A pricing measure for non-tradable assets with mean-reverting dynamics

Σχετικά έγγραφα
ST5224: Advanced Statistical Theory II

P AND P. P : actual probability. P : risk neutral probability. Realtionship: mutual absolute continuity P P. For example:

Credit Risk. Finance and Insurance - Stochastic Analysis and Practical Methods Spring School Jena, March 2009

Uniform Convergence of Fourier Series Michael Taylor

Statistical Inference I Locally most powerful tests

Exercises to Statistics of Material Fatigue No. 5

2 Composition. Invertible Mappings

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

Every set of first-order formulas is equivalent to an independent set

Απόκριση σε Μοναδιαία Ωστική Δύναμη (Unit Impulse) Απόκριση σε Δυνάμεις Αυθαίρετα Μεταβαλλόμενες με το Χρόνο. Απόστολος Σ.

Homework 3 Solutions

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Example Sheet 3 Solutions

Mean-Variance Hedging on uncertain time horizon in a market with a jump

SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM

C.S. 430 Assignment 6, Sample Solutions

forms This gives Remark 1. How to remember the above formulas: Substituting these into the equation we obtain with

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Homework 8 Model Solution Section

: Ω F F 0 t T P F 0 t T F 0 P Q. Merton 1974 XT T X T XT. T t. V t t X d T = XT [V t/t ]. τ 0 < τ < X d T = XT I {V τ T } δt XT I {V τ<t } I A

SCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018

Other Test Constructions: Likelihood Ratio & Bayes Tests

CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS

Reminders: linear functions

Math221: HW# 1 solutions

4.6 Autoregressive Moving Average Model ARMA(1,1)

6.3 Forecasting ARMA processes

Chapter 6: Systems of Linear Differential. be continuous functions on the interval

Concrete Mathematics Exercises from 30 September 2016

Jesse Maassen and Mark Lundstrom Purdue University November 25, 2013

Congruence Classes of Invertible Matrices of Order 3 over F 2

Matrices and Determinants

Mean-Variance Analysis

MA 342N Assignment 1 Due 24 February 2016

Second Order Partial Differential Equations

Areas and Lengths in Polar Coordinates

EE512: Error Control Coding

Homomorphism in Intuitionistic Fuzzy Automata

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8 questions or comments to Dan Fetter 1

D Alembert s Solution to the Wave Equation

A Two-Sided Laplace Inversion Algorithm with Computable Error Bounds and Its Applications in Financial Engineering

12. Radon-Nikodym Theorem

CE 530 Molecular Simulation

The k-α-exponential Function

HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:

MATH423 String Theory Solutions 4. = 0 τ = f(s). (1) dτ ds = dxµ dτ f (s) (2) dτ 2 [f (s)] 2 + dxµ. dτ f (s) (3)

Exercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1.

Areas and Lengths in Polar Coordinates

The Simply Typed Lambda Calculus

Iterated trilinear fourier integrals with arbitrary symbols

Solution Series 9. i=1 x i and i=1 x i.

Abstract Storage Devices

Coefficient Inequalities for a New Subclass of K-uniformly Convex Functions

Finite Field Problems: Solutions

ANSWERSHEET (TOPIC = DIFFERENTIAL CALCULUS) COLLECTION #2. h 0 h h 0 h h 0 ( ) g k = g 0 + g 1 + g g 2009 =?

Section 8.3 Trigonometric Equations

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in

Reflecting Brownian motion in two dimensions: Exact asymptotics for the stationary distribution

Appendix S1 1. ( z) α βc. dβ β δ β

Math 446 Homework 3 Solutions. (1). (i): Reverse triangle inequality for metrics: Let (X, d) be a metric space and let x, y, z X.

Lecture 21: Properties and robustness of LSE

Tridiagonal matrices. Gérard MEURANT. October, 2008

Second Order RLC Filters

557: MATHEMATICAL STATISTICS II RESULTS FROM CLASSICAL HYPOTHESIS TESTING

Teor imov r. ta matem. statist. Vip. 94, 2016, stor

A Bonus-Malus System as a Markov Set-Chain. Małgorzata Niemiec Warsaw School of Economics Institute of Econometrics

Takeaki Yamazaki (Toyo Univ.) 山崎丈明 ( 東洋大学 ) Oct. 24, RIMS

Part III - Pricing A Down-And-Out Call Option

( y) Partial Differential Equations

Space-Time Symmetries

Notes on the Open Economy

ORDINAL ARITHMETIC JULIAN J. SCHLÖDER

Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data

Solutions to Exercise Sheet 5

CRASH COURSE IN PRECALCULUS

Higher Derivative Gravity Theories

5. Choice under Uncertainty

Figure A.2: MPC and MPCP Age Profiles (estimating ρ, ρ = 2, φ = 0.03)..

ECE598: Information-theoretic methods in high-dimensional statistics Spring 2016

Risk! " #$%&'() *!'+,'''## -. / # $

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β

A summation formula ramified with hypergeometric function and involving recurrence relation

Srednicki Chapter 55

Mock Exam 7. 1 Hong Kong Educational Publishing Company. Section A 1. Reference: HKDSE Math M Q2 (a) (1 + kx) n 1M + 1A = (1) =

Appendix to Technology Transfer and Spillovers in International Joint Ventures

Section 7.6 Double and Half Angle Formulas

Μηχανική Μάθηση Hypothesis Testing

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p)

6.1. Dirac Equation. Hamiltonian. Dirac Eq.

ECE Spring Prof. David R. Jackson ECE Dept. Notes 2

Empirical best prediction under area-level Poisson mixed models

Probability and Random Processes (Part II)

1. A fully continuous 20-payment years, 30-year term life insurance of 2000 is issued to (35). You are given n A 1

HW 3 Solutions 1. a) I use the auto.arima R function to search over models using AIC and decide on an ARMA(3,1)

The Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality

Partial Differential Equations in Biology The boundary element method. March 26, 2013

Parametrized Surfaces

Additional Results for the Pareto/NBD Model

ΤΟ ΜΟΝΤΕΛΟ Οι Υποθέσεις Η Απλή Περίπτωση για λi = μi 25 = Η Γενική Περίπτωση για λi μi..35

Transcript:

A pricing measure for non-tradable assets with mean-reverting dynamics Salvador Ortiz-Latorre, UiO joint work with Fred E. Benth AMAMEF & Swiss Quote Conference EPFL, Laussane 7-10 September 2015 Support from the projects EMMOS and MAWREM is gratefully acknowledged

Non tradable assets I An asset is non tradable if we cannot build a portfolio with it. I Energy related examples: non-storable commodities, weather indices, electricity... I Interested in pricing forwards contracts on these assets. I There is no buy and hold strategy =) classical non-arbitrage arguments break down. I Any probability measure Q equivalent to the historical measure P is a valid risk neutral pricing measure. I The forward price with time to delivery 0 < T < T at time 0 < t < T is given by F Q (t, T ) = E Q [S(T )jf t ] where F t is the information in the market up to time t. Assuming deterministic interest rates and r = 0.

Risk premium pro le I The risk premium for forward prices is de ned by RQ F (t, T ), E Q [S(T )jf t ] E P [S(T )jf t ]. I Goal: To be able to obtain more realistic risk premium pro les. For instance: R 4 2 2 4 50 100 150 200 250 300 350 where τ = T t is the time to delivery.

Mathematical model I Let (Ω, F, ff t g t2[0,t ], P) be a ltered probability space satisfying the usual hypothesis, where T > 0 is a xed nite time horizon. I Consider a standard Brownian motion W and a pure jump Lévy subordinator Z L(t) = zn L (ds, dz), t 2 [0, T ], 0 0 where N L (ds, dz) is a Poisson random measure with Lévy measure ` satisfying R 0 z`(dz) <. I Let κ L (θ), log E P [e θl(1) ], and Θ L, supfθ 2 R + : E P [e θl(1) ] < g. I A minimal assumption is that Θ L > 0.

Mathematical model I Consider the Ornstein-Uhlenbeck processes X with Barndor -Nielsen & Shephard stochastic volatility σ X (t) = X (0) α X (s)ds + σ(s)w (t), 0 0 σ 2 (t) = σ 2 (0) ρ 0 σ2 (s))ds + L(t) Z = σ 2 (0) + 0 (κ0 L (0) ρσ2 (s))ds + 0 0 with t 2 [0, T ], α, ρ > 0, X (0) 2 R, σ 2 (0) > 0 and zñ L (ds, dz), Ñ L (ds, dz) = N L (ds, dz) `(dz)ds. I We model the spot price process by S(t) = Λ g exp (X (t)), t 2 [0, T ].

The change of measure I Let β = (β 1, β 2 ) 2 [0, 1] 2 and θ = (θ 1, θ 2 ) 2 D L, R D L, where D L, (, Θ L /2). I Consider the following family of kernels G θ1,β 1 (t), σ 1 (t) (θ 1 + αβ 1 X (t)), t 2 [0, T ], H θ2,β 2 (t, z), e θ 2z 1 + ρβ 2 κl 00(θ 2) zσ2 (t ), t 2 [0, T ], z 2 R +. I Next, de ne the following family of Wiener and Poisson integrals G θ1,β 1 (t), G θ 1,β 1 (s)dw (s), t 2 [0, T ], 0 Z H θ2,β 2 (t), H θ2,β 2 (s, z) 1 Ñ L (ds, dz), t 2 [0, T ], 0 0 associated to the kernels G θ1,β 1 and H θ2,β 2, respectively.

The change of measure I The family of measure changes is given by Q θ, β P, β 2 [0, 1] 2, θ 2 D L, with dq θ, β dp, E( G θ1,β 1 + H θ2,β 2 )(t), t 2 [0, T ], Ft where E() denotes the stochastic exponential. I Recall that, if M is a semimartingale, the stochastic exponential of M is the unique strong solution of de(m)(t) = E(M)(t )dm(t), t 2 [0, T ], E(M)(0) = 1, which is given by E(M)(t) = exp M(t) 1 2 hmc, M c i(t) (1 + M(s)) e M (s). 0st

The change of measure I Yor s Addition Formula: Let M 1 and M 2 two semimartingales starting at 0. Then, E(M 1 + M 2 + [M 1, M 2 ])(t) = E(M 1 )(t)e(m 2 )(t), 0 t T, where [M 1, M 2 ](t) = hm1 c, Mc 2 i(t) + M 1 (s) M 2 (s). st I As [ G θ1,β 1, H θ2,β 2 ] 0, we can write E( G θ1,β 1 + H θ2,β 2 )(t) = E( G θ1,β 1 )(t)e( H θ2,β 2 )(t), t 2 [0, T ]. I Conditioning on F L T, we have E P [E( G θ1,β 1 + H θ2,β 2 )(T )] = E P [E P [E( G θ1,β 1 )(T )jf L T ]E( H θ2,β 2 )(T )]. I Hence the problem is reduced to show that E( G θ1,β 1 ) and E( H θ2,β 2 ) are true martingales with respect to appropriate ltrations.

Sketch of the proof that E(M) is a martingale I M can be G θ1,β 1 or H θ2,β 2. I Localize E(M) using a reducing sequence fτ n g n1. I Check that 1 = lim n! E P [E(M) τn (T )] = E P [E(M)(T )]. I Test the uniform integrability of fe(m) τ n (T )g n1 with F (x) = x log(x), i.e. sup E P [F (E(M) τ n (T ))] <. (1) n I For any n 1, fe(m) τ n (t)g t2[0,t ] is a true martingale and induces a change of measure dq n dp = E(M) τ n (t). Ft I Condition (1) can be rewritten as sup n E Q n [log(e(m) τ n (T ))] <. I We can get rid of the ordinary exponential in E(M) τ n (T ). I The problem is reduced to nd a uniform bound for the second moment of X and σ 2 under Q n.

The dynamics under the new pricing measure I By Girsanov s theorem for semimartingales, we can write X (t) = X (0) + BQ X (t) + σ(s)dw θ, Q (t), t 2 [0, T β 0 θ, β ], Z σ 2 (t) = σ 2 (0) + BQ σ2 (t) + zñ θ, β Q L (ds, dz), t 2 [0, T ], 0 0 θ, β where B X Q θ, β (t) = 0 (θ 1 α(1 β 1 )X (s))ds, t 2 [0, T ], and BQ σ2 (t) = κ 0 θ, β L (θ 2) ρ(1 β 2 )σ 2 (s) ds, t 2 [0, T ]. 0

The dynamics under the new pricing measure I The Q θ, β -compensator measure of σ2 is given by vq σ2 (dt, dz) = θ, β eθ 2z 1 + ρβ 2 κl 00(θ 2) zσ2 (t ) `(dz)dt. I Using integration by parts, we get X (T ) = X (t)e α(1 β 1)(T t) θ + 1 α(1 β 1 ) (1 e α(1 β 1)(T t) ) Z T + σ(u)e α(1 β 1)(T t u) dw Q (u), θ, β σ 2 (T ) = σ 2 (t)e α(1 β 2)(T t) + κ0 L (θ 2) where 0 t T T. α(1 β 2 ) (1 e α(1 β 2)(T t) ) Z T Z + e ρ(1 β 2)(T u) zñq L (du, dz), t 0 θ, β

Moment condition under the historical measure I A su cient condition for S to have nite expectation under P is the following: Assumption (P) We assume that α, ρ > 0 and Θ L satisfy for some δ > 0. 1 ρ 2ρ 2α 1 ρ 1 2α Θ L δ, I If Θ L = then assumption P is satis ed. I If Θ L <, then if we choose ρ close to zero the value of α must be bounded away from zero, and vice versa, for assumption P to be satis ed.

Forward price formula Proposition The forward price F Q θ, (t, T ) is given by β F Q (t, T ) θ, β! = Λ g (T ) exp X (t)e α(1 β 1)(T t) + σ 2 (t)e ρ(1 β 2)(T t) 1 e (2α ρ(1 β 2))(T t) 2(2α ρ(1 β 2 )) κ 0 exp L (θ!! 2) 1 e 2α(T t) e ρ(1 β 2)(T t) 1 e (2α ρ(1 β 2))(T t) 2ρ(1 β 2 ) 2α (2α ρ(1 β 2 )) θ exp 1 α(1 β 1 ) (1 e α(1 β 1)(T t) ) " e 2αT Z T Z s Z! # E Q θ, exp e (2α ρ(1 β 2))s e ρ(1 β2)u zñ L β 2 Q (du, dz) ds jf t t t 0 In the particular case Q θ, β = P, it holds that! F P (t, T ) = Λ g (T ) exp X (t)e α(t t) + σ 2 ρ(t t) 1 e (2α ρ)(t t) (t)e 2(2α ρ)!! Z T t ρs 1 e (2α ρ)s exp κ L e ds. 0 2(2α ρ)

A ne transform formula Theorem Let β = (β 1, β 2 ) 2 [0, 1] 2, θ = (θ 1, θ 2 ) 2 D L and T > 0. Suppose there exist functions Ψ θ, β i, i = 0, 1, 2 belonging to C 1 ([0, T ]; R), satisfying the generalised Riccati equation d dt Ψ θ, β 0 (t) = θ 1Ψ θ, β 2 (t) + κ L d dt Ψ θ, β 1 (t) = ρψ θ, β 1 (t) + (Ψ θ, β d dt Ψ θ, β 2 (t) = α(1 β 1)Ψ θ, β 2 (t), Ψ θ, β 1 (t) + θ 2 2 (t))2 2 + ρβ 2 κl 00(θ 2) κ 0 L κ L (θ 2 ), Ψ θ, β 1 (t) + θ 2 with initial conditions Ψ θ, β 0 (0) = Ψ θ, β 1 (0) = 0 and Ψ θ, β 2 (0) = 1. Moreover, suppose that the integrability condition sup κl 00 θ 2 + Ψ θ, β 1 (t) <, t2[0,t ] holds. κl 0 (θ 2),

A ne transform formula Theorem (Continue) Then, and E Q [exp(x (T ))jf θ, β t ] = exp Ψ θ, β 0 (T t) + Ψ θ, β 1 (T t)σ2 (t) + Ψ θ, β 2 (T t)x (t), RQ F (t, T ) = E θ, P [S(T )jf β t ] ( for t 2 [0, T ]. exp Ψ θ, Z T t β 0 (T t) κ L e 0 + Ψ θ, β ρ(t t) 1 e (2α ρ)(t t) 1 (T t) e 2(2α ρ) + Ψ θ, β 2 (T t) e α(t t) X (t)! ρs 1 e (2α ρ)s ds 2(2α ρ)! o 1, σ 2 (t)

Some remarks on the theorem I The proof follows by applying a result by Kallsen and Muhle-Karbe (2010). I Limited applicability as it is stated. I Reduction to a one dimensional non autonomous ODE. I I We have that for any θ 2 D L, β 2 [0, 1] 2, the solution of the last equation is given by Ψ θ, β 2 (t) = exp( α(1 β 1)t). Plugging this solution to the rst equation we get the following equation to solve for Ψ θ, β 1 (t) d dt Ψ θ, β 1 (t) = ρψ θ, β 1 (t) + e 2α(1 β1)t 2 + ρβ 2 κl 00(θ 2) (κ0 L (Ψ θ, β 1 (t) + θ 2) κl 0 (θ 2)), I with initial condition Ψ θ, β 1 (0) = 0. θ, β The equation for Ψ 0 (t) is solved by integrating Λ θ, β 0 (Ψ θ, β 1 (t), Ψ θ, β 2 (t)), i.e., Ψ θ, β 0 (t) = 0 1 e α(1 β1)t = θ 1 α(1 β 1 ) fθ 1 Ψ θ, β 2 (s) + κ L(Ψ θ, β 1 (s) + θ 2) κ L (θ 2 )gds + fκ L (Ψ θ, β 1 (s) + θ 2) κ L (θ 2 )gds. 0

Su cient conditions for existence of global solutions I Study of the equation d dt ˆΨ θ 2,β 2 (t) = Λ θ 2,β 2 ( ˆΨ θ 2,β 2 ), where I Let Λ θ 2,β 2 (u) = ρu + 1 2 + ρβ 2 κ 00 L (θ 2) (κ0 L (u + θ 2) κ 0 L (θ 2)). D b = f(θ 2, β 2 ) 2 D L (0, 1) : 9u 2 [0, Θ L θ 2 ) s.t. Λ θ 2,β 2 (u) 0g. Theorem If (θ 2, β 2 ) 2 D b and (θ 1, β 1 ) 2 R [0, 1) then Ψ θ, β 0 (t), Ψ θ, β 1 (t) and Ψ θ, β 2 (t)) are C 1 ([0, T ]; R) for any T > 0. Moreover, Ψ θ, β 0 (t)! θ Z 1 nκ α(1 β 1 ) + L Ψ θ, β 1 (s) + θ 2 0 and 1 (t), Ψ θ, β 2 (t))! (0, 0), t!, (Ψ θ, β t 1 log (Ψ θ, β for some negative constant γ. 1 (t), Ψ θ, β 2 (t))! γ, t!, o κ L (θ 2 ) ds, t!,

Risk premium analysis in the geometric BNS model Lemma If (θ 2, β 2 ) 2 D b and (θ 1, β 1 ) 2 R [0, 1), the sign of the risk premium RQ F (t, T ) is the same as the sign of the function θ, β Σ(t, T ), Ψ θ, β 0 (T t) Ψ0,0 Moreover, + (Ψ θ, β 2 lim Σ(t, T ) T t! = 0 (T t) + (Ψ θ, β 1 (T t) Ψ0,0 2 (T t))x (t). θ 1 α(1 β 1 ) + Z Z Z 1 0 0 Z 1 0 κ0 L λψ θ, β 1 (s) + θ 2! 0 κ0 L λe ρs 1 e (2α ρ)s 2(2α ρ) (T t) Ψ0,0 1 (T t))σ2 (t) dλψ θ, β 1 (s)ds dλe ρs 1 e (2α ρ)s ds, 2(2α ρ) and d lim T t!0 dt Σ(t, T ) = θ 1 + αβ 1 X (t). I Analysis of possible risk pro les in Benth and O.-L. (2015).

Conclusions I A pricing measure for the BNS model for non-tradable assets with mean reversion extending Esscher s transform. I Preserves a ne structure of the model. I Gives control on the speed and level of mean reversion. I Provides more realistic risk premium pro les. Further work I Study the BNS model with jumps I Calibration. X (t) = X (0) σ 2 (t) = σ 2 (0) I Pricing more complex derivatives. I Other models with mean reversion. α X (s)ds + σ(s)w (t) + ηl(t), 0 0 ρ 0 σ2 (s)ds + L(t).

References Barndor -Nielsen, O. E. and Shephard, N. (2001). Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in nancial economics. Journal of the Royal Statistical Society Series B, Royal Statistical Society, Vol. 63(2), pp. 167-241. Benth, F.E. (2011) The stochastic volatility model of Barndor -Nielsen and Shepard in commodity markets. Math. Finance, 21, pp. 595-625. Benth, F.E. and Ortiz-Latorre, S. (2014) A pricing measure to explain the risk premium in power markets. SIAM Journal on Financial Mathematics, Vol. 5, No. 1, pp. 685-728. Benth, F.E. and Ortiz-Latorre, S. (2015) A change of measure preserving the a ne structure in the BNS model for commodity markets. To appear in International Journal of Theoretical and Applied Finance. Kallsen, J. and Muhle-Karbe, J. (2010). Exponentially a ne martingales, a ne measure changes and exponential moments of a ne processes. Stochastic Processes and their Applications 120, pp. 163 181.