Time-changed Brownian motion and option pricing - IM...

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  • Time-changed Brownian motionand option pricing

    Peter Hieber

    Chair of Mathematical Finance, TU Munich

    6th AMaMeF Warsaw, June 13th 2013

    Partially joint with

    Marcos Escobar (RU Toronto),

    Matthias Scherer (TU Munich).

  • Motivation

    Stock price process {St}t0with known characteristic function (u)

    of the log-asset price ln(ST ).

    How to compute the

    stock price density of STefficiently?

    How to compute

    f (k) := exp(iuk)(u) du ?

    Is it possible

    to price more complicated products

    like barrier options?

    Peter Hieber, Time-changed Brownian motion and option pricing 2

  • Overview

    (1) Time-changed geometric Brownian motion (GBM)

    (2) Pricing barrier options

    (3) Pricing call options

    (4) Extensions and examples

    Peter Hieber, Time-changed Brownian motion and option pricing 3

  • Time-changed GBMDefinition

    Consider a geometric Brownian motion (GBM)

    dStSt

    = rdt + dWt, (1.1)

    where r R, > 0, and Wt is a standard Brownian motion.

    Introduce a stochastic clock = {t}t0 (independent of S) and consider Stinstead of St.

    Definition 1.1 (Time-changed Brownian motion) Let = {t}t0 be anincreasing stochastic process with 0 = 0, limt t = Q-a.s.. This stochastictime-scale is used to time-change S, i.e. we consider the process St, for t 0.

    Denote the Laplace transform of T by T (u) := E[exp(uT )], u 0.

    Peter Hieber, Time-changed Brownian motion and option pricing 4

  • Time-changed GBMMotivation

    Time-changed Brownian motion is convenient since:

    Natural interpretation of time-change as measure of economic activity(business time scale, transaction clock).

    Many well-known models can be represented as a time-changed Brownianmotion (e.g. Variance Gamma, Normal inverse Gaussian). This coversnot only Lvy-type models, but also regime-switching, Sato, orstochastic volatility models.

    Peter Hieber, Time-changed Brownian motion and option pricing 5

  • Time-changed GBMMotivation

    continuously time-changedBrownian motion

    time-changed Brownian motion

    If the time change {t}t0 is continuous, it is possible to derive the first-passagetime of {St}t0 analytically following Hieber and Scherer [2012].

    Peter Hieber, Time-changed Brownian motion and option pricing 6

  • Time-changed GBMMotivation

    down-and-out call options(=barrier options)

    call options

    Peter Hieber, Time-changed Brownian motion and option pricing 7

  • Time-changed GBMExample 1: Variance Gamma model

    The Variance Gamma process, also known as Laplace motion, is obtained if aGBM (drift , volatility > 0) is time-changed by a Gamma(t; 1/, ) process, > 0. The drift adjustment due to the jumps is := ln

    (1 2/2

    )/.

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

    0.65

    0.7

    0.75

    0.8

    0.85

    0.9

    0.95

    1

    1.05

    time t

    sam

    ple

    path

    Peter Hieber, Time-changed Brownian motion and option pricing 8

  • Time-changed GBMExample 2: Markov switching model

    The Markov switching model (see, e.g., Hamilton [1989]):

    dStSt

    = rdt + ZtdWt, S0 > 0, (1.2)

    where Z = {Zt}t0 {1, 2, . . . ,M} is a time-homogeneous Markov chain withintensity matrix Q0 and W = {Wt}t0 an independent Brownian motion.

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.5

    0.6

    0.7

    0.8

    0.9

    1

    1.1

    1.2

    1.3

    1.4

    1.5

    time t

    sam

    ple

    path

    Peter Hieber, Time-changed Brownian motion and option pricing 9

  • Time-changed GBMFurther examples

    The class of time-changed GBM is rich. It also contains

    Stochastic volatility models: Heston model, Stein & Stein model,Hull-White model, certain continuous limits of GARCH models.

    The Normal inverse Gaussian model.

    Sato models: For example extensions of the Variance Gamma model.

    The Ornstein-Uhlenbeck process.

    The class is restricted by the fact that the time change {t}t0 is independent ofthe stock price process {St}t0.

    Peter Hieber, Time-changed Brownian motion and option pricing 10

  • Overview

    (1) Time-changed geometric Brownian motion (GBM)

    (2) Pricing barrier options

    (3) Pricing call options

    (4) Extensions and examples

    Peter Hieber, Time-changed Brownian motion and option pricing 11

  • Pricing barrier options

    Barrier options with payoff

    1{D

  • Pricing barrier optionsTransition density

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

    0.7

    0.8

    0.9

    1

    1.1

    1.2

    1.3

    time t

    sam

    ple

    path

    D

    PA transition density describes the probability density that theprocess S starts at time 0 at S0, stays within the corridor [D,P ]until time T > 0 and ends up at ST at time T .(This of course implies that S0 (D, P ) and ST (D, P ).)

    More formally,

    p(T, S0, ST ) := Q(ST dx, D < St < P for 0 t T

    S0 = s0) .

    Peter Hieber, Time-changed Brownian motion and option pricing 13

  • Pricing barrier optionsTransition density

    Lemma 1.2 (Transition density GBM)Consider S = {St}t0 with drift r R and volatility > 0. S starts at S0, stayswithin the corridor (D,P ) until time T and ends up in ST . Its transition density is

    p(T, S0, ST ) =2 exp

    (2 ln(ST/S0)

    )ln(P/D)

    n=1

    An sin

    (n ln(ST )

    ln(P/D)

    )exp(nT ) .

    where

    n :=1

    2

    (2

    2+

    n222

    ln(P/D)2

    ), An := sin

    (n ln(S0)

    ln(P/D)

    ), := r 1

    22 .

    Proof: Cox and Miller [1965], see also Pelsser [2000].

    Peter Hieber, Time-changed Brownian motion and option pricing 14

  • Pricing barrier optionsTransition density

    Theorem 1.3 (Transition density time-changed GBM)Consider S = {St}t0 with drift r R and volatility > 0, time-changed byindependent {t}t0 with Laplace transform T (u). S starts at S0, stays withinthe corridor (D,P ) until time T and ends up in ST . Its transition density is

    p(T, S0, ST ) =2 exp

    (2 ln(ST/S0)

    )ln(P/D)

    n=1

    An sin

    (n ln(ST )

    ln(P/D)

    )T(n) .

    where

    n :=1

    2

    (2

    2+

    n222

    ln(P/D)2

    ), An := sin

    (n ln(S0)

    ln(P/D)

    ), := r 1

    22 .

    Peter Hieber, Time-changed Brownian motion and option pricing 15

  • Pricing barrier optionsTransition density

    Proof 1 (Transition density time-changed GBM)

    If the time-change {t}t0 is continuous, we are conditional on T back inthe case of Brownian motion.

    Then, by Lemma 1.2

    p(T , S0, x) = const.

    n=1

    An sin

    (n ln(x)

    ln(P/D)

    )exp(nT ) .

    From this,

    EQ[p(T , S0, x)

    ]= const.

    n=1

    An sin

    (n ln(x)

    ln(P/D)

    )E[exp(nT )

    ]= const.

    n=1

    An sin

    (n ln(x)

    ln(P/D)

    )T(n) .

    Peter Hieber, Time-changed Brownian motion and option pricing 16

  • Pricing barrier options

    Theorem 1.4 (Barrier options, Escobar/Hieber/Scherer (2013))Consider S = {St}t0 with drift r R and volatility > 0, continuouslytime-changed by independent {t}t0 with Laplace transform T (u). S starts atS0. Conditional on {D < St < P, for 0 t T}, the price of a down-and-out calloption with strike K and maturity T is

    DOC(0) =2

    ln(P/D)

    n=1

    T(n)An

    PD

    max(ST K, 0

    )sin

    (n ln(ST )

    ln(P/D)

    )exp( 2

    ln(ST/S0))dST ,

    where

    n :=1

    2

    (2

    2+

    n222

    ln(P/D)2

    ), An := sin

    (n ln(S0)

    ln(P/D)

    ), := r 1

    22 .

    Peter Hieber, Time-changed Brownian motion and option pricing 17

  • Pricing barrier options

    Proof 2 (Barrier options)

    DOC(0) =

    PD

    max(ST K, 0

    )p(T, S0, ST ) dST

    = const.

    n=1

    An T(n)

    PD

    max(ST K, 0

    )sin

    (n ln(ST )

    ln(P/D)

    )exp( 2

    ln(ST/S0))dST .

    The integral PD max

    (ST K, 0

    )sin(n ln(ST )ln(P/D)

    )dST can be computed explicitly.

    The same ideas apply to any other down-and-out contract(e.g. bonus certificates, digital options).

    Peter Hieber, Time-changed Brownian motion and option pricing 18

  • Pricing barrier optionsNumerical example

    Implementation:

    DOC(0) = const.

    n=1

    fn(K)T(n)

    const.Nn=1

    fn(K)T(n) .

    Error bounds for the truncation parameter N are available for many models.

    Peter Hieber, Time-changed Brownian motion and option pricing 19

  • Overview

    down-and-out call options(=barrier options)

    call options

    Peter Hieber, Time-changed Brownian motion and option pricing 20

  • Pricing call options

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.4

    0.6

    0.8

    1

    1.2

    1.4

    time t

    sam

    ple

    path

    D

    P

    Sample path of {St}t0 with a lower barrier D and an upper barrier P .

    Peter Hieber, Time-changed Brownian motion and option pricing 21

  • Pricing call options

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.4

    0.6

    0.8

    1

    1.2

    1.4

    time t

    sam

    ple

    path

    D

    P

    Sample path of {St}t0 with a lower barrier D and an upper barrier P .

    Peter Hieber, Time-changed Brownian motion and option pricing 21

  • Pricing call options

    0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.4

    0.6

    0.8

    1

    1.2

    1.4

    time t

    sam

    ple

    path

    P

    D

    Sample path of {St}t0 with a lower barrier D and an upper barrier P .

    A barrier option can approximate a call option, i.e.

    1{D

  • Pricing call optionsNumerical example

    (Vanilla) Call options can be approximated by barrier options.

    Again: Black-Scholes model (r = 0, = 0.2), T = 1, K = 80.

    (D;P ) barrier price N comp. time

    (0.7; 1.3) 12.21580525385 7 0.1ms

    (0.6; 1.4) 13.08137347245 9 0.1ms

    (0.4; 2.7) 21.18586311986 22 0.1ms

    (0.1; 7.4) 21.18592951321 44 0.1ms

    call price comp. time

    21.18592951321 1.2ms

    Computation of barrier options faster than Black-Scholes formulaa.

    Accuracy of approximation is very high.

    aThe call option was priced using blsprice.m in Matlab (version 2009a).

    Peter Hieber, Time-changed Brownian motion and option pricing 21

  • Overview

    (1) Time-changed geometric Brownian motion (GBM)

    (2) Pricing barrier options

    (3) Pricing call options

    (4) Extensions and examples

    Peter Hieber, Time-changed Brownian motion and option pricing 22

  • Numerical example

    Stock price process {St}t0with known characteristic function (u)

    of the log-asset price ln(ST ).

    How to compute

    E[(ST K)+

    ]:=

    0 exp(iuk) ((u), u

    )du ?

    Peter Hieber, Time-changed Brownian motion and option pricing 23

  • Numerical exampleAlternatives

    Fast Fourier pricing: Most popular approach, see Carr and Madan [1999].Many extensions, e.g., Raible [2000], Chourdakis [2004].

    Black-Scholes (BS) approximation: Works for time-changed Brownianmotion, see Albrecher et al. [2013].

    E[(ST K)+

    ] const.

    Nn=1

    Bn CBS(n, n, K

    ).

    COS Method: Closest to our approach, see Fang and Oosterlee [2008].

    E[(ST K)+

    ] const.

    Nn=1

    Cn(K) Re(( na b

    )ein

    bab

    ).

    Rational approximations: Works for time-changed Brownian motion, seePistorius and Stolte [2012]. Uses Gauss-Legendre quadrature.

    E[(ST K)+

    ] const.

    Nn=1

    Dn(K)

    (Mm=1

    cmxn + dm

    )T(xn) .

    Peter Hieber, Time-changed Brownian motion and option pricing 24

  • Numerical exampleParameter set

    Variance Gamma model Markov switching model

    parameter set -0.10 -0.20 -0.30 0.10 0.20 0.30 0.15 0.30 0.45T 0.10 0.25 1.00

    parameter set 1 0.10 0.20 0.302 0.10 0.15 0.201 0.10 0.50 1.002 0.10 1.00 2.00T 0.10 0.25 1.00

    The parameters sets were obtained from Chourdakis [2004].

    We use 31 equidistant strikes K out of [85, 115], the current price is S0 = 100.

    The rows and allow us to test many different parameter sets to adequatelycompare the different numerical techniques.

    Peter Hieber, Time-changed Brownian motion and option pricing 25

  • Numerical exampleResults I: Pricing call options

    Variance Gamma model (char. fct. decays hyperbolically)

    our approach FFT COS method BS approx.

    N 100 4096 200 10

    average comp. time 0.5ms 4.9ms 1.4ms 0.3ms

    average rel. error 4.5e-08 2.0e-07 3.5e-07 5.4e-05

    max. rel. error 2.7e-07 5.8e-07 2.6e-06 3.0e-04

    sample price 20.76524 20.76523 20.76524 20.76105

    Numerical comparison on different parameter sets following Chourdakis [2004].A sample price was obtained using K = 80 and the average parameter set fromslide 26. The barriers (D;P ) were set to (exp(3); exp(3)).

    Peter Hieber, Time-changed Brownian motion and option pricing 26

  • Numerical exampleResults II: Pricing call options

    Absolute error vs. number of terms N : Variance Gamma model.

    2 4 8 16 32 64 128 256 512 1024 20480

    0.001

    0.002

    0.003

    0.004

    0.005

    0.006

    0.007

    0.008

    0.009

    0.01

    abso

    lute

    err

    or

    N

    our approachBS approximationCOS methodrational approximationFFT

    Peter Hieber, Time-changed Brownian motion and option pricing 27

  • Numerical exampleResults III: Pricing call options

    Absolute error vs. number of terms N : Markov switching model.

    2 4 8 16 32 64 128 256 512 1024 20480

    0.001

    0.002

    0.003

    0.004

    0.005

    0.006

    0.007

    0.008

    0.009

    0.01

    abso

    lute

    err

    or

    N

    our approachCOS methodFFTrational approximation

    Peter Hieber, Time-changed Brownian motion and option pricing 28

  • Numerical exampleResults IV: Pricing call options

    Logarithmic error vs. number of terms N : Markov switching model.

    2 4 8 16 32 64 128 256 512 1024 204835

    30

    25

    20

    15

    10

    5

    0

    5

    10

    abso

    lute

    err

    or

    N

    our approachCOS methodFFTrational approximation

    Peter Hieber, Time-changed Brownian motion and option pricing 29

  • Numerical exampleDiscussion

    Our approach and the Fang and Oosterlee [2008] results are extremely fastfor quickly (e.g. exponentially) decaying characteristic functions.

    High accuracy (e.g. 1e10) is possible since one avoids any kind ofdiscretization. Error bounds are available.

    Evaluation of several strikes comes at almost no cost.

    Apart from option pricing, one is able to evaluate densities or distributionswith known characteristic function.

    Peter Hieber, Time-changed Brownian motion and option pricing 30

  • Summary

    continuously time-changedBrownian motion

    time-changed Brownian motion

    Peter Hieber, Time-changed Brownian motion and option pricing 31

  • Summary

    down-and-out call options(=barrier options)

    call options

    Peter Hieber, Time-changed Brownian motion and option pricing 32

  • Literature

    H.-J. Albrecher, F. Guillaume, and W. Schoutens. Implied liquidity: Model sensitivity. Journal of Empirical Finance, Vol. 23:pp. 4867,2013.

    P. Carr and D. B. Madan. Option valuation using the fast Fourier transform. Journal of Computational Finance, Vol. 2:pp. 6173,1999.

    K. Chourdakis. Option pricing using the fractional FFT. Journal of Computational Finance, Vol. 8, No. 2:pp. 118, 2004.

    M. Escobar, P. Hieber, and M. Scherer. Efficiently pricing barrier derivatives in stochastic volatility models. Working paper, 2013.

    F. Fang and K. Oosterlee. A novel pricing method for European options based on Fourier-Cosine series expansions. SIAM Journal onScientific Computing, Vol. 31, No. 2:pp. 826848, 2008.

    P. Hieber and M. Scherer. A note on first-passage times of continuously time-changed Brownian motion. Statistics & ProbabilityLetters, Vol. 82, No. 1:pp. 165172, 2012.

    A. Pelsser. Pricing double barrier options using Laplace transforms. Finance and Stochastics, Vol. 4:pp. 95104, 2000.

    M. Pistorius and J. Stolte. Fast computation of vanilla prices in time-changed models and implied volatilities using rationalapproximations. International Journal of Theoretical and Applied Finance, Vol. 14, No. 4:pp. 134, 2012.

    S. Raible. Lvy processes in finance: Theory, numerics, and empirical facts. PhD thesis, Freiburg University, 2000.

    Peter Hieber, Time-changed Brownian motion and option pricing 33

  • Discontinuous time-change

    Example of a discontinuous time-change. While the original process {Bt}t0(black) hits the barrier, the time-changed process {Bt}t0 (grey) does not. Thisis not possible if the time-change is continuous; then all barrier crossings are

    observed until time T .

    0 0.2 0.4 0.6 0.8 1

    0.2

    0.1

    0

    0.1

    0.2

    0.3

    calendar time t

    Bro

    wni

    an m

    otio

    n

    original process B

    t

    timechanged process Bt

    0 0.2 0.4 0.6 0.8 10

    0.1

    0.2

    0.3

    0.4

    0.5

    0.6

    0.7

    0.8

    0.9

    1

    calendar time t

    time

    chan

    ge

    t

    Peter Hieber, Time-changed Brownian motion and option pricing 34