 1.1, 1.13, 1.15, 1.18 (pp 9-31) #.,.6,.9 Week 4 (pp 36-37)
More information13.3 A Stochastic Production Planning Model
13.3. A Stochastic Production Planning Model 347 From (13.9), we can formally write (dx t ) = f (dt) + G (dz t ) + fgdz t dt, (13.3) dx t dt = f(dt) + Gdz t dt. (13.33) The exact meaning of these expressions
More informationLocal vs Non-local Forward Equations for Option Pricing
Local vs Non-local Forward Equations for Option Pricing Rama Cont Yu Gu Abstract When the underlying asset is a continuous martingale, call option prices solve the Dupire equation, a forward parabolic
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 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 informationarxiv: v1 [q-fin.pr] 18 Feb 2010
CONVERGENCE OF HESTON TO SVI JIM GATHERAL AND ANTOINE JACQUIER arxiv:1002.3633v1 [q-fin.pr] 18 Feb 2010 Abstract. In this short note, we prove by an appropriate change of variables that the SVI implied
More informationStock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models
Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models David Prager 1 1 Associate Professor of Mathematics Anderson University (SC) Based on joint work with Professor Qing Zhang,
More informationSaddlepoint Approximation Methods for Pricing. Financial Options on Discrete Realized Variance
Saddlepoint Approximation Methods for Pricing Financial Options on Discrete Realized Variance Yue Kuen KWOK Department of Mathematics Hong Kong University of Science and Technology Hong Kong * This is
More informationMarket interest-rate models
Market interest-rate models Marco Marchioro www.marchioro.org November 24 th, 2012 Market interest-rate models 1 Lecture Summary No-arbitrage models Detailed example: Hull-White Monte Carlo simulations
More informationMSC FINANCIAL ENGINEERING PRICING I, AUTUMN LECTURE 6: EXTENSIONS OF BLACK AND SCHOLES RAYMOND BRUMMELHUIS DEPARTMENT EMS BIRKBECK
MSC FINANCIAL ENGINEERING PRICING I, AUTUMN 2010-2011 LECTURE 6: EXTENSIONS OF BLACK AND SCHOLES RAYMOND BRUMMELHUIS DEPARTMENT EMS BIRKBECK In this section we look at some easy extensions of the Black
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 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 3: Black-Scholes Equation and Its Numerical Evaluation
Chapter 3: Black-Scholes Equation and Its Numerical Evaluation 3.1 Itô Integral 3.1.1 Convergence in the Mean and Stieltjes Integral Definition 3.1 (Convergence in the Mean) A sequence {X n } n ln of random
More information2.1 Mean-variance Analysis: Single-period Model
Chapter Portfolio Selection The theory of option pricing is a theory of deterministic returns: we hedge our option with the underlying to eliminate risk, and our resulting risk-free portfolio then earns
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 informationPricing Variance Swaps under Stochastic Volatility Model with Regime Switching - Discrete Observations Case
Pricing Variance Swaps under Stochastic Volatility Model with Regime Switching - Discrete Observations Case Guang-Hua Lian Collaboration with Robert Elliott University of Adelaide Feb. 2, 2011 Robert Elliott,
More informationVolatility Smiles and Yield Frowns
Volatility Smiles and Yield Frowns Peter Carr NYU CBOE Conference on Derivatives and Volatility, Chicago, Nov. 10, 2017 Peter Carr (NYU) Volatility Smiles and Yield Frowns 11/10/2017 1 / 33 Interest Rates
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 informationMLEMVD: A R Package for Maximum Likelihood Estimation of Multivariate Diffusion Models
MLEMVD: A R Package for Maximum Likelihood Estimation of Multivariate Diffusion Models Matthew Dixon and Tao Wu 1 Illinois Institute of Technology May 19th 2017 1 https://papers.ssrn.com/sol3/papers.cfm?abstract
More informationPricing Dynamic Solvency Insurance and Investment Fund Protection
Pricing Dynamic Solvency Insurance and Investment Fund Protection Hans U. Gerber and Gérard Pafumi Switzerland Abstract In the first part of the paper the surplus of a company is modelled by a Wiener process.
More informationLecture 7: Computation of Greeks
Lecture 7: Computation of Greeks Ahmed Kebaier kebaier@math.univ-paris13.fr HEC, Paris Outline 1 The log-likelihood approach Motivation The pathwise method requires some restrictive regularity assumptions
More informationThe 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 informationθ(t ) = T f(0, T ) + σ2 T
1 Derivatives Pricing and Financial Modelling Andrew Cairns: room M3.08 E-mail: A.Cairns@ma.hw.ac.uk Tutorial 10 1. (Ho-Lee) Let X(T ) = T 0 W t dt. (a) What is the distribution of X(T )? (b) Find E[exp(
More informationOption Pricing Model with Stepped Payoff
Applied Mathematical Sciences, Vol., 08, no., - 8 HIARI Ltd, www.m-hikari.com https://doi.org/0.988/ams.08.7346 Option Pricing Model with Stepped Payoff Hernán Garzón G. Department of Mathematics Universidad
More informationCourse information FN3142 Quantitative finance
Course information 015 16 FN314 Quantitative finance This course is aimed at students interested in obtaining a thorough grounding in market finance and related empirical methods. Prerequisite If taken
More informationDefinition 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 information25. Interest rates models. MA6622, Ernesto Mordecki, CityU, HK, References for this Lecture:
25. Interest rates models MA6622, Ernesto Mordecki, CityU, HK, 2006. References for this Lecture: John C. Hull, Options, Futures & other Derivatives (Fourth Edition), Prentice Hall (2000) 1 Plan of Lecture
More informationA THREE-FACTOR CONVERGENCE MODEL OF INTEREST RATES
Proceedings of ALGORITMY 01 pp. 95 104 A THREE-FACTOR CONVERGENCE MODEL OF INTEREST RATES BEÁTA STEHLÍKOVÁ AND ZUZANA ZÍKOVÁ Abstract. A convergence model of interest rates explains the evolution of the
More informationInterest rate volatility
Interest rate volatility II. SABR and its flavors Andrew Lesniewski Baruch College and Posnania Inc First Baruch Volatility Workshop New York June 16-18, 2015 Outline The SABR model 1 The SABR model 2
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 informationStochastic Volatility (Working Draft I)
Stochastic Volatility (Working Draft I) Paul J. Atzberger General comments or corrections should be sent to: paulatz@cims.nyu.edu 1 Introduction When using the Black-Scholes-Merton model to price derivative
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 informationOn the pricing equations in local / stochastic volatility models
On the pricing equations in local / stochastic volatility models Hao Xing Fields Institute/Boston University joint work with Erhan Bayraktar, University of Michigan Kostas Kardaras, Boston University Probability
More informationForwards 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 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 informationTangent Lévy Models. Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford.
Tangent Lévy Models Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford June 24, 2010 6th World Congress of the Bachelier Finance Society Sergey
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 informationSYLLABUS. IEOR E4728 Topics in Quantitative Finance: Inflation Derivatives
SYLLABUS IEOR E4728 Topics in Quantitative Finance: Inflation Derivatives Term: Summer 2007 Department: Industrial Engineering and Operations Research (IEOR) Instructor: Iraj Kani TA: Wayne Lu References:
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 informationOptimal robust bounds for variance options and asymptotically extreme models
Optimal robust bounds for variance options and asymptotically extreme models Alexander Cox 1 Jiajie Wang 2 1 University of Bath 2 Università di Roma La Sapienza Advances in Financial Mathematics, 9th January,
More informationLocal Volatility Dynamic Models
René Carmona Bendheim Center for Finance Department of Operations Research & Financial Engineering Princeton University Columbia November 9, 27 Contents Joint work with Sergey Nadtochyi Motivation 1 Understanding
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 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 informationReading: You should read Hull chapter 12 and perhaps the very first part of chapter 13.
FIN-40008 FINANCIAL INSTRUMENTS SPRING 2008 Asset Price Dynamics Introduction These notes give assumptions of asset price returns that are derived from the efficient markets hypothesis. Although a hypothesis,
More informationAspects of Financial Mathematics:
Aspects of Financial Mathematics: Options, Derivatives, Arbitrage, and the Black-Scholes Pricing Formula J. Robert Buchanan Millersville University of Pennsylvania email: Bob.Buchanan@millersville.edu
More informationVolatility Smiles and Yield Frowns
Volatility Smiles and Yield Frowns Peter Carr NYU IFS, Chengdu, China, July 30, 2018 Peter Carr (NYU) Volatility Smiles and Yield Frowns 7/30/2018 1 / 35 Interest Rates and Volatility Practitioners and
More informationIntroduction to Affine Processes. Applications to Mathematical Finance
and Its Applications to Mathematical Finance Department of Mathematical Science, KAIST Workshop for Young Mathematicians in Korea, 2010 Outline Motivation 1 Motivation 2 Preliminary : Stochastic Calculus
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 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 informationInterest rate models in continuous time
slides for the course Interest rate theory, University of Ljubljana, 2012-13/I, part IV József Gáll University of Debrecen Nov. 2012 Jan. 2013, Ljubljana Continuous time markets General assumptions, notations
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 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 informationVariance Reduction for Monte Carlo Simulation in a Stochastic Volatility Environment
Variance Reduction for Monte Carlo Simulation in a Stochastic Volatility Environment Jean-Pierre Fouque Tracey Andrew Tullie December 11, 21 Abstract We propose a variance reduction method for Monte Carlo
More informationEconomics has never been a science - and it is even less now than a few years ago. Paul Samuelson. Funeral by funeral, theory advances Paul Samuelson
Economics has never been a science - and it is even less now than a few years ago. Paul Samuelson Funeral by funeral, theory advances Paul Samuelson Economics is extremely useful as a form of employment
More informationINTEREST RATES AND FX MODELS
INTEREST RATES AND FX MODELS 4. Convexity Andrew Lesniewski Courant Institute of Mathematics New York University New York February 24, 2011 2 Interest Rates & FX Models Contents 1 Convexity corrections
More information1 The Hull-White Interest Rate Model
Abstract Numerical Implementation of Hull-White Interest Rate Model: Hull-White Tree vs Finite Differences Artur Sepp Mail: artursepp@hotmail.com, Web: www.hot.ee/seppar 30 April 2002 We implement the
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 informationExtended Libor Models and Their Calibration
Extended Libor Models and Their Calibration Denis Belomestny Weierstraß Institute Berlin Vienna, 16 November 2007 Denis Belomestny (WIAS) Extended Libor Models and Their Calibration Vienna, 16 November
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 informationRobust Pricing and Hedging of Options on Variance
Robust Pricing and Hedging of Options on Variance Alexander Cox Jiajie Wang University of Bath Bachelier 21, Toronto Financial Setting Option priced on an underlying asset S t Dynamics of S t unspecified,
More informationShape of the Yield Curve Under CIR Single Factor Model: A Note
Shape of the Yield Curve Under CIR Single Factor Model: A Note Raymond Kan University of Toronto June, 199 Abstract This note derives the shapes of the yield curve as a function of the current spot rate
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 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 informationLecture 5: Review of interest rate models
Lecture 5: Review of interest rate models Xiaoguang Wang STAT 598W January 30th, 2014 (STAT 598W) Lecture 5 1 / 46 Outline 1 Bonds and Interest Rates 2 Short Rate Models 3 Forward Rate Models 4 LIBOR and
More informationApproximation Methods in Derivatives Pricing
Approximation Methods in Derivatives Pricing Minqiang Li Bloomberg LP September 24, 2013 1 / 27 Outline of the talk A brief overview of approximation methods Timer option price approximation Perpetual
More informationInterest rate models and Solvency II
www.nr.no Outline Desired properties of interest rate models in a Solvency II setting. A review of three well-known interest rate models A real example from a Norwegian insurance company 2 Interest rate
More informationAsymptotic 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 informationFrom 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 informationComputational 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 informationDependence Structure and Extreme Comovements in International Equity and Bond Markets
Dependence Structure and Extreme Comovements in International Equity and Bond Markets René Garcia Edhec Business School, Université de Montréal, CIRANO and CIREQ Georges Tsafack Suffolk University Measuring
More informationApplication of Moment Expansion Method to Option Square Root Model
Application of Moment Expansion Method to Option Square Root Model Yun Zhou Advisor: Professor Steve Heston University of Maryland May 5, 2009 1 / 19 Motivation Black-Scholes Model successfully explain
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 informationWKB Method for Swaption Smile
WKB Method for Swaption Smile Andrew Lesniewski BNP Paribas New York February 7 2002 Abstract We study a three-parameter stochastic volatility model originally proposed by P. Hagan for the forward swap
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 informationAveraged bond prices for Fong-Vasicek and the generalized Vasicek interest rates models
MATHEMATICAL OPTIMIZATION Mathematical Methods In Economics And Industry 007 June 3 7, 007, Herl any, Slovak Republic Averaged bond prices for Fong-Vasicek and the generalized Vasicek interest rates models
More informationLIBOR Convexity Adjustments for the Vasiček and Cox-Ingersoll-Ross models
LIBOR Convexity Adjustments for the Vasiček and Cox-Ingersoll-Ross models B. F. L. Gaminha 1, Raquel M. Gaspar 2, O. Oliveira 1 1 Dep. de Física, Universidade de Coimbra, 34 516 Coimbra, Portugal 2 Advance
More informationPricing CDOs with the Fourier Transform Method. Chien-Han Tseng Department of Finance National Taiwan University
Pricing CDOs with the Fourier Transform Method Chien-Han Tseng Department of Finance National Taiwan University Contents Introduction. Introduction. Organization of This Thesis Literature Review. The Merton
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 informationAn Equilibrium Model of the Term Structure of Interest Rates
Finance 400 A. Penati - G. Pennacchi An Equilibrium Model of the Term Structure of Interest Rates When bond prices are assumed to be driven by continuous-time stochastic processes, noarbitrage restrictions
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 informationUtility Indifference Pricing and Dynamic Programming Algorithm
Chapter 8 Utility Indifference ricing and Dynamic rogramming Algorithm In the Black-Scholes framework, we can perfectly replicate an option s payoff. However, it may not be true beyond the Black-Scholes
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 informationValuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility model
Valuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility model 1(23) Valuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility
More informationA No-Arbitrage Theorem for Uncertain Stock Model
Fuzzy Optim Decis Making manuscript No (will be inserted by the editor) A No-Arbitrage Theorem for Uncertain Stock Model Kai Yao Received: date / Accepted: date Abstract Stock model is used to describe
More informationFixed Income and Risk Management
Fixed Income and Risk Management Fall 2003, Term 2 Michael W. Brandt, 2003 All rights reserved without exception Agenda and key issues Pricing with binomial trees Replication Risk-neutral pricing Interest
More information