Chapter 4 Applications
6.2 Concluding Remarks
Because Eq. (6.6) coincides with Eq. (5.8) after rearranging, the exact backward dynamics coincide with the optimal backward dynamics in the range ofO(∆t) un- der this approximation. Therefore, we can interpret TRMC (EF) as one of the approximation methods of the optimal backward dynamics.
In this thesis, we assumed that the number of time steps is fixed. This is not, however, always clear in advance in realistic problems. In the case of a realistic typhoon model, we must consider the case of passing through Tokyo between two discrete time steps. This problem is called the high-dimensional boundary crossing problem. To our best knowledge, a few research is known for the high-dimensional boundary crossing problem (see, e.g., [57, 58, 59]). Therefore, the interpolation method for our case must be developed in future work.
Appendix
A Time Reversal of Diffusions
In this section, we review the theory of time reversal for diffusion processes developed by Haussmann and Pardoux [9] and F¨ollmer [60]. For simplicity, we treat a one- dimensional system.
Let the dynamics of a stochastic processX ={Xu; 0≤u < T}on the probability space (Ω,F,P) is defined by
Xt=X0+ Z t
0
b(u, Xu)du+ Z t
0
σ(u, Xu)dWu,0≤t ≤T (A.1) where the integral is defined as the Ito integral andW ={Wu; 0≤u < T}is Brow- nian motion. This type of process is called “diffusion process” or “Ito process”. A diffusion process is a Markov process with continuous sample paths. The functions b and σ are called, respectively, the “drift coefficient” and “diffusion coefficient”.
Standard Brownian motion corresponds to a diffusion process with b(t, x) = 0 and σ(t, x) = 1.
Definition A.1 The Ito integral. We assume that Xt, Yt be an adapted stochastic process. LetΠ ={t0, t1, ..., tn}be a partition of [0, t]with0 = t0 < t1 <· · ·< tn=t, then define the sum
I(t) =
n−1
X
j=0
Ytj+1(Xtj+1−Xtj). (A.2) The stochastic process I(t) in Eq.(A.2) is the Ito integral. I(t) converges in proba- bility as the mesh ||Π|| of the partition tends to zero if the limit exists. In this case, the Ito integral is written as
Z t
Given a stochastic process X, we define the time-reversed process
X˜t =XT−t. (A.4)
Using Eq. (A.1), we can write XT−t =XT +
Z T−t T
b(u, Xu)du+ Z T−t
T
σ(u, Xu)dWu. (A.5) Eq.(A.5) can be written with time-reversed process ˜X as
X˜t= ˜X0− Z t
0
b(T −s,X˜s)ds− Z t
0
σ(T −s,X˜s)dWT−s, (A.6) where we make the substitution s = T − u. The stochastic integral Rt
0 σ(T − s,X˜s)dWT−s in Eq.(A.6) is defined as
n−1
X
i=0
σ(T −ti+1,X˜ti+1)(WT−ti−WT−ti+1), (A.7) where we choose partition points
0 = t0 < t1 <· · ·< tn=t. (A.8) We also define the time-reversed Brownian motion as the following:
W¯t=WT−t−WT. (A.9)
Lemma A.1 The Ito’s Lemma. let f(t, x) be a function for which the partial derivatives ft(t, x), fx(t, x), and fxx(t, x) are defined and continuous. Let X = {Xt; 0≤u≤T} be an Ito process defined by Eq.(A.1). Then, for every T ≥0,
f(T, XT) = f(0, X0) +
Z T 0
ft(t, Xt) +fx(t, Xt)b(t, Xt) + 1
2fxx(t, Xt)σ2(t, Xt)
dt +
Z T 0
fx(t, Xt)σ(t, Xt)dWt. (A.10)
In shorthand differential form,
df(t, Xt) =
ft(t, Xt) +fx(t, Xt)b(t, Xt) + 1
2fxx(t, Xt)σ2(t, Xt)
dt
+fx(t, Xt)σ(t, Xt)dWt. (A.11)
Applying Ito’s lemma to σ(t, Xt), Eq. (A.7) is written as
n−1
X
i=0
σ(T −ti+1,X˜ti+1)(WT−ti −WT−ti+1)
=−
n−1
X
i=0
σ(T −ti+1,X˜ti+1)( ¯Wti+1−W¯ti)
=−
n−1
X
i=0
σ(T −ti,X˜ti)( ¯Wti+1−W¯ti) +σ(T −ti,X˜ti)∂xσ(T −ti,X˜ti)∆ti, (A.12) where ∆ti =ti+1−ti. Taking the limit as n→ ∞, we get
Z t 0
σ(T −s,X˜s)dWT−s= Z t
0
σ(T −s,X˜s)dW¯s+ Z t
0
σ(T −s,X˜s)∂xσ(T −s,X˜s)ds.
(A.13) Using Eq. (A.13) and (A.6), we get
X˜t= ˜X0+ Z t
0
n−b(T −s,X˜s) +σ(T −s,X˜s)∂xσ(T −s,X˜s)o ds +
Z t 0
σ(T −s,X˜s)dW¯s (A.14)
It is important here to note that ˜W is not Brownian motion because E
hX˜t( ¯Wu−W¯t)i
=E h
XT−t(WT−u−WT−t)i
6= 0, (A.15) where 0≤t≤u≤T. To find the correct Brownian motion, we prove the following relation
E
G(Xt)
Wt−Wu+ Z t
u
∂x(p(s, Xs)σ(s, Xs)) p(s, Xs) ds
= 0, (A.16) whereG:R→R. See [61] for the detail. Then, we define ˜Wt as the following
W˜t= ¯Wt− Z T
T−t
∂x(p(s, Xs)σ(s, Xs))
p(s, Xs) ds, (A.17)
W˜t is the Brownian motion.
As Eq.(A.1) can be written in differential form
dXt=b(t, Xt)dt+σ(t, Xt)dWt (A.18)
, we can also write the exact backward dynamics in differential form as
dX˜t= ˜b(T −t,X˜t)dt+σ(T −t,X˜t)dW˜t (A.19)
˜b(t, x) = −b(t, x) +σ(t, x)∂xσ(t, x) +σ(t, x)∂x(p(t, x)σ(t, x))
p(t, x) . (A.20) This equation is an equation that strictly reverses time. We call this the exact backward dynamics here. We can simulate the exact backward dynamics using a numerical method such as the Euler method.
B Application to Options Portfolio Valuation
In this appendix, we introduce options portfolio valuation where TRMC can be effective as an application for finance. An option is a kind of derivative securities.
Derivative securities are securities whose value is based on underlying security price (e.g., stock price, bond price, etc.). For example, the simplest option, a European call option on an underlying security, gives the holder a right (not an obligation;
then, the name “option”) to buy the underlying security at a specified date for a specified price (this is called the strike price, we denote by K). There are different types of options. Here, we only deal with the call/put option, which gives the holder the right to buy/sell security. See, for example, [62] for further information about derivative securities and financial engineering.
Estimating the correct value for these options is an essential and challenging problem in the financial industry. The breakthrough was occurred by Black and Scholes (1973) [63], which introduced the no-arbitrage principle and pricing-formula for generic options. Generally speaking, we can estimate the right value of the option VT
VT = Z ∞
0
dUT payoff(UT)p(UT|U0)p(U0), (B.1) where T is the maturity time of the options, U = {Ut; 0≤t ≤T} is the stochastic process of an underlying security price,p(UT|U0) is the transition probability density fromU0toUT, and payoff(·) is a payoff function depending onUT, K, and the option types. the payoff of a call/put option are the following
payoff(u) =
max(u−K,0) (call option) max(K−u,0) (put option)
. (B.2)
In this appendix, we assume that there is one underlying security and the discretized dynamics of the price process U ={U0, U1, . . . , UN} follows a log-normal model
Ui+1 =rUi∆t+iUi√
∆t, (B.3)
where r is the annualized risk-free interest rate, N∆t = T, and the noise i is
Monte Carlo methods are popular computational methods to estimates Eq. (B.1), especially when the portfolio includes a lot of options (options portfolio), or the underlying space has a large dimensionality [64].
An important point in the practical financial risk management of an options portfolio is that the value of an option portfolio changes non-linearly. By starting a simulation from multiple initial conditions (e.g., multiple stock prices) and cal- culating the value of an options portfolio for each initial condition, we can grasp the non-linear risk. Then, it is possible to estimate how much loss in the value of a portfolio will occur when the financial market falls sharply. TRMC can be effi- cient in evaluating derivative portfolios with payoffs under ”limited conditions” as we explain later.
Here, we regard this ”limited condition” as the target region A and estimate an options portfolio value by applying TRMC. In the financial industry, it is common to calculate using a forward simulation, so we use FS as a baseline in this appendix.
We fix r = 0.01,∆t = 0.01, T = 1, K = 18000, σ = 0.2. We also assume that initial conditions (underlying security price) U0 is uniformly distributed in {u0; 15000≤u0 ≤25000}. The target regionAis{uT; 17500≤uT ≤18500}We set the number of Monte Carlo paths M to 106 and the number of time steps N to 100.
We can use the Black-Scholes formula to price call/put options respectively Vcall,T =N(d1)U0−N(d2)Ke−rT
Vput,T =−N(−d1)U0−N(−d2)Ke−rT, (B.4)
where d1 = log(UTK )
σ√
T , d2 = d1 −σ√
T and N(·) is the standard normal cumulative distribution function [62].
We also designed the payoff function shown in Fig. B.1. The non-zero region of this payoff function corresponds to the target region A in TRMC. Since the payoff function introduced here can be described as a combination of call/put options, we can use Black-Scholes formula to calculate the exact solution of this simulation. As Black-Scholes formula calculates the value at a specific underlying security price U0, we divided the initial distribution of underlying security price into bins (we set this to 40), and evaluated the options using the security price at the center of each
0 100 200 300 400 500
16000 17000 18000 19000 20000
Underlying security price
P a y off
Figure B.1: The shape of the payoff function, which we use here. The non-zero region of this payoff function corresponds to the target regionA in TRMC.
bin. Fig. B.2 shows the result of computational experiments for this simulation.
It shows that the value calculated using TRMC and the exact solution by Black- Scholes formula almost agree within the error bars. Fig. B.3 shows that TRMC is more efficient than FS in a wide range of underlying security prices. Here, we denote a standard error of TRMC to evaluate the computational efficiency byσs. We also denote the standard error of FS byσsF. Using these variables, as in the main text, we define a measure of the relative computational effciencyρ2 as
ρ2 = σsF
σs 2
τF τ ,
whereτ is the computational time in seconds of the simulation and τF is the com- putational time of FS in seconds. This efficiency is defined in the sense of the actual performance considering both the computational time and the variance of the resulting estimates.
There are two advantageous points in TRMC compared to using Black-Scholes
10 15 20 25
15000 17500 20000 22500 25000
Underlying security price
V alue
Backward Exact solution
Figure B.2: Comparison among TRMC and the exact solution by BS formula for options portfolio valuation. Error bars indicate approximate 1 standard error con- fidence intervals for TRMC.
2 3 4 5
15000 17500 20000 22500 25000
Underlying security price ρ
2Backward
Figure B.3: TRMC is more efficient than FS in a wide range of underlying security prices. ρ2 values in a wide range of underlying security prices.
obtain an analytical solution by Black-Scholes formula. The second point is that we can easily apply TRMC to high-dimensional systems compared to getting an analytical solution by Black-Scholes formula.
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