Finite dimensional realizations of HJM models

Size: px
Start display at page:

Download "Finite dimensional realizations of HJM models"

Transcription

1 Finite dimensional realizations of HJM models Tomas Björk Stockholm School of Economics Camilla Landén KTH, Stockholm Lars Svensson KTH, Stockholm UTS, December 2008, 1

2 Definitions: p t (x) : Price, at t of zero coupon bond maturing at t + x, r t (x) : Forward rate, contracted at t, maturing at t + x R t : Short rate. r t (x) = log p t(x) x p t (x) = e x 0 r t(s)ds R t = r t (0). 2

3 Heath-Jarrow-Morton-Musiela Idea: Model the dynamics for the entire forward rate curve. The yield curve itself (rather than the short rate R) is the explanatory variable. Model forward rates. Use observed forward rate curve as initial condition. Q-dynamics: dr t (x) = α t (x)dt + σ t (x)dw t, r 0 (x) = r0 (x), x W : d-dimensional Wiener process One SDE for every fixed x. 3

4 Theorem: (HJMM drift Condition) The following relations must hold, under a martingale measure Q. α t (x) = x x r t(x) + σ t (x) σ t(s)ds. 0 Moral: Volatility can be specified freely. The forward rate drift term is then uniquely determined. 4

5 The Interest Rate Model r t = r t ( ), σ t (x) = σ(r t, x) Heath-Jarrow-Morton-Musiela equation: dr t = µ 0 (r t )dt + σ(r t )dw t µ 0 (r t, x) = x x r t(x) + σ(r t, x) σ(r t, s)ds 0 The HJMM equation is an infinite dimensional SDE evolving in the space H of forward rate curves. 5

6 Sometimes you are lucky! Example: σ(r, x) = σe ax In this case the HJMM equation has a finite dimensional state space realization. We have in fact: r t (x) = B(t, x)z t A(t, x) where Z solves the one-dimensional SDE dz t = {Φ(t) az t } dt + σdw t Furthermore the state process Z can be identified with the short rate R = r(0). (A, B and Φ are deterministic functions) 6

7 A Hilbert Space Definition: For each (α, β) R 2, the space H α,β is defined by where where H α,β = {f C [0, ); f < } f 2 = β n n=0 0 [ f (n) (x) ] 2 e αx dx f (n) (x) = dn f dt n (x). We equip H with the inner product (f, g) = β n n=0 0 f (n) (x)g (n) (x)e αx dx 7

8 Properties of H Proposition: The following hold. The linear operator is bounded on H F = x H is complete, i.e. it is a Hilbert space. The elements in H are real analytic functions on R (not only on R + ). NB: Filipovic and Teichmann! 8

9 Stratonovich Integrals Definition The Stratonovich integral t 0 X s dy s is defined as t 0 X s dy s = t 0 X sdy s X, Y t X, Y t = t 0 dx sdy s, Proposition: For any smooth F we have df (t, Y t ) = F t dt + F y dy t 9

10 Stratonovich Form of HJMM dr t = µ(r t )dt + σ(r t ) dw t where µ(r t ) = µ 0 (r t ) 1 2 d σ, W dt Main Point: Using the Stratonovich differential we have no Itô second order term. Thus we can treat the SDE above as the ODE dr t dt = µ(r t) + σ(r t ) v t where v t = white noise. 10

11 Natural Questions What do the forward rate curves look like? What is the support set of the HJMM equation? When is a given model (e.g. Hull-White) consistent with a given family (e.g. Nelson-Siegel) of forward rate curves? When is the short rate Markov? When is a finite set of benchmark forward rates Markov? When does the interest rate model admit a realization in terms of a finite dimensional factor model? If there exists an FDR how can you construct a concrete realization? 11

12 Finite Dimensional Realizations Main Problem: When does a given interest rate model possess a finite dimensional realisation, i.e. when can we write r as z t = η(z t )dt + δ(z t ) dw (t), r t (x) = G(z t, x), where z is a finite-dimensional diffusion, and or alternatively G : R d R + R G : R d H H = the space of forward rate curves 12

13 Examples: σ(r, x) = e ax, σ(r, x) = xe ax, σ(r, x) = e x2, σ(r, x) = log σ(r, x) = ( x 2 ) 0 e s r(s)ds x 2 e ax., Which of these admit a finite dimensional realisation? 13

14 Earlier literature Cheyette (1996) Bhar & Chiarella (1997) Chiarella & Kwon (1998) Inui & Kijima (1998) Ritchken & Sankarasubramanian (1995) Carverhill (1994) Eberlein & Raible (1999) Jeffrey (1995) All these papers present sufficient conditions for existence of an FDR. 14

15 Present paper We would like to obtain: Necessary and sufficient conditions. struc- A better understanding of the deep ture of the FDR problem. A general theory of FDR for arbitrary infinite dimensional SDEs. We attack the general problem by viewing it as a geometrical problem. 15

16 Invariant Manifolds Def: Consider an interest rate model dr t = µ(r t )dt + σ(r t ) dw t on the space H of forward rate curves. A manifold (surface) G H is an invariant manifold if P -a.s. for all t > 0 r 0 G r t G 16

17 Main Insight There exists a finite dimensional realization. iff There exists a finite dimensional invariant manifold. 17

18 Characterizing Invariant Manifolds Proposition: (Björk-Christensen) Consider an interest rate model on Stratonovich form dr t = µ(r t )dt + σ(r t ) dw t A manifold G is invariant under r if and only if µ(r) T G (r), σ(r) T G (r), at all points of G. Here T G (r) is the tangent space of G at the point r G. 18

19 Main Problem Given: An interest rate model on Stratonovich form dr t = µ(r t )dt + σ(r t ) dw t An inital forward rate curve r 0 : x r 0 (x) 19

20 Question: When does there exist a finite dimensional manifold G, such that and r 0 G µ(r) T G (r), σ(r) T G (r), A manifold satisfying these conditions is called a tangential manifold. 20

21 Abstract Problem On the Hilbert space H, we are given two vector fields f 1 (r) and f 2 (r). We are also given a point r 0 H. Problem: When does there exist a finite dimensional manifold G H such that We have the inclusion r 0 G For all points r G we have the relations f 1 (r) T G (r), f 2 (r) T G (r) We call such a G an tangential manifold. 21

22 Easier Problem On the space H, we are given one vector field f 1 (r). We are also given a point r 0 H. Problem: When does there exist a finite dimensional manifold G H such that We have the inclusion r 0 G We have the relation f 1 (r) T G (r) Answer to Easy Problem: ALWAYS! 22

23 Proof: Solve the ODE dr t dt = f 1(r t ) with initial point r 0. Denote the solution at time t by e f 1t r 0 Then the integral curve { e f 1t r 0 ; t R } solves the problem, i.e. G = { e f 1t r 0 ; t R } 23

24 Furthermore, the mapping where G : R G G(t) = e f 1t r 0 parametrizes G. We have G = Im[G] Thus we even have a one dimensional coordinate system for G, given by ϕ : G R ϕ = G 1 24

25 Back to original problem: We are given two vector fields f 1 (r) and f 2 (r) and a point r 0 H. Naive Conjecture: There exists a two-dimensional tangential manifold, which is parametrized by the mapping where G : R 2 X G(s, t) = e f 2s e f 1t r 0 Generally False! Argument: If there exists a 2-dimensional manifold, then it should also be parametrized by H(s, t) = e f 1s e f 2t r 0 Moral: We need some commutativity. 25

26 Lie Brackets Given two vector fields f 1 (r) and f 2 (r), their Lie bracket [f 1, f 2 ] is a vector field defined by [f 1, f 2 ] = (Df 2 )f 1 (Df 1 )f 2 where D is the Frechet derivative (Jacobian). Fact: e f 1h e f 2h r 0 e f 2h e f 1h r 0 [f 1, f 2 ]h 2 Fact: If G is tangential to f 1 and f 2, then it is also tangential to [f 1, f 2 ]. 26

27 Definition: Given vector fields f 1 (r),..., f n (r), the Lie algebra {f 1 (r),..., f n (r)} LA is the smallest linear space of vector fields, containing f 1 (r),..., f n (r), which is closed under the Lie bracket. Conjecture: f 1 (r),..., f n (r) generates a finite dimensional tangential manifold iff dim {f 1 (r),..., f n (r)} LA < 27

28 Frobenius Theorem: Given n independent vector fields f 1,..., f n. There will exist an n-dimensional tangential manifold iff span {f 1,..., f n } is closed under the Lie-bracket. Corollary: Given n vector fields f 1,..., f n. Then there exists exists a finite dimensional tangential manifold iff the Lie-algebra {f 1,..., f n } LA generated by f 1,..., f n has finite dimension at each point. The dimension of the manifold equals the dimension of the Lie-algebra. 28

29 Proposition: Suppose that the vector fields f 1,..., f n are independent and closed under the Lie bracket. Fix a point r 0 X. Then the tangential manifold is parametrized by where G : R n G G(t 1,..., t n ) = e f nt n... e f 2t 2 e f 1t 1 r 0 29

30 Main result Given any fixed initial forward rate curve r 0, there exists a finite dimensional invariant manifold G with r 0 G if and only if the Lie-algebra is finite dimensional. L = {µ, σ} LA Given any fixed initial forward rate curve r 0, there exists a finite dimensional realization if and only if the Lie-algebra L = {µ, σ} LA is finite dimensional. The dimension of the realization equals dim {µ, σ} LA. 30

31 Deterministic Volatility σ(r, x) = σ(x) Consider a deterministic volatility function σ(x). Then the Ito and Stratonovich formulations are the same: where dr = {Fr + S} dt + σdw F = x x, S(x) = σ(x) σ(s)ds. 0 The Lie algebra L is generated by the two vector fields µ(r) = Fr + S, σ(r) = σ 31

32 Proposition: There exists an FDR iff σ is quasi exponential, i.e. of the form σ(x) = n i=1 where p i is a polynomial. p i (x)e α ix 32

33 Constant Direction Volatility σ(r, x) = ϕ(r)λ(x) Theorem Assume that ϕ (r)(λ, λ) 0. Then the model admits a finite dimensional realization if and only if λ is quasi-exponential. The scalar field ϕ(r) can be arbitrary. Note: The degenerate case ϕ(r) (λ, λ) 0 corresponds to CIR. 33

34 Short Rate Realizations Question: When is a given forward rate model realized by a short rate model? r(t, x) = G(t, R t, x) dr t = a(t, R t )dt + b(t, R t ) dw Answer: There must exist a 2-dimensional realization. (With the short rate R and running time t as states). Proposition: The model is a short rate model only if dim {µ, σ} LA 2 Theorem: The model is a generic short rate model if and only if [µ, σ] //σ 34

35 All short rate models are affine Theorem: (Jeffrey) Assume that the forward rate volatitliy is of the form σ(r t, x) Then the model is a generic short rate model if and only if σ is of the form σ(r, x) = c (Ho-Lee) σ(r, x) = ce ax (Hull-White) σ(r, x) = λ(x) ar + b (CIR) (λ solves a certain Ricatti equation) Slogan: Ho-Lee, Hull-White and CIR are the only generic short rate models. 35

36 Constructing an FDR Problem: Suppose that there actually exists an FDR, i.e. that dim {µ, σ} LA <. How do you construct a realization? Good news: There exists a general and easy theory for this, including a concrete algorithm. See Björk & Landen (2001). 36

37 Example: Deterministic Direction Volatility Model: σ i (r, x) = ϕ(r)λ(x). Minimal Realization: dz 0 = dt, dz 1 0 = [c 0Z 1 n + γϕ 2 (G(Z))]dt + ϕ(g(z))dw t, dzi 1 = (c i Zn 1 + Zi 1 1 )dt, i = 1,..., n, dz 2 0 = [d 0Z 2 q + ϕ 2 (G(Z))]dt, dzj 2 = (d j Zq 2 + Zj 1 2 )dt, j = 1,..., q. 37

38 Stochastic Volatility Forward rate equation: dr t = µ 0 (r t, y t )dt + σ(r t, y t )dw t, dy t = a(y t )dt + b(y t ) dv t Here W and V are independent Wiener and y is a finite dimensional diffusion living on R k. µ 0 = x r t(x) + σ(r t, y t, x) x 0 σ(r t, y t, s)ds Problem: When does there exist an FDR? Good news: This can be solved completely using the Lie algebra approach. See Björk- Landen-Svensson (2002). 38

39 Point Process Extensions Including a driving point process leads to hard problems. More precisely The equivalence between existence of an FDR and existence of an invariant manifold still holds. The characterization of an invariant manifold as a tangential manifold is no longer true. This is because a point process act globally whereas a Wiener process act locally, thereby allowing differential calculus. Including a driving point process requires, for a general theory, completely different arguments. The picture is very unclear. 39

40 Point Processes: Special Cases Chiarella & Nikitopoulos Sklibosios (2003) Sufficient Conditions Tappe (2007) Necessary Conditions using Lie algebra techinques. Elhouar (2008) Wiener driven models with point process driven volatilities using Lie algebra techinques. 40

41 Björk, T. & Christensen, B.J. (1999) Interest rate dynamics and consistent forward rate curves. Mathematical Finance, 9, No. 4, Björk, T. & Gombani A. (1997) Minimal realization of interest rate models. Finance and Stochastics, 3, No. 4, Björk, T. & Svensson, L. (2001) On the existence of finite dimensional nonlinear realizations for nonlinear forward rate rate models. Mathematical Finance, 11, Björk, T. (2001) A geometric view of interest rate theory. In Option Pricing, Interst Rates and Risk Mangement. Cambridge University Press. Björk, T. & Landen C. (2001) On the construction of finite dimensional nonlinear realizations for nonlinear forward rate models. Finance and Stochastics. Björk, T. & Landen C. & Svenssom, L. (2002) On finite Markovian realizations for stochastic volatility forward rate models. Proc. Royal Soc. Filipovic, D. & Teichmann, J. (2001) Finite dimensional realizations for stochastic equations in the HJM framework. Journal of Functional Analysis. 41

42 Earlier literature Cheyette, O. (1996) Markov representation of the Heath- Jarrow-Morton model. Working paper. BARRA Inc, Berkeley. Bhar, R. & Chiarella, C. (1997) Transformation of Heath-Jarrow-Morton models to markovian systems. European Journal of Finance, 3, No. 1, Chiarella, C & Kwon, K. (1998) Forward rate dependent Markovian transformations of the Heath-Jarrow- Morton term structure model. Finance and Stochastics, 5, Inui, K. & Kijima, M. (1998) A markovian framework in multi-factor Heath-Jarrow-Morton models. JFQA 333 no. 3, Ritchken, P. & Sankarasubramanian, L. (1995) Volatility structures of forward rates and the dynamics of the term structure. Mathematical Finance, 5, no. 1, Carverhill, A. (1994) When is the spot rate Markovian? Mathematical Finance,, Eberlein, E. & Raible, S. (1999) Term structure models driven by general Levy processes. Mathematical Finance, 9, No 1, Jeffrey, A. (1995) Single factor Heath-Jarrow-Morton term structure models based on Markovian spot interest rates. JFQA 30 no.4,

Affine term structures for interest rate models

Affine term structures for interest rate models Stefan Tappe Albert Ludwig University of Freiburg, Germany UNSW-Macquarie WORKSHOP Risk: modelling, optimization and inference Sydney, December 7th, 2017 Introduction Affine processes in finance: R = a

More information

Basic Arbitrage Theory KTH Tomas Björk

Basic 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 information

An Introduction to Point Processes. from a. Martingale Point of View

An Introduction to Point Processes. from a. Martingale Point of View An Introduction to Point Processes from a Martingale Point of View Tomas Björk KTH, 211 Preliminary, incomplete, and probably with lots of typos 2 Contents I The Mathematics of Counting Processes 5 1 Counting

More information

Continuous Time Finance. Tomas Björk

Continuous 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 information

Interest rate modelling: How important is arbitrage free evolution?

Interest rate modelling: How important is arbitrage free evolution? Interest rate modelling: How important is arbitrage free evolution? Siobhán Devin 1 Bernard Hanzon 2 Thomas Ribarits 3 1 European Central Bank 2 University College Cork, Ireland 3 European Investment Bank

More information

European call option with inflation-linked strike

European 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 information

Lecture 5: Review of interest rate models

Lecture 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 information

Interest rate models in continuous time

Interest 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 information

Multi-dimensional Term Structure Models

Multi-dimensional Term Structure Models Multi-dimensional Term Structure Models We will focus on the affine class. But first some motivation. A generic one-dimensional model for zero-coupon yields, y(t; τ), looks like this dy(t; τ) =... dt +

More information

A new approach for scenario generation in risk management

A new approach for scenario generation in risk management A new approach for scenario generation in risk management Josef Teichmann TU Wien Vienna, March 2009 Scenario generators Scenarios of risk factors are needed for the daily risk analysis (1D and 10D ahead)

More information

AMH4 - ADVANCED OPTION PRICING. Contents

AMH4 - ADVANCED OPTION PRICING. Contents AMH4 - ADVANCED OPTION PRICING ANDREW TULLOCH Contents 1. Theory of Option Pricing 2 2. Black-Scholes PDE Method 4 3. Martingale method 4 4. Monte Carlo methods 5 4.1. Method of antithetic variances 5

More information

Arbeitsgruppe Stochastik. PhD Seminar: HJM Forwards Price Models for Commodities. M.Sc. Brice Hakwa

Arbeitsgruppe Stochastik. PhD Seminar: HJM Forwards Price Models for Commodities. M.Sc. Brice Hakwa Arbeitsgruppe Stochastik. Leiterin: Univ. Prof. Dr. Barbara Rdiger-Mastandrea. PhD Seminar: HJM Forwards Price Models for Commodities M.Sc. Brice Hakwa 1 Bergische Universität Wuppertal, Fachbereich Angewandte

More information

Path Dependent British Options

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

More information

Term Structure Models with Parallel and Proportional Shifts

Term Structure Models with Parallel and Proportional Shifts Term Structure Models with Parallel and Proportional Shifts Fredrik Armerin Bjarne Astrup Jensen, Department of Mathematics Department of Finance Royal Institute of Technology Copenhagen Business School

More information

Consistent Calibration of HJM Models to Cap Implied Volatilities

Consistent Calibration of HJM Models to Cap Implied Volatilities Consistent Calibration of HJM Models to Cap Implied Volatilities Flavio Angelini Stefano Herzel University of Perugia Abstract This paper proposes a calibration algorithm that fits multi-factor Gaussian

More information

One-Factor Models { 1 Key features of one-factor (equilibrium) models: { All bond prices are a function of a single state variable, the short rate. {

One-Factor Models { 1 Key features of one-factor (equilibrium) models: { All bond prices are a function of a single state variable, the short rate. { Fixed Income Analysis Term-Structure Models in Continuous Time Multi-factor equilibrium models (general theory) The Brennan and Schwartz model Exponential-ane models Jesper Lund April 14, 1998 1 Outline

More information

Lecture 4. Finite difference and finite element methods

Lecture 4. Finite difference and finite element methods Finite difference and finite element methods Lecture 4 Outline Black-Scholes equation From expectation to PDE Goal: compute the value of European option with payoff g which is the conditional expectation

More information

Practical example of an Economic Scenario Generator

Practical example of an Economic Scenario Generator Practical example of an Economic Scenario Generator Martin Schenk Actuarial & Insurance Solutions SAV 7 March 2014 Agenda Introduction Deterministic vs. stochastic approach Mathematical model Application

More information

Greek parameters of nonlinear Black-Scholes equation

Greek 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 information

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin SPDE and portfolio choice (joint work with M. Musiela) Princeton University November 2007 Thaleia Zariphopoulou The University of Texas at Austin 1 Performance measurement of investment strategies 2 Market

More information

Enlargement of filtration

Enlargement 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 information

Randomness and Fractals

Randomness and Fractals Randomness and Fractals Why do so many physicists become traders? Gregory F. Lawler Department of Mathematics Department of Statistics University of Chicago September 25, 2011 1 / 24 Mathematics and the

More information

RISK-NEUTRAL VALUATION AND STATE SPACE FRAMEWORK. JEL Codes: C51, C61, C63, and G13

RISK-NEUTRAL VALUATION AND STATE SPACE FRAMEWORK. JEL Codes: C51, C61, C63, and G13 RISK-NEUTRAL VALUATION AND STATE SPACE FRAMEWORK JEL Codes: C51, C61, C63, and G13 Dr. Ramaprasad Bhar School of Banking and Finance The University of New South Wales Sydney 2052, AUSTRALIA Fax. +61 2

More information

θ(t ) = T f(0, T ) + σ2 T

θ(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 information

On the Ross recovery under the single-factor spot rate model

On the Ross recovery under the single-factor spot rate model .... On the Ross recovery under the single-factor spot rate model M. Kijima Tokyo Metropolitan University 11/08/2016 Kijima (TMU) Ross Recovery SMU @ August 11, 2016 1 / 35 Plan of My Talk..1 Introduction:

More information

Finance II. May 27, F (t, x)+αx f t x σ2 x 2 2 F F (T,x) = ln(x).

Finance II. May 27, F (t, x)+αx f t x σ2 x 2 2 F F (T,x) = ln(x). Finance II May 27, 25 1.-15. All notation should be clearly defined. Arguments should be complete and careful. 1. (a) Solve the boundary value problem F (t, x)+αx f t x + 1 2 σ2 x 2 2 F (t, x) x2 =, F

More information

VII. Incomplete Markets. Tomas Björk

VII. Incomplete Markets. Tomas Björk VII Incomplete Markets Tomas Björk 1 Typical Factor Model Setup Given: An underlying factor process X, which is not the price process of a traded asset, with P -dynamics dx t = µ (t, X t ) dt + σ (t, X

More information

Forward Rate Curve Smoothing

Forward Rate Curve Smoothing Forward Rate Curve Smoothing Robert A Jarrow June 4, 2014 Abstract This paper reviews the forward rate curve smoothing literature The key contribution of this review is to link the static curve fitting

More information

The British Russian Option

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

More information

25857 Interest Rate Modelling

25857 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 information

Optimizing Portfolios

Optimizing Portfolios Optimizing Portfolios An Undergraduate Introduction to Financial Mathematics J. Robert Buchanan 2010 Introduction Investors may wish to adjust the allocation of financial resources including a mixture

More information

Risk, Return, and Ross Recovery

Risk, Return, and Ross Recovery Risk, Return, and Ross Recovery Peter Carr and Jiming Yu Courant Institute, New York University September 13, 2012 Carr/Yu (NYU Courant) Risk, Return, and Ross Recovery September 13, 2012 1 / 30 P, Q,

More information

STOCHASTIC CALCULUS AND BLACK-SCHOLES MODEL

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

More information

The stochastic calculus

The 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 information

Constructing Markov models for barrier options

Constructing Markov models for barrier options Constructing Markov models for barrier options Gerard Brunick joint work with Steven Shreve Department of Mathematics University of Texas at Austin Nov. 14 th, 2009 3 rd Western Conference on Mathematical

More information

MSc Financial Engineering CHRISTMAS ASSIGNMENT: MERTON S JUMP-DIFFUSION MODEL. To be handed in by monday January 28, 2013

MSc 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 information

Fixed Income Modelling

Fixed Income Modelling Fixed Income Modelling CLAUS MUNK OXPORD UNIVERSITY PRESS Contents List of Figures List of Tables xiii xv 1 Introduction and Overview 1 1.1 What is fixed income analysis? 1 1.2 Basic bond market terminology

More information

Parameters Estimation in Stochastic Process Model

Parameters Estimation in Stochastic Process Model Parameters Estimation in Stochastic Process Model A Quasi-Likelihood Approach Ziliang Li University of Maryland, College Park GEE RIT, Spring 28 Outline 1 Model Review The Big Model in Mind: Signal + Noise

More information

Sensitivity Analysis on Long-term Cash flows

Sensitivity Analysis on Long-term Cash flows Sensitivity Analysis on Long-term Cash flows Hyungbin Park Worcester Polytechnic Institute 19 March 2016 Eastern Conference on Mathematical Finance Worcester Polytechnic Institute, Worceseter, MA 1 / 49

More information

Forward Dynamic Utility

Forward Dynamic Utility Forward Dynamic Utility El Karoui Nicole & M RAD Mohamed UnivParis VI / École Polytechnique,CMAP elkaroui@cmapx.polytechnique.fr with the financial support of the "Fondation du Risque" and the Fédération

More information

IMPA Commodities Course : Forward Price Models

IMPA Commodities Course : Forward Price Models IMPA Commodities Course : Forward Price Models Sebastian Jaimungal sebastian.jaimungal@utoronto.ca Department of Statistics and Mathematical Finance Program, University of Toronto, Toronto, Canada http://www.utstat.utoronto.ca/sjaimung

More information

Term Structure Models Workshop at AFIR-ERM Colloquium, Panama, 2017

Term Structure Models Workshop at AFIR-ERM Colloquium, Panama, 2017 Term Structure Models Workshop at AFIR-ERM Colloquium, Panama, 2017 Michael Sherris CEPAR and School of Risk and Actuarial Studies UNSW Business School UNSW Sydney m.sherris@unsw.edu.au UNSW August 2017

More information

Stochastic Modelling Unit 3: Brownian Motion and Diffusions

Stochastic Modelling Unit 3: Brownian Motion and Diffusions Stochastic Modelling Unit 3: Brownian Motion and Diffusions Russell Gerrard and Douglas Wright Cass Business School, City University, London June 2004 Contents of Unit 3 1 Introduction 2 Brownian Motion

More information

Discrete time interest rate models

Discrete time interest rate models slides for the course Interest rate theory, University of Ljubljana, 2012-13/I, part II József Gáll University of Debrecen, Faculty of Economics Nov. 2012 Jan. 2013, Ljubljana Introduction to discrete

More information

On Pricing Derivatives in the Presence of Auxiliary State Variables

On Pricing Derivatives in the Presence of Auxiliary State Variables On Pricing Derivatives in the Presence of Auxiliary State Variables J. Lin P. Ritchken September 28, 2001 Department of Operations, Weatherhead School of Management, Case Western Reserve University, 10900

More information

Hedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework

Hedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework Hedging of swaptions in a Lévy driven Heath-Jarrow-Morton framework Kathrin Glau, Nele Vandaele, Michèle Vanmaele Bachelier Finance Society World Congress 2010 June 22-26, 2010 Nele Vandaele Hedging of

More information

Optimal 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 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 information

1 Interest Based Instruments

1 Interest Based Instruments 1 Interest Based Instruments e.g., Bonds, forward rate agreements (FRA), and swaps. Note that the higher the credit risk, the higher the interest rate. Zero Rates: n year zero rate (or simply n-year zero)

More information

Lecture 8: Introduction to asset pricing

Lecture 8: Introduction to asset pricing THE UNIVERSITY OF SOUTHAMPTON Paul Klein Office: Murray Building, 3005 Email: p.klein@soton.ac.uk URL: http://paulklein.se Economics 3010 Topics in Macroeconomics 3 Autumn 2010 Lecture 8: Introduction

More information

The Mathematics of Currency Hedging

The Mathematics of Currency Hedging The Mathematics of Currency Hedging Benoit Bellone 1, 10 September 2010 Abstract In this note, a very simple model is designed in a Gaussian framework to study the properties of currency hedging Analytical

More information

Lecture 3: Review of mathematical finance and derivative pricing models

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

More information

1 Mathematics in a Pill 1.1 PROBABILITY SPACE AND RANDOM VARIABLES. A probability triple P consists of the following components:

1 Mathematics in a Pill 1.1 PROBABILITY SPACE AND RANDOM VARIABLES. A probability triple P consists of the following components: 1 Mathematics in a Pill The purpose of this chapter is to give a brief outline of the probability theory underlying the mathematics inside the book, and to introduce necessary notation and conventions

More information

On Pricing Derivatives in the Presence of Auxiliary State Variables

On Pricing Derivatives in the Presence of Auxiliary State Variables On Pricing Derivatives in the Presence of Auxiliary State Variables J. Lin P. Ritchken May 23, 2001 The authors thank L. Sankarasubramanian for extremely helpful comments. Department of Operations, Weatherhead

More information

BACHELIER FINANCE SOCIETY. 4 th World Congress Tokyo, Investments and forward utilities. Thaleia Zariphopoulou The University of Texas at Austin

BACHELIER FINANCE SOCIETY. 4 th World Congress Tokyo, Investments and forward utilities. Thaleia Zariphopoulou The University of Texas at Austin BACHELIER FINANCE SOCIETY 4 th World Congress Tokyo, 26 Investments and forward utilities Thaleia Zariphopoulou The University of Texas at Austin 1 Topics Utility-based measurement of performance Utilities

More information

A No-Arbitrage Theorem for Uncertain Stock Model

A 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 information

Credit Risk Models with Filtered Market Information

Credit Risk Models with Filtered Market Information Credit Risk Models with Filtered Market Information Rüdiger Frey Universität Leipzig Bressanone, July 2007 ruediger.frey@math.uni-leipzig.de www.math.uni-leipzig.de/~frey joint with Abdel Gabih and Thorsten

More information

Calibration of Interest Rates

Calibration of Interest Rates WDS'12 Proceedings of Contributed Papers, Part I, 25 30, 2012. ISBN 978-80-7378-224-5 MATFYZPRESS Calibration of Interest Rates J. Černý Charles University, Faculty of Mathematics and Physics, Prague,

More information

Polynomial Models in Finance

Polynomial Models in Finance Polynomial Models in Finance Martin Larsson Department of Mathematics, ETH Zürich based on joint work with Damir Filipović, Anders Trolle, Tony Ware Risk Day Zurich, 11 September 2015 Flexibility Tractability

More information

MODELING DEFAULTABLE BONDS WITH MEAN-REVERTING LOG-NORMAL SPREAD: A QUASI CLOSED-FORM SOLUTION

MODELING DEFAULTABLE BONDS WITH MEAN-REVERTING LOG-NORMAL SPREAD: A QUASI CLOSED-FORM SOLUTION MODELING DEFAULTABLE BONDS WITH MEAN-REVERTING LOG-NORMAL SPREAD: A QUASI CLOSED-FORM SOLUTION Elsa Cortina a a Instituto Argentino de Matemática (CONICET, Saavedra 15, 3er. piso, (1083 Buenos Aires, Agentina,elsa

More information

Application of Stochastic Calculus to Price a Quanto Spread

Application of Stochastic Calculus to Price a Quanto Spread Application of Stochastic Calculus to Price a Quanto Spread Christopher Ting http://www.mysmu.edu/faculty/christophert/ Algorithmic Quantitative Finance July 15, 2017 Christopher Ting July 15, 2017 1/33

More information

LIBOR models, multi-curve extensions, and the pricing of callable structured derivatives

LIBOR 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 information

EXPLICIT BOND OPTION AND SWAPTION FORMULA IN HEATH-JARROW-MORTON ONE FACTOR MODEL

EXPLICIT BOND OPTION AND SWAPTION FORMULA IN HEATH-JARROW-MORTON ONE FACTOR MODEL EXPLICIT BOND OPTION AND SWAPTION FORMULA IN HEATH-JARROW-MORTON ONE FACTOR MODEL MARC HENRARD Abstract. We present an explicit formula for European options on coupon bearing bonds and swaptions in the

More information

FINANCIAL PRICING MODELS

FINANCIAL PRICING MODELS Page 1-22 like equions FINANCIAL PRICING MODELS 20 de Setembro de 2013 PhD Page 1- Student 22 Contents Page 2-22 1 2 3 4 5 PhD Page 2- Student 22 Page 3-22 In 1973, Fischer Black and Myron Scholes presented

More information

Internet Appendix to Idiosyncratic Cash Flows and Systematic Risk

Internet Appendix to Idiosyncratic Cash Flows and Systematic Risk Internet Appendix to Idiosyncratic Cash Flows and Systematic Risk ILONA BABENKO, OLIVER BOGUTH, and YURI TSERLUKEVICH This Internet Appendix supplements the analysis in the main text by extending the model

More information

THE USE OF NUMERAIRES IN MULTI-DIMENSIONAL BLACK- SCHOLES PARTIAL DIFFERENTIAL EQUATIONS. Hyong-chol O *, Yong-hwa Ro **, Ning Wan*** 1.

THE USE OF NUMERAIRES IN MULTI-DIMENSIONAL BLACK- SCHOLES PARTIAL DIFFERENTIAL EQUATIONS. Hyong-chol O *, Yong-hwa Ro **, Ning Wan*** 1. THE USE OF NUMERAIRES IN MULTI-DIMENSIONAL BLACK- SCHOLES PARTIAL DIFFERENTIAL EQUATIONS Hyong-chol O *, Yong-hwa Ro **, Ning Wan*** Abstract The change of numeraire gives very important computational

More information

Affine Processes, Arbitrage-Free Term Structures of Legendre Polynomials, and Option Pricing

Affine Processes, Arbitrage-Free Term Structures of Legendre Polynomials, and Option Pricing Affine Processes, Arbitrage-Free Term Structures of Legendre Polynomials, and Option Pricing Caio Ibsen Rodrigues de Almeida January 13, 5 Abstract Multivariate Affine term structure models have been increasingly

More information

Numerical schemes for SDEs

Numerical schemes for SDEs Lecture 5 Numerical schemes for SDEs Lecture Notes by Jan Palczewski Computational Finance p. 1 A Stochastic Differential Equation (SDE) is an object of the following type dx t = a(t,x t )dt + b(t,x t

More information

M.I.T Fall Practice Problems

M.I.T Fall Practice Problems M.I.T. 15.450-Fall 2010 Sloan School of Management Professor Leonid Kogan Practice Problems 1. Consider a 3-period model with t = 0, 1, 2, 3. There are a stock and a risk-free asset. The initial stock

More information

On the pricing equations in local / stochastic volatility models

On 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 information

D MATH Departement of Mathematics Finite dimensional realizations for the CNKK-volatility surface model

D MATH Departement of Mathematics Finite dimensional realizations for the CNKK-volatility surface model Finite dimensional realizations for the CNKK-volatility surface model Josef Teichmann Outline 1 Introduction 2 The (generalized) CNKK-approach 3 Affine processes as generic example for the CNNK-approach

More information

Polynomial processes in stochastic portofolio theory

Polynomial processes in stochastic portofolio theory Polynomial processes in stochastic portofolio theory Christa Cuchiero University of Vienna 9 th Bachelier World Congress July 15, 2016 Christa Cuchiero (University of Vienna) Polynomial processes in SPT

More information

Heinz W. Engl. Industrial Mathematics Institute Johannes Kepler Universität Linz, Austria

Heinz W. Engl. Industrial Mathematics Institute Johannes Kepler Universität Linz, Austria Some Identification Problems in Finance Heinz W. Engl Industrial Mathematics Institute Johannes Kepler Universität Linz, Austria www.indmath.uni-linz.ac.at Johann Radon Institute for Computational and

More information

Optimal asset allocation under forward performance criteria Oberwolfach, February 2007

Optimal asset allocation under forward performance criteria Oberwolfach, February 2007 Optimal asset allocation under forward performance criteria Oberwolfach, February 2007 Thaleia Zariphopoulou The University of Texas at Austin 1 References Indifference valuation in binomial models (with

More information

Tangent 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. 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 information

Market interest-rate models

Market 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 information

4: SINGLE-PERIOD MARKET MODELS

4: SINGLE-PERIOD MARKET MODELS 4: SINGLE-PERIOD MARKET MODELS Marek Rutkowski School of Mathematics and Statistics University of Sydney Semester 2, 2016 M. Rutkowski (USydney) Slides 4: Single-Period Market Models 1 / 87 General Single-Period

More information

Lecture 2 - Calibration of interest rate models and optimization

Lecture 2 - Calibration of interest rate models and optimization - Calibration of interest rate models and optimization Elisabeth Larsson Uppsala University, Uppsala, Sweden March 2015 E. Larsson, March 2015 (1 : 23) Introduction to financial instruments Introduction

More information

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

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t Economathematics Problem Sheet 1 Zbigniew Palmowski 1. Calculate Ee X where X is a gaussian random variable with mean µ and volatility σ >.. Verify that where W is a Wiener process. Ws dw s = 1 3 W t 3

More information

Rough volatility models: When population processes become a new tool for trading and risk management

Rough volatility models: When population processes become a new tool for trading and risk management Rough volatility models: When population processes become a new tool for trading and risk management Omar El Euch and Mathieu Rosenbaum École Polytechnique 4 October 2017 Omar El Euch and Mathieu Rosenbaum

More information

Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs.

Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs. Functional vs Banach space stochastic calculus & strong-viscosity solutions to semilinear parabolic path-dependent PDEs Andrea Cosso LPMA, Université Paris Diderot joint work with Francesco Russo ENSTA,

More information

25. Interest rates models. MA6622, Ernesto Mordecki, CityU, HK, References for this Lecture:

25. 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 information

1.1 Basic Financial Derivatives: Forward Contracts and Options

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

More information

A discretionary stopping problem with applications to the optimal timing of investment decisions.

A discretionary stopping problem with applications to the optimal timing of investment decisions. A discretionary stopping problem with applications to the optimal timing of investment decisions. Timothy Johnson Department of Mathematics King s College London The Strand London WC2R 2LS, UK Tuesday,

More information

THE JUMP COMPONENT OF THE VOLATILITY STRUCTURE OF INTEREST RATE FUTURES MARKETS: AN INTERNATIONAL COMPARISON

THE JUMP COMPONENT OF THE VOLATILITY STRUCTURE OF INTEREST RATE FUTURES MARKETS: AN INTERNATIONAL COMPARISON THE JUMP COMPONENT OF THE VOLATILITY STRUCTURE OF INTEREST RATE FUTURES MARKETS: AN INTERNATIONAL COMPARISON CARL CHIARELLA* AND THUY-DUONG TÔ** School of Finance and Economics University of Technology,

More information

An HJM approach for multiple yield curves

An HJM approach for multiple yield curves An HJM approach for multiple yield curves Christa Cuchiero (based on joint work with Claudio Fontana and Alessandro Gnoatto) TU Wien Stochastic processes and their statistics in finance, October 31 st,

More information

Control Improvement for Jump-Diffusion Processes with Applications to Finance

Control Improvement for Jump-Diffusion Processes with Applications to Finance Control Improvement for Jump-Diffusion Processes with Applications to Finance Nicole Bäuerle joint work with Ulrich Rieder Toronto, June 2010 Outline Motivation: MDPs Controlled Jump-Diffusion Processes

More information

Valuation of derivative assets Lecture 8

Valuation of derivative assets Lecture 8 Valuation of derivative assets Lecture 8 Magnus Wiktorsson September 27, 2018 Magnus Wiktorsson L8 September 27, 2018 1 / 14 The risk neutral valuation formula Let X be contingent claim with maturity T.

More information

Large Deviations and Stochastic Volatility with Jumps: Asymptotic Implied Volatility for Affine Models

Large Deviations and Stochastic Volatility with Jumps: Asymptotic Implied Volatility for Affine Models Large Deviations and Stochastic Volatility with Jumps: TU Berlin with A. Jaquier and A. Mijatović (Imperial College London) SIAM conference on Financial Mathematics, Minneapolis, MN July 10, 2012 Implied

More information

Stochastic modelling of term structure of interest rates, realistic with no arbitrage

Stochastic modelling of term structure of interest rates, realistic with no arbitrage Stochastic modelling of term structure of interest rates, realistic with no arbitrage Prof. Dr. Sergey Smirnov Head of the Department of Risk Management and Insurance Director of the Financial Engineering

More information

Fast narrow bounds on the value of Asian options

Fast narrow bounds on the value of Asian options Fast narrow bounds on the value of Asian options G. W. P. Thompson Centre for Financial Research, Judge Institute of Management, University of Cambridge Abstract We consider the problem of finding bounds

More information

"Pricing Exotic Options using Strong Convergence Properties

Pricing Exotic Options using Strong Convergence Properties Fourth Oxford / Princeton Workshop on Financial Mathematics "Pricing Exotic Options using Strong Convergence Properties Klaus E. Schmitz Abe schmitz@maths.ox.ac.uk www.maths.ox.ac.uk/~schmitz Prof. Mike

More information

M5MF6. Advanced Methods in Derivatives Pricing

M5MF6. Advanced Methods in Derivatives Pricing Course: Setter: M5MF6 Dr Antoine Jacquier MSc EXAMINATIONS IN MATHEMATICS AND FINANCE DEPARTMENT OF MATHEMATICS April 2016 M5MF6 Advanced Methods in Derivatives Pricing Setter s signature...........................................

More information

Local Volatility Dynamic Models

Local 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 information

Implementing the HJM model by Monte Carlo Simulation

Implementing the HJM model by Monte Carlo Simulation Implementing the HJM model by Monte Carlo Simulation A CQF Project - 2010 June Cohort Bob Flagg Email: bob@calcworks.net January 14, 2011 Abstract We discuss an implementation of the Heath-Jarrow-Morton

More information

Simulating Stochastic Differential Equations

Simulating Stochastic Differential Equations IEOR E4603: Monte-Carlo Simulation c 2017 by Martin Haugh Columbia University Simulating Stochastic Differential Equations In these lecture notes we discuss the simulation of stochastic differential equations

More information

Resolution of a Financial Puzzle

Resolution of a Financial Puzzle Resolution of a Financial Puzzle M.J. Brennan and Y. Xia September, 1998 revised November, 1998 Abstract The apparent inconsistency between the Tobin Separation Theorem and the advice of popular investment

More information

SYLLABUS. IEOR E4728 Topics in Quantitative Finance: Inflation Derivatives

SYLLABUS. 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 information

Using of stochastic Ito and Stratonovich integrals derived security pricing

Using of stochastic Ito and Stratonovich integrals derived security pricing Using of stochastic Ito and Stratonovich integrals derived security pricing Laura Pânzar and Elena Corina Cipu Abstract We seek for good numerical approximations of solutions for stochastic differential

More information

Supplementary Appendix to The Risk Premia Embedded in Index Options

Supplementary Appendix to The Risk Premia Embedded in Index Options Supplementary Appendix to The Risk Premia Embedded in Index Options Torben G. Andersen Nicola Fusari Viktor Todorov December 214 Contents A The Non-Linear Factor Structure of Option Surfaces 2 B Additional

More information

OPTION PRICE WHEN THE STOCK IS A SEMIMARTINGALE

OPTION 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 information