Research Article  Open Access
Winter Sinkala, Tembinkosi F. Nkalashe, "Lie Symmetry Analysis of a FirstOrder Feedback Model of Option Pricing", Advances in Mathematical Physics, vol. 2015, Article ID 361785, 9 pages, 2015. https://doi.org/10.1155/2015/361785
Lie Symmetry Analysis of a FirstOrder Feedback Model of Option Pricing
Abstract
A firstorder feedback model of option pricing consisting of a coupled system of two PDEs, a nonliner generalised BlackScholes equation and the classical BlackScholes equation, is studied using Lie symmetry analysis. This model arises as an extension of the classical BlackScholes model when liquidity is incorporated into the market. We compute the admitted Lie point symmetries of the system and construct an optimal system of the associated onedimensional subalgebras. We also construct some invariant solutions of the model.
1. Introduction
Extensions of the BlackScholes equation typically lead to nonlinear PDEs that are often tackled with numerical methods. In instances when analytic solutions exist, Lie symmetry methods [1–12] may be employed to construct the solutions. The model we consider in this paper arises as an extension of the BlackScholes equation when liquidity is incorporated into the market. We start by introducing the BlackScholes equation to provide a context. The BlackScholes equation has played a very significant role in the development of modern derivative asset analysis. Based on a small set of assumptions on the price behavior of the underlying asset, the BlackScholes equation allows investors to calculate the “fair” price of a derivative security. In its simplest form, the BlackScholes equation requires the estimation of only one parameter that cannot be observed in the market, namely, the market volatility of the underlying asset price, which is assumed to be constant. Another assumption of the BlackScholes model is that the market is perfectly liquid. However, increases in market volatility of asset prices have been observed in recent years and it seems they are caused by the extensive usage of the BlackScholes model and the associated hedging strategies for pricing derivative securities [13]. This observed feedback effect of BlackScholes pricing on the underlying’s price and consequently on the price of the derivatives has led to extensions to the BlackScholes model aimed at accounting for this phenomenon [13–16]. In this paper we consider one such extension, the firstorder feedback model [17].
2. The FirstOrder Feedback Model
In the classical BlackScholes model the price process of the underlying asset is assumed to be governed by a geometric Brownian motion:where is the price of the underlying stock, and (assumed to be nonnegative constants) are the drift and volatility, respectively, and is a standard Brownian motion. To incorporate liquidity into the BlackScholes we modify the underlying asset price process. We introduce a forcing term, , dependent on the stock price and time so that in place of (1) we havewhere is an arbitrary function that models the nature of price impact and liquidity [17].
Applying Itô’s formula on the function , we obtain which when substituted into (2) leads toBy simply squaring both sides of (4) and applying the usual rules, and [18], we obtainSubstituting (5) into (4) and rearranging, we arrive at a stochastic process analogous to (1):where Under this setting, where the stock price follows the modified stochastic process (6), it is deduced via standard arguments that , the derivative price, satisfies the generalised BlackScholes equation (see Duffie [19] for more details):where is the riskfree interest rate, assumed to be a positive constant. When (8) is used as a model for the value of a European call option, for example, then at expiry time we have thatwhere is the strike price.
Consistent with standard BlackScholes arguments, the drift of the modified process does not appear in the option pricing PDE. In the context of markets with finite elasticity, can be interpreted as the number of extra shares that should be held due to some deterministic hedging/trading strategy and hence specifies the number of shares needed to be bought or sold at time and price due to such a strategy. If we are interested in the price impact due to delta hedging then we can identify as the delta of the option being replicated; that is,This leads to the question of what strategy the hedgers are assumed to follow. Either they use the BlackScholes option delta to hedge, ignoring price impact, or they try to incorporate price impact into the hedging strategy by using the modified delta. The first case is called the firstorder feedback, and in this case (8) becomes a linear PDE:where is the solution to the standard BlackScholes equation:Both (11) and (12) are subject to the same terminal condition (9), with being replaced by in the case of (12).
The other scenario, in which the hedger is assumed to be aware of the feedback effect and so would change the hedging strategy accordingly, corresponds to the case when . In this case the trading strategy adopted has to be found as part of the problem. This is called the full feedback and leads to the nonlinear PDE:We will apply Lie symmetry analysis to the firstorder feedback model (11), which is coupled with the standard BlackScholes equation (12). Investigation of solutions of differential equations via Lie symmetry analysis has been done to many problems in financial mathematics, for example, [20–24]. The primary objective of the present study is to determine general solutions (as invariant solutions) of the firstorder feedback model (11).
3. The Admitted Lie Point Symmetries
For convenience, we let , , and represent , , and , respectively. Further, we use the standard substitution representing time to expiry of the option (and rename as ), so that (11) and (12) are represented byrespectively, with and . The constants , , and are to be considered nonzero.
Remark 1. System (15)(16) is a generalisation somewhat of (11) and (12). The symmetries reported in (19) and Tables 1 and 2 are based on this generalised system.

We will apply the standard machinery of symmetry analysis to find particular solutions of (15)(16). Let us consider the oneparameter Lie group of transformations in the space given bywhere is the group parameter, with infinitesimal generatorFor (17) to be admitted by (15)(16), one requires that the transformation leaves invariant the set of solutions of system (15)(16). Applying program Lie [25], we obtain that the Lie algebra of infinitesimal generators of system (15)(16) is spanned by the seven operators:Table 1 is the commutator table for the operators (19).
4. Determination of Invariant Solutions
An invariant solution is a solution that is mapped into itself by a group transformation. Such solutions are very important in that the majority of exact solutions that have important real world applications are reportedly invariant solutions [3, p. 29]. The algorithm for constructing invariant solutions of a given system of differential equations using admitted symmetries is well documented in many standard books on Lie symmetry analysis [1, 5, 7–9]. Each of the operators in (19) is of the formand generates a onedimensional subalgebra of the algebra spanned by (19). We consider invariant solutions associated with . Variables in which (15)(16) reduces to a coupled system of ordinary differential equations are determined by the invariants of , which are obtained by solving the quasilinear PDE:where is some function of the independent and dependent variables. The general solution of (21) iswhere is an arbitrary function andare functionally independent solutions of the characteristic systemThe invariants , , and in (23) are now used to determine new variables in which (15)(16) should be written so that it is reduced to a secondorder system of ODEs, the solution of which leads to a family of invariant solutions of (15)(16) associated with .
4.1. Invariant Solutions of an Arbitrary Element of
In principle every element from can be used to construct an invariant solution of system (15)(16). Take, for example, taken asin which case the infinitesimal coefficients areThe invariants of (25) are obtained as solutions of the characteristic equation (24), with the infinitesimals given in (26). We obtainThe functional form of the invariant solution is now constructed from these invariants. Taking the solution in the form we obtain that invariant solutions of (15)(16) arising from (25) have the formwhere and are arbitrary functions. When and as prescribed in (29) are substituted in (15)(16) we obtain the following pair of ODEs:These equations are easily solved as linear firstorder ODEs, and we obtainwhere and are arbitrary constants. Therefore, invariant solutions of (15)(16) arising from (25) are of the form where
4.2. Optimal System of OneDimensional Subalgebras
How every onedimensional subalgebra of the algebra may be used to find a family of invariant solutions of (15)(16) has just been illustrated. The exercise of finding all such families from all possible elements of is reduced to that of finding families of invariant solutions associated with only a small number of inequivalent symmetries, called an optimal system of onedimensional subalgebras [6, 8]. Two elements of are equivalent if the family of invariant solutions associated with one element can be transformed into a family of invariant solutions associated with the other by one of the Lie point symmetries of . We set out to construct an optimal system of onedimensional subalgebras of .
Following Olver’s approach [8] we start by constructing the adjoint presentation of the Lie group generated by (19) on its Lie algebra. Each of the basis symmetries (19) generates an adjoint representation defined by the Lie seriesThe meaning of (34) is that the generator is equivalent to under the Lie group generated by . All the adjoint actions for the operators (19) are given in Table 2, with the th entry indicating .
We construct the optimal system via the naive approach of taking a general element and subjecting it to various adjoint transformations to simplify it as much as possible [8]. Given a nonzero vectorwe endeavour to simplify as many of the coefficients as possible through judicious applications of adjoint maps to . We deal with particular cases depending on which of the coefficients are nonzero.
4.3.
Scaling away from (35), since , we can write (35) equivalently asIf we now act on (36) by we obtainWe can make the coefficient of vanish by setting With this choice of , we obtainSimilarly in (39) is simplified further by using with We obtainFurther simplification of is achieved with followed by , with and . We obtain the following simplifications: No further simplification is possible through the action of adjoint maps. This means that every onedimensional subalgebra generated by a general element with is equivalent to the subalgebra spanned byThe remaining subalgebras are spanned by vectors of the form (35) with .
4.4.
In this case the general element can be written in the formThe simplification of (44) is achieved as follows:where Note that none of the ’s in (47) can be eliminated by adjoint transformations, but we can scale away the coefficient of to obtain
4.5.
The general element has the form and is simplified as follows:
4.6.
Simplification of the general elementthrough the action of adjoint maps is not possible in this case!
4.7.
In this case, the general element is simplified through the adjoint map with , . The resulting operator is then divided by . We obtain
4.8.
For , we can only scale away the coefficient :We have therefore constructed an optimal system, (54), of onedimensional subalgebras for the firstorder feedback model (15)(16):
5. Invariant Solutions of Elements of the Optimal System
In this section we construct invariant solutions associated with elements of the constructed optimal system. We follow the procedure outlined and illustrated in Section 4.
5.1. ,
Solving the characteristic equation, (24), corresponding to we obtain the invariants from which we deduce the form of the invariant solution,where is any solution to the BlackScholes equation, (16), and is an arbitrary function. Taking, for example, the simple solutionof (16), (15) reduces to the ordinary differential equation:the solution of which is where is an arbitrary constant. Hence one family of invariant solutions of system (15)(16) arising from have the form
Remark 2. We remark here that in place of (57) one could use any solution of (16). One could, for example, use an invariant solution of (16) such aswhich arises from the following symmetry of (15):
5.2. ,
Invariant solutions of system (15)(16) arising from in this case are found to be of the form Substituting (63) in system (15)(16) leads to the following set of ordinary differential equations:Solving (64) for , we obtainwhere and are arbitrary constants. The solution of (65) taking into account the (66) completes the process. It is however not easy to solve (65) analytically.
5.3.
In this case dictates that and that solve (15)(16) take the formSubstituting (67) into system (15)(16), we obtain the following pair of coupled ODEs:Equation (68) is a linear ODE and is solved to givewhere and are arbitrary constants. Equation (69) is to be solved for .
5.4. ,
Invariant solutions arising from this element of the optimal system are such that and have the formswhere and are arbitrary functions , and Substituting (71) into system (15)(16), we obtain the following coupled system of ODEs:where Equation (73) is a linear secondorder ODE with constant coefficients. The solution iswhere and are arbitrary constants. This leaves (74) to be solved for .
Remark 3. We remark that (65), (69), and (74) are variable coefficient and linear secondorder ODEs, which are of the formThe analysis of such equations can be found in many standard books on ordinary differential equations such as [26, 27]. Also numerical methods are available for solution of differential equations of the form (77).
5.5. Interpretation of the Invariant Solutions
Analytic solutions of the firstorder feedback problem obtained as group invariant solutions in this study have various uses. They may be used to benchmark numerical schemes developed for solution of the firstorder feedback problem. They may also be used to differentiate between types of auxiliary conditions which lead to qualitatively different forms of option prices. Perhaps more importantly, the solutions can be used to provide insight into valuation dynamics of the firstorder feedback model. A phenomenon of particular interest which can be investigated using the invariant solutions is the relationship between the value of the option and the liquidity parameter . However, not all the invariant solutions generated have direct relevance to the context of the firstorder feedback problem. Among the solutions constructed, we identify two solutions (corresponding to and ) that depict an important feature of the firstorder feedback model. In Figure 1 we illustrate the option value in these cases for the liquidity parameters , , , and , with time to maturity , riskfree rate , and volatility . There are other free parameters in the solution that ought to be assigned suitable values. The graphs in the two cases considered were generated with the help of Mathematica’s NDSolve command [28]. In both cases the option value exhibits the important feature whereby as is increased, the option value is eroded monotonically.
(a)
(b)
6. Concluding Remarks
In this paper we have used Lie symmetry analysis to study a nonlinear equation that arises as a generalisation of the BlackScholes equation when liquidity is incorporated into the market. The generalised BlackScholes equation is called a firstorder feedback model and is coupled with the standard BlackScholes equation as a result of the assumption that a hedger holds the number of stocks dictated by the standard BlackScholes equation delta rather than the delta from the modified option price in the market with liquidity. We have exploited the Lie point symmetries admitted by the coupled system to construct particular solutions that represent option prices in the modified BlackScholes market.
As explained in Section 2, an improvement to the firstorder feedback model is the full feedback (13), a fully nonlinear PDE. Existence and uniqueness of the solution to (13) are established by Frey [14], who has shown that options in such a market can be perfectly replicated, an attestation to the completeness of the market. Unfortunately (13) is poorly endowed with Lie point symmetries and, therefore, does not lend itself to any interesting analysis via Lie point symmetries. Perhaps analysis of this equation would benefit from the use of more general symmetries.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The authors thank the Directorate of Research Development of Walter Sisulu University for the continued financial support and anonymous reviewers who made valuable comments that improved the paper.
References
 G. W. Bluman and S. Kumei, Symmetries and Differential Equations, Springer, New York, NY, USA, 1989. View at: Publisher Site  MathSciNet
 N. H. Ibragimov, Ed., CRC Handbook of Lie Group Analysis of Differential Equations, vol. 1, CRC Press, Boca Raton, Fla, USA, 1994.
 N. H. Ibragimov, Ed., CRC Handbook of Lie Group Analysis of Differential Equations, vol. 2, CRC Press, Boca Raton, Fla, USA, 1995.
 N. H. Ibragimov, Ed., CRC Handbook of Lie Group Analysis of Differential Equations, vol. 3, CRC Press, Boca Raton, Fla, USA, 1996.
 B. J. Cantwell, Introduction to Symmetry Analysis, Cambridge University Press, 2002. View at: MathSciNet
 P. E. Hydon, Symmetry Methods for Differential Equations: A Beginner's Guide, Cambridge University Press, New York, NY, USA, 2000. View at: Publisher Site  MathSciNet
 N. H. Ibragimov, Introduction to Modern Group Analysis, Ufa, Tay, Scotland, 2000.
 P. J. Olver, Applications of Lie Groups to Differential Equations, vol. 107 of Graduate Texts in Mathematics, Springer, New York, NY, USA, 2nd edition, 1993. View at: Publisher Site  MathSciNet
 L. V. Ovsiannikov, Group Analysis of Differential Equations, Academic Press, New York, NY, USA, 1982. View at: MathSciNet
 G. Baumann, Symmetry Analysis of Differential Equations with Mathematica, Springer, New York, NY, USA, 2000. View at: Publisher Site  MathSciNet
 F. Schwarz, “Symmetries of differential equations: from Sophus Lie to computer algebra,” SIAM Review, vol. 30, no. 3, pp. 450–481, 1988. View at: Publisher Site  Google Scholar  MathSciNet
 H. Stephani, Differential Equations: Their Solution Using Symmetries, Cambridge University Press, Cambridge, New York, NY, USA, 1989. View at: MathSciNet
 K. R. Sircar and G. Papanicolaou, “General BlackScholes models accounting for increased market volatility from hedging strategies,” Applied Mathematical Finance, vol. 5, no. 1, pp. 45–82, 1998. View at: Publisher Site  Google Scholar
 R. Frey, “Perfect option replication for a large trader,” Finance and Stochastics, vol. 2, pp. 115–142, 1998. View at: Google Scholar
 R. Frey and U. Polte, “Nonlinear BlackScholes equations in finance: associated control problems and properties of solutions,” SIAM Journal on Control and Optimization, vol. 49, no. 1, pp. 185–204, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 P. J. Schonbucher and P. Wilmott, “The feedback effect of hedging in illiquid markets,” SIAM Journal on Applied Mathematics, vol. 61, no. 1, pp. 232–272, 2000. View at: Publisher Site  Google Scholar  MathSciNet
 K. J. Glover, The analysis of PDEs arising in nonlinear and nonstandard option pricing [Ph.D. thesis], School of Mathematics, University of Manchester, 2008.
 B. Øksendal, Stochastic Differential Equations: An introduction with Applications, Springer, New York, NY, USA, 1995. View at: Publisher Site  MathSciNet
 D. Duffie, Dynamic Asset Pricing Theory, Princeton University Press, Boston, Mass, USA, 2001.
 N. C. Caister, J. G. O'Hara, and K. S. Govinder, “Solving the Asian option PDE using Lie symmetry methods,” International Journal of Theoretical and Applied Finance, vol. 13, no. 8, pp. 1265–1277, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 R. K. Gazizov and N. H. Ibragimov, “Lie symmetry analysis of differential equations in finance,” Nonlinear Dynamics, vol. 17, no. 4, pp. 387–407, 1998. View at: Publisher Site  Google Scholar  MathSciNet
 V. Naicker, K. Andriopoulos, and P. G. Leach, “Symmetry reductions of a HamiltonJacobiBellman equation arising in financial mathematics,” Journal of Nonlinear Mathematical Physics, vol. 12, no. 2, pp. 268–283, 2005. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 W. Sinkala, P. G. L. Leach, and J. G. O'Hara, “Zerocoupon bond prices in the vasicek and CIR models: their computation as groupinvariant solutions,” Mathematical Methods in the Applied Sciences, vol. 31, no. 6, pp. 665–678, 2008. View at: Publisher Site  Google Scholar  MathSciNet
 W. Sinkala, “Two ways to solve, using Lie group analysis, the fundamental valuation equation in the doublesquareroot model of the term structure,” Communications in Nonlinear Science and Numerical Simulation, vol. 16, no. 1, pp. 56–62, 2011. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. K. Head, “LIE, a PC program for Lie analysis of differential equations,” Computer Physics Communications, vol. 77, no. 2, pp. 241–248, 1993. View at: Publisher Site  Google Scholar  MathSciNet
 E. L. Ince, Ordinary Differential Equations, Dover, New York, NY, USA, 1956.
 D. Zwillinger, Handbook of Differential Equations, Academic Press, New York, NY, USA, 1998.
 Wolfram Research, Mathematica, Version 8.0, Wolfram Research, Champaign, Ill, USA, 2010.
Copyright
Copyright © 2015 Winter Sinkala and Tembinkosi F. Nkalashe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.