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Numerical Solution of Jump-Diffusion LIBOR Market Models
Finance Stochast. 7, 1–27 (2003) c Springer-Verlag 2003 Numerical solution of jump-diffusion LIBOR market models Paul Glasserman1, Nicolas Merener2 1 403 Uris Hall, Graduate School of Business, Columbia University, New York, NY 10027, USA (e-mail: [email protected]) 2 Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, USA (e-mail: [email protected]) Abstract. This paper develops, analyzes, and tests computational procedures for the numerical solution of LIBOR market models with jumps. We consider, in par- ticular, a class of models in which jumps are driven by marked point processes with intensities that depend on the LIBOR rates themselves. While this formulation of- fers some attractive modeling features, it presents a challenge for computational work. As a first step, we therefore show how to reformulate a term structure model driven by marked point processes with suitably bounded state-dependent intensities into one driven by a Poisson random measure. This facilitates the development of discretization schemes because the Poisson random measure can be simulated with- out discretization error. Jumps in LIBOR rates are then thinned from the Poisson random measure using state-dependent thinning probabilities. Because of discon- tinuities inherent to the thinning process, this procedure falls outside the scope of existing convergence results; we provide some theoretical support for our method through a result establishing first and second order convergence of schemes that accommodates thinning but imposes stronger conditions on other problem data. The bias and computational efficiency of various schemes are compared through numerical experiments. Key words: Interest rate models, Monte Carlo simulation, market models, marked point processes JEL Classification: G13, E43 Mathematics Subject Classification (1991): 60G55, 60J75, 65C05, 90A09 The authors thank Professor Steve Kou for helpful comments and discussions. -
CURRICULUM VITAE Anatoliy SWISHCHUK Department Of
CURRICULUM VITAE Anatoliy SWISHCHUK Department of Mathematics & Statistics, University of Calgary 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4 Office: MS552 E-mails: [email protected] Tel: +1 (403) 220-3274 (office) home page: http://www.math.ucalgary.ca/~aswish/ Education: • Doctor of Phys. & Math. Sci. (1992, Doctorate, Institute of Mathematics, National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine) • Ph.D. (1984, Institute of Mathematics, NASU, Kiev, Ukraine) • M.Sc., B.Sc. (1974-1979, Kiev State University, Faculty of Mathematics & Mechanics, Probability Theory & Mathematical Statistics Department, Kiev, Ukraine) Work Experience: • Full Professor, Department of Mathematics and Statistics, University of Calgary, Calgary, Canada (April 2012-present) • Co-Director, Mathematical and Computational Finance Laboratory, Department of Math- ematics and Statistics, University of Calgary, Calgary, Canada (October 2004-present) • Associate Professor, Department of Mathematics and Statistics, University of Calgary, Calgary, Canada (July 2006-March 2012) • Assistant Professor, Department of Mathematics and Statistics, University of Calgary, Calgary, Canada (August 2004-June 2006) • Course Director, Department of Mathematics & Statistics, York University, Toronto, ON, Canada (January 2003-June 2004) • Research Associate, Laboratory for Industrial & Applied Mathematics, Department of Mathematics & Statistics, York University, Toronto, ON, Canada (November 1, 2001-July 2004) • Professor, Probability Theory & Mathematical Statistics -
Introduction to Black's Model for Interest Rate
INTRODUCTION TO BLACK'S MODEL FOR INTEREST RATE DERIVATIVES GRAEME WEST AND LYDIA WEST, FINANCIAL MODELLING AGENCY© Contents 1. Introduction 2 2. European Bond Options2 2.1. Different volatility measures3 3. Caplets and Floorlets3 4. Caps and Floors4 4.1. A call/put on rates is a put/call on a bond4 4.2. Greeks 5 5. Stripping Black caps into caplets7 6. Swaptions 10 6.1. Valuation 11 6.2. Greeks 12 7. Why Black is useless for exotics 13 8. Exercises 13 Date: July 11, 2011. 1 2 GRAEME WEST AND LYDIA WEST, FINANCIAL MODELLING AGENCY© Bibliography 15 1. Introduction We consider the Black Model for futures/forwards which is the market standard for quoting prices (via implied volatilities). Black[1976] considered the problem of writing options on commodity futures and this was the first \natural" extension of the Black-Scholes model. This model also is used to price options on interest rates and interest rate sensitive instruments such as bonds. Since the Black-Scholes analysis assumes constant (or deterministic) interest rates, and so forward interest rates are realised, it is difficult initially to see how this model applies to interest rate dependent derivatives. However, if f is a forward interest rate, it can be shown that it is consistent to assume that • The discounting process can be taken to be the existing yield curve. • The forward rates are stochastic and log-normally distributed. The forward rates will be log-normally distributed in what is called the T -forward measure, where T is the pay date of the option. -
A University of Sussex Phd Thesis Available Online Via Sussex
A University of Sussex PhD thesis Available online via Sussex Research Online: http://sro.sussex.ac.uk/ This thesis is protected by copyright which belongs to the author. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the Author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the Author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Please visit Sussex Research Online for more information and further details NON-STATIONARY PROCESSES AND THEIR APPLICATION TO FINANCIAL HIGH-FREQUENCY DATA Mailan Trinh A thesis submitted for the degree of Doctor of Philosophy University of Sussex March 2018 UNIVERSITY OF SUSSEX MAILAN TRINH A THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY NON-STATIONARY PROCESSES AND THEIR APPLICATION TO FINANCIAL HIGH-FREQUENCY DATA SUMMARY The thesis is devoted to non-stationary point process models as generalizations of the standard homogeneous Poisson process. The work can be divided in two parts. In the first part, we introduce a fractional non-homogeneous Poisson process (FNPP) by applying a random time change to the standard Poisson process. We character- ize the FNPP by deriving its non-local governing equation. We further compute moments and covariance of the process and discuss the distribution of the arrival times. Moreover, we give both finite-dimensional and functional limit theorems for the FNPP and the corresponding fractional non-homogeneous compound Poisson process. The limit theorems are derived by using martingale methods, regular vari- ation properties and Anscombe's theorem. -
STOCHASTIC COMPARISONS and AGING PROPERTIES of an EXTENDED GAMMA PROCESS Zeina Al Masry, Sophie Mercier, Ghislain Verdier
STOCHASTIC COMPARISONS AND AGING PROPERTIES OF AN EXTENDED GAMMA PROCESS Zeina Al Masry, Sophie Mercier, Ghislain Verdier To cite this version: Zeina Al Masry, Sophie Mercier, Ghislain Verdier. STOCHASTIC COMPARISONS AND AGING PROPERTIES OF AN EXTENDED GAMMA PROCESS. Journal of Applied Probability, Cambridge University press, 2021, 58 (1), pp.140-163. 10.1017/jpr.2020.74. hal-02894591 HAL Id: hal-02894591 https://hal.archives-ouvertes.fr/hal-02894591 Submitted on 9 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Applied Probability Trust (4 July 2020) STOCHASTIC COMPARISONS AND AGING PROPERTIES OF AN EXTENDED GAMMA PROCESS ZEINA AL MASRY,∗ FEMTO-ST, Univ. Bourgogne Franche-Comt´e,CNRS, ENSMM SOPHIE MERCIER & GHISLAIN VERDIER,∗∗ Universite de Pau et des Pays de l'Adour, E2S UPPA, CNRS, LMAP, Pau, France Abstract Extended gamma processes have been seen to be a flexible extension of standard gamma processes in the recent reliability literature, for cumulative deterioration modeling purpose. The probabilistic properties of the standard gamma process have been well explored since the 1970's, whereas those of its extension remain largely unexplored. In particular, stochastic comparisons between degradation levels modeled by standard gamma processes and aging properties for the corresponding level-crossing times are nowadays well understood. -
Lecture Notes
Lecture Notes Lars Peter Hansen October 8, 2007 2 Contents 1 Approximation Results 5 1.1 One way to Build a Stochastic Process . 5 1.2 Stationary Stochastic Process . 6 1.3 Invariant Events and the Law of Large Numbers . 6 1.4 Another Way to Build a Stochastic Process . 8 1.4.1 Stationarity . 8 1.4.2 Limiting behavior . 9 1.4.3 Ergodicity . 10 1.5 Building Nonstationary Processes . 10 1.6 Martingale Approximation . 11 3 4 CONTENTS Chapter 1 Approximation Results 1.1 One way to Build a Stochastic Process • Consider a probability space (Ω, F, P r) where Ω is a set of sample points, F is an event collection (sigma algebra) and P r assigns proba- bilities to events . • Introduce a function S :Ω → Ω such that for any event Λ, S−1(Λ) = {ω ∈ Ω: S(ω) ∈ Λ} is an event. • Introduce a (Borel measurable) measurement function x :Ω → Rn. x is a random vector. • Construct a stochastic process {Xt : t = 1, 2, ...} via the formula: t Xt(ω) = X[S (ω)] or t Xt = X ◦ S . Example 1.1.1. Let Ω be a collection of infinite sequences of real numbers. Specifically, ω = (r0, r1, ...), S(ω) = (r1, r2, ...) and x(ω) = r0. Then Xt(ω) = rt. 5 6 CHAPTER 1. APPROXIMATION RESULTS 1.2 Stationary Stochastic Process Definition 1.2.1. The transformation S is measure-preserving if: P r(Λ) = P r{S−1(Λ)} for all Λ ∈ F. Proposition 1.2.2. When S is measure-preserving, the process {Xt : t = 1, 2, ...} has identical distributions for every t. -
Arbitrage Free Approximations to Candidate Volatility Surface Quotations
Journal of Risk and Financial Management Article Arbitrage Free Approximations to Candidate Volatility Surface Quotations Dilip B. Madan 1,* and Wim Schoutens 2,* 1 Robert H. Smith School of Business, University of Maryland, College Park, MD 20742, USA 2 Department of Mathematics, KU Leuven, 3000 Leuven, Belgium * Correspondence: [email protected] (D.B.M.); [email protected] (W.S.) Received: 19 February 2019; Accepted: 10 April 2019; Published: 21 April 2019 Abstract: It is argued that the growth in the breadth of option strikes traded after the financial crisis of 2008 poses difficulties for the use of Fourier inversion methodologies in volatility surface calibration. Continuous time Markov chain approximations are proposed as an alternative. They are shown to be adequate, competitive, and stable though slow for the moment. Further research can be devoted to speed enhancements. The Markov chain approximation is general and not constrained to processes with independent increments. Calibrations are illustrated for data on 2695 options across 28 maturities for SPY as at 8 February 2018. Keywords: bilateral gamma; fast Fourier transform; sato process; matrix exponentials JEL Classification: G10; G12; G13 1. Introduction A variety of arbitrage free models for approximating quoted option prices have been available for some time. The quotations are typically in terms of (Black and Scholes 1973; Merton 1973) implied volatilities for the traded strikes and maturities. Consequently, the set of such quotations is now popularly referred to as the volatility surface. Some of the models are nonparametric with the Dupire(1994) local volatility model being a popular example. The local volatility model is specified in terms of a deterministic function s(K, T) that expresses the local volatility for the stock if the stock were to be at level K at time T. -
Portfolio Optimization and Option Pricing Under Defaultable Lévy
Universit`adegli Studi di Padova Dipartimento di Matematica Scuola di Dottorato in Scienze Matematiche Indirizzo Matematica Computazionale XXVI◦ ciclo Portfolio optimization and option pricing under defaultable L´evy driven models Direttore della scuola: Ch.mo Prof. Paolo Dai Pra Coordinatore dell’indirizzo: Ch.ma Prof.ssa. Michela Redivo Zaglia Supervisore: Ch.mo Prof. Tiziano Vargiolu Universit`adegli Studi di Padova Corelatori: Ch.mo Prof. Agostino Capponi Ch.mo Prof. Andrea Pascucci Johns Hopkins University Universit`adegli Studi di Bologna Dottorando: Stefano Pagliarani To Carolina and to my family “An old man, who had spent his life looking for a winning formula (martingale), spent the last days of his life putting it into practice, and his last pennies to see it fail. The martingale is as elusive as the soul.” Alexandre Dumas Mille et un fantˆomes, 1849 Acknowledgements First and foremost, I would like to thank my supervisor Prof. Tiziano Vargiolu and my co-advisors Prof. Agostino Capponi and Prof. Andrea Pascucci, for coauthoring with me all the material appearing in this thesis. In particular, thank you to Tiziano Vargiolu for supporting me scientifically and finan- cially throughout my research and all the activities related to it. Thank you to Agostino Capponi for inviting me twice to work with him at Purdue University, and for his hospital- ity and kindness during my double stay in West Lafayette. Thank you to Andrea Pascucci for wisely leading me throughout my research during the last four years, and for giving me the chance to collaborate with him side by side, thus sharing with me his knowledge and his experience. -
A Representation Free Quantum Stochastic Calculus
JOURNAL OF FUNCTIONAL ANALYSIS 104, 149-197 (1992) A Representation Free Quantum Stochastic Calculus L. ACCARDI Centro Matemarico V. Volterra, Diparfimento di Matematica, Universitci di Roma II, Rome, Italy F. FACNOLA Dipartimento di Matematica, Vniversild di Trento, Povo, Italy AND J. QUAEGEBEUR Department of Mathemarics, Universiteit Leuven, Louvain, Belgium Communicated by L. Gross Received January 2; revised March 1, 1991 We develop a representation free stochastic calculus based on three inequalities (semimartingale inequality, scalar forward derivative inequality, scalar conditional variance inequality). We prove that our scheme includes all the previously developed stochastic calculi and some new examples. The abstract theory is applied to prove a Boson Levy martingale representation theorem in bounded form and a general existence, uniqueness, and unitarity theorem for quantum stochastic differential equations. 0 1992 Academic Press. Inc. 0. INTRODUCTION Stochastic calculus is a powerful tool in classical probability theory. Recently various kinds of stochastic calculi have been introduced in quan- tum probability [18, 12, 10,211. The common features of these calculi are: - One starts from a representation of the canonical commutation (or anticommutation) relations (CCR or CAR) over a space of the form L2(R+, dr; X) (where 3? is a given Hilbert space). - One introduces a family of operator valued measures on R,, related to this representation, e.g., expressed in terms of creation or annihilation or number or field operators. 149 0022-1236192 $3.00 Copyright 0 1992 by Academic Press, Inc. All rights of reproduction in any form reserved. 150 QUANTUM STOCHASTIC CALCULUS -- One shows that it is possible to develop a theory of stochastic integration with respect to these operator valued measures sufficiently rich to allow one to solve some nontrivial stochastic differential equations. -
Pricing Bermudan Swaptions on the LIBOR Market Model Using the Stochastic Grid Bundling Method
Pricing Bermudan Swaptions on the LIBOR Market Model using the Stochastic Grid Bundling Method Stef Maree,∗ Jacques du Toity Abstract We examine using the Stochastic Grid Bundling Method (SGBM) to price a Bermu- dan swaption driven by a one-factor LIBOR Market Model (LMM). Using a well- known approximation formula from the finance literature, we implement SGBM with one basis function and show that it is around six times faster than the equivalent Longstaff–Schwartz method. The two methods agree in price to one basis point, and the SGBM path estimator gives better (higher) prices than the Longstaff–Schwartz prices. A closer examination shows that inaccuracies in the approximation formula introduce a small bias into the SGBM direct estimator. 1 Introduction The LIBOR Market Model (LMM) is an interest rate market model. It owes much of its popularity to the fact that it is consistent with Black’s pricing formulas for simple interest rate derivatives such as caps and swaptions, see, for example, [3]. Under LMM all forward rates are modelled as individual processes, which typically results in a high dimensional model. Consequently when pricing more complex instruments under LMM, Monte Carlo simulation is usually used. When pricing products with early-exercise features, such as Bermudan swaptions, a method is required to determine the early-exercise policy. The most popular approach to this is the Longstaff–Schwartz Method (LSM) [8]. When time is discrete (as is usually the case in numerical schemes) Bellman’s backward induction principle says that the price of the option at any exercise time is the maximum of the spot payoff and the so-called contin- uation value. -
Approximations to the Lévy LIBOR Model By
Approximations to the Lévy LIBOR Model by Hassana Al-Hassan ([email protected]) Supervised by: Dr. Sure Mataramvura Co-Supervised by: Emeritus Professor Ronnie Becker Department of Mathematics and Applied Mathematics Faculty of Science University of Cape Town, South Africa A thesis submitted for the degree of Master of Science University of Cape Town May 27, 2014 The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or non- commercial research purposes only. Published by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University of Cape Town Plagiarism Declaration “I, Hassana AL-Hassan, know the meaning of plagiarism and declare that all of the work in the thesis, save for that which is properly acknowledged, is my own”. SIGNED Hassana Al-Hassan, May 27, 2014 i Declaration of Authorship I, Hassana AL-Hassan, declare that this thesis titled, “The LIBOR Market Model Versus the Levy LIBOR Market Model” and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at this University. Where any part of this thesis has previously been submitted for a degree or any other quali- fication at this University or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly attributed. -
Normalized Random Measures Driven by Increasing Additive Processes
The Annals of Statistics 2004, Vol. 32, No. 6, 2343–2360 DOI: 10.1214/009053604000000625 c Institute of Mathematical Statistics, 2004 NORMALIZED RANDOM MEASURES DRIVEN BY INCREASING ADDITIVE PROCESSES By Luis E. Nieto-Barajas1, Igor Prunster¨ 2 and Stephen G. Walker3 ITAM-M´exico, Universit`adegli Studi di Pavia and University of Kent This paper introduces and studies a new class of nonparamet- ric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increas- ing additive processes. In particular, we present results for the dis- tribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process. 1. Introduction. This paper considers the problem of constructing a stochastic process, defined on the real line, which has sample paths be- having almost surely (a.s.) as a probability distribution function (d.f.). The law governing the process acts as a prior in Bayesian nonparametric prob- lems. One popular idea is to take the random probability d.f. as a normal- ized increasing process F (t)= Z(t)/Z¯, where Z¯ = limt→∞ Z(t) < +∞ (a.s.). For example, the Dirichlet process [Ferguson (1973)] arises when Z is a suitably reparameterized gamma process. We consider the case of normal- ized random d.f.s driven by an increasing additive process (IAP) L, that is, Z(t)= k(t,x) dL(x) and provide regularity conditions on k and L to ensure F is a random probability d.f.