Numerical Methods for Pricing Exotic Options

Transcription

1 Numerical Methods for Pricing Exotic Options Dimitra Bampou Supervisor: Dr. Daniel Kuhn Second Marker: Professor Berç Rustem 18 June 2008

2 2 Numerical Methods for Pricing Exotic Options

3 0BAbstract 3 Abstract Derivative securities, when used correctly, can help investors increase their expected returns and minimize their exposure to risk. Options offer leverage and insurance for risk-averse investors. For the more risky investors, they can be ways of speculation. When an option is issued, we face the problem of determining the price of a product which depends on the performance of another security and on the same time we must make sure to eliminate arbitrage opportunities. Due to the nature of those contracts, over the past decades a lot of research has focused on finding accurate valuation models for options. Popular pricing methods such as Black-Scholes PDE have proven to be inefficient for pricing exotic options as it is impossible to express their price in an analytic solution In this project, we investigate two recently proposed valuation techniques. They approach the problem of pricing an option by approximating upper and lower bounds for the option price using semidefinite programming. We will explore the price dynamics of options and the efficiency of these methods for pricing European and Exotic options.

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5 1BAcknowledgements 5 Acknowledgements I would like to thank my supervisor, Dr. Daniel Kuhn, for his constant guidance and support during the project. His motivation and enthusiasm helped me develop a great interest in optimization and operations research. I would like to acknowledge Professor Berç Rustem, my second marker, for his constructive comments and suggestions. I would also like to thank my parents for being my inspiration. Finally I would like to thank my sister Elina and my friend George for their support all those years in London.

6 6 Numerical Methods for Pricing Exotic Options Table of Contents ABSTRACT 3 ACKNOWLEDGEMENTS 5 TABLE OF CONTENTS 6 INTRODUCTION Motivation Contributions 11 BACKGROUND Linear and Semi-definite Programming Probability and Measure Theory The problem of Moments and Optimization with Polynomials Mathematical Models for Asset Prices Financial Background Options Role of Options 26 BLACK-SCHOLES AND MONTE CARLO METHODS Black-Scholes Monte Carlo Method Geometric Brownian motion Ornstein-Uhlenbeck process Monte Carlo method for European Options Monte Carlo for Asian options Monte Carlo for Barrier options 33 BOUNDING OPTION PRICES BY SEMI DEFINITE PROGRAMMING Formulation of the problem for a European Call option Dual upper bound problem conversion to SDP Dual lower bound problem conversion to SDP Formulating the SDP for European Put options Dual upper bound problem conversion to SDP 43

7 59 2BTable of Contents Dual lower bound problem conversion to SDP Calculating the moments Conclusions 47 PRICING A CLASS OF EXOTIC OPTIONS VIA MOMENTS AND SDP RELAXATIONS Formulating the problem Asian Options Formulation Computing the moments of U European Options Formulation Computing the moments of U 5.4 Barrier Options Formulation Defining the moments 63 NUMERICAL RESULTS Software and Hardware Pricing a Class of Exotic Options via Moments and SDP relaxations European Call options Asian Call Options Bounding Option Prices by Semidefinite Programming 78 CONCLUSION Qualitative Evaluation Future Work 83 REFERENCES 84

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9 3BIntroduction 9 Chapter 1 Introduction 1.1 Motivation An option, put in simple terms, is a contract between two parties, giving one of the parties the right but not the obligation to purchase or to sell an asset in the future. For example, given a stock selling at 50 today, A and B agree that in one year from now, B will have the option to purchase that stock from A for 54 for exchange of a fee. B pays the fee to A today and on the expiration date, B will exercise his right only if the stock is selling on the stock markets at a higher price. This type of option is called a European Call option. The existence of options traces back thousands of years, in the Phoenicians and Ancient Greek civilizations. According to Aristotle (Luenberger 1998), Thales the Milesian, believed by observing the stars, that the olive harvest would be really good in the summer, so he rented in advance olive presses at a low price since demand was low during the winter. In the summer, his judgement was proved correct and by controlling the supply of the equipment while demand was high, he made profit. Nowadays, options are considered to be extremely popular financial instruments as investors find them a really attractive way of investing their money. Options are usually included in portfolios as ways of hedging and speculation. For example, an investor believing that the stock price of IBM will rise, will enter a contract such as the one described above, making profit by selling the stock after the expiration of the option, if of course his judgment is proved to be correct. Options can also result in spectacular losses. The IBM stock price may not rise after all and the investor will lose the fee he paid for the option. There are many standard types of options selling in the global financial markets, such as Europeans and Americans. Further, new types of options arise as investors try to create their preferred risk return profile for their portfolios. A special case of options called exotics has gain popularity. Each option is characterized by the way its payoff depends on price of the underlying asset. The main challenge regarding the options market is how to price them fairly to avoid arbitrage opportunities. Regarding the above example, what is the optimal price that A should charge B today for the option he is selling? Certainly, B would

10 10 Numerical Methods for Pricing Exotic Options not pay the same price for a similar option as the above with an exercise price of 70 instead of 54. Perhaps the most popular valuation model for options is the Black-Scholes PDE, proposed by Robert C. Merton. The model is based on the theory that markets are arbitrage free and assumes that the price of the underlying asset is characterized by a Geometric Brownian motion (an Ito process). This method is commonly used for pricing European options as there is an analytic solution for their price. On the other hand, in most cases it is almost impossible to express the Black-Scholes PDE in an analytic formula that would calculate the prices of exotic options. Another technique for pricing options is the binomial lattice model. In essence, it is a simplification of the Black-Scholes method as it considers the fluctuation of the price of the underlying asset in discrete time. Binomial lattices are easily implemented but can be computationally demanding. This model is typically used to determine the price of European and American options. Monte Carlo simulation is a numerical method for pricing options. It assumes that in order to value an option, we need to find the expected value of the price of the underlying asset on the expiration date. Since the price is a random variable, one possible way of finding its expected value is by simulation. A disadvantage of this method is that it can be computationally demanding and to obtain an accurate result for the price of a simple Vanilla option, one would need to generate 10 5 random numbers. On the other hand, this model can be adapted to price almost any type of option. In 2000, Bertsimas and Popescu proposed a semi definite programming approach for pricing European options. The method was later modified by Gotoh and Konno, who also proposed a cutting plane algorithm for solving the problem (Gotoh and Konno 2002). The objective of the method is to approximate upper and lower bounds, producing a tight set of possible values, for the price of a European option, given the first few moments of the risk neutral distribution of the price of the underlying asset, which can be modeled by any process (as long as we are able to calculate its moments). This is an advantage since market has shown that stock prices often do not behave as Geometric Brownian motion. Recently, Lasserre, Prieto-Rumeau and Zervos introduced a new technique for pricing exotic options (Lasserre, Prieto-Rumeau and Zervos 2006). Their technique is based on the work of Dawson which involves the use of moments to derive a solution for martingale problems. According to this method, one needs to write the problem of finding the price of an option as an infinite system of 1 st degree polynomials, whose variables are moments of certain measures. Thus one obtains a linear programming problem which cannot be solved because it is infinite. The objective is now to convert the above problem into a semidefinite

11 3BIntroduction 11 programming problem by applying finite dimensional relaxations to the moments of the above measures, thus creating moment and localizing matrices, which is a technique introduced by Lasserre in The advantage of this technique against the Black-Scholes model is that the distribution of the underlying asset can be modeled by many types of processes. Indeed in their paper, the authors have applied their method on European, Asian and Barrier options whose underlying asset can follow the GBM, the Ornstein-Uhlenbeck process or the standard square root process. 1.2 Contributions The key contributions of the project are: We investigate two recently proposed methods for pricing options. The comparative advantage of these methods against traditional pricing techniques such as Monte Carlo is that they try to approximate upper and lower bounds for the prices of the options by finding appropriate measures for the prices of the underlying assets and formulating problems involving some moments of those measures (chapters 4, 5). We investigate the problem of moments, where we are interested in finding the maximum/minimum point of a non-convex polynomial. The solution of this problem lies on the fact that by requiring certain necessary and sufficient moment conditions, we can convert it into a convex problem (chapter 2). We analyze the method proposed by Gotoh and Konno for pricing European call options and we show how we can adapt it in order to find upper and lower bounds for the prices of put options (chapter 4). We consider the method proposed by Lasserre, Prieto-Rumeau and Zervos for pricing Asian and Barrier options using the problem of moments. We present the adaptation for pricing European options (chapter 5). We study several fundamental pricing techniques and we explore the dynamics of asset prices in the financial world (chapters 2, 3). We provide implementations of the above techniques in Matlab and we analyze the results. We then investigate the advantages and disadvantages for these techniques based on assumptions they rely on, their complexity and how easily can be adapted for other options (chapters 6, 7.1).

12 12 Numerical Methods for Pricing Exotic Options Chapter 2 Background In this chapter, we explain the mathematical concepts and the financial theory and calculus needed in order to understand the methods for derivative pricing described in this report. We focus on semidefinite programming, probability and measure theory and stochastic differential equations. We then apply these concepts in the financial world. We assume no prior knowledge of finance. 2.1 Linear and Semi-definite Programming Linear programming (LP) is a technique used in optimization problems where the decision variables are linearly related. Linear programming is applied in decision making problems including the fields of engineering and finance. An LP problem consists of the decision variables, whose optimal values are what we need to approximate subject to some restrictions, referred to as constraints. The standard LP problem is defined as 0, R ; R R In this problem, defined as the primal, we are interested in finding the optimal value of such that our objective function is minimized. The feasible region, the convex hull, of variable is defined by the constraints and 0. An LP problem can be solved using the SIMPLEX algorithm, which walks along the edges of the polyhedron defined by the constraints and finds the optimal solution. We define the dual of the above problem, where is the dual variable, as 0 ;,

13 4BBackground 13 The solution of the dual is bounded from above by the primal solution,, and the main duality theory states that given a pair of primal and dual problems, if one of them has an optimal solution, so does the other, and the optimal value of the objective functions are the same,. A semi-definite programming problem (SDP) is an extension of a linear problem where the objective function is linear but the set of constraints is defined as a combination of positive semidefinite matrices. SDP are convex optimization problems since the constraints form a convex set and the objective is a linear (hence convex) function. To define the SPD problem formally, consider the set of symmetric matrices and, 0,. Our decision variable is. Then an SDP is defined as 0 The constraints of an SDP define a convex cone of positive semidefinite matrices in the set of symmetric matrices. The main difference between an LP and a SDP is that a SDP is not a linear problem. SDP has a wide area of application and in this report we will focus on its use as a solution of polynomial optimization problems. The dual of an SDP, with dual variable is defined as Y Y 0 The solution of the dual SDP problem is again bounded from above by the primal solution if both the primal and the dual have feasible solutions. Equality of the optimal primal and optimal dual solution holds if both are strictly feasible, i.e. if and are positive definite matrices. SDP problems can be solved using interior point methods and many open source solvers exist such as Sedumi and SDPA. 2.2 Probability and Measure Theory Given a set and a collection of subsets of then is algebra of subsets of if i) ii) If then the complement of,, which means is closed under complementation

14 14 Numerical Methods for Pricing Exotic Options iii) For any,, which means that is closed under finite union. Hence is closed under finite intersection since and by ii Additionally given,, if th en is called σ-algeb ra. Given a nonnegative, real valued function on a σ-algebra, is called a measure if for any, 1 : If 1 then is a probability measure and,, is a probability space. For example, if is a subset of then the smallest σ-algebra containing is,,,. Now consider the set of real numbers and, with. Consider also a subset of such that,. Then we can think of as an interval of and define the measure t o be the length of A. Given a subset of an algebra, a measure on is called finite if. Furthermore, we say that is countably additive on if the union of any finite or infinite collection of disjoint sets of belongs to. Also is called finitely additive on if it is finite and countably additive. Let be a finitely additive measure on the algebra. Then we have: i) 0 ii) for all, iii) Given, and, then For the proof, we refer the reader to (Ash and Doleans-Dade 2000) A finitely, nonnegative additive measure on σ-algebra is called σ-finite if for all, i) and ii),,1 Hence is bounded, both from above since sup : and from below since a measu re is nonnegative. Let,, where is a σ-algebra, and define to be a countably additive measure on. If, form an increasing sequence of sets with limit such that lim, then as. A Borel set (or simply ) of is defined as the smallest σ-algebra of subsets of containing all open intervals of. Since a Borel set is a σ-algebra (closed under complementation) it must contain all the closed intervals of.

15 4BBackground 15 Hence a Borel set contains all intervals of. Every subset of, i.e. any real number, belongs in the Borel set. One can think of a Borel set by starting with an interval on the set of real functions and add to the collection all the unions, intersections and complements of all the sets added in the collection. Given a function : and a set, we define. Then is called Borel if :. A Lebesgue-Stieltjes measure on is the measure on such that for each bounded interval (Ash and Doleans-Dade 2000). For a distribution function : is increasing, i.e. and right continuous, i.e.lim. Then we have, 0. If is a decreasing sequence of points then, 0. A random variable defined on the probability space,, is a Borel measure from to, hence :,, ). Informally, we can think of as a sample space and for an event, the measure is the probability that event will take place. The probability measure is defined as :, The distribution function : 0,1 of a random variable and is defined as : If the distribution function of a random variable is absolutely continuous on the set of real numbers, then is called an absolutely continuous random variable. That is, if there is a Borel measure such that,, In this case, we call the density function of. Furthermore is the Lebesgue-Stieltjes measure of. Let, 1 be random variables defined on,,. Then,, are independent if for all the Borel sets, 1

16 16 Numerical Methods for Pricing Exotic Options If, 1 are independent random variables as defined above and, 1 is the distribution function of each, then the distribution function of the random variable is defined by Define a set and a subset of. Then the indicator : 0,1 of is defined by 1, 0, We define the expectation of a random variable defined on the probability space,, by The moment about the mean, often referred to as the central moment is defined as. For 0, the 0-moment of a random variable is equal to 1. For 1, then the 1 moment is the expectation as defined above. We refer to the 2 central moment as the variance of, i.e., If, 1 are independent random variables defined on,, then When more than one random variable is related with an experiment, we need to refer to random vectors. An dimentional random vector defined on the probability space,, is a Borel measure from to. Hence : and each random variable is a Borel measure. Now the probability measure is defined as :, The distribution function : 0,1 or equivalently the joint distribution function of a random vector is given by, :, 1.

17 4BBackground 17 For an absolutely continuous random vector there is a Borel measure, which is the density function, such that,, The covariance of tw o random variables and is defined as, From the above, we can see than, The correlation coefficient of two random variables and is defined as,, If and are independent then, 0 hence, 0. When they are completely dependent then,, 1. Given a stochastic process, i.e. a sequence of random variables,, defined on the probability space,,, being a σ-algebra and, being sub σ-algebras of where each is measurable on respectively, we say that,, is a martingale relative to if the conditional expected value of given all, is equ al to th e expected value of, that is,, 1,2, Let, be a martingale and be an increasing sequence of σ-algebras of. A random variable 1,2,. is called a stopping time relative to if. A stopping time is a condition of whether to stop or continue a process. Let be the first time that a 6 appears when we roll a fair die and be the number of 6 s appeared until the th roll. We can define to be a stopping time such that 6, 6,

18 18 Numerical Methods for Pricing Exotic Options 2.3 The problem of Moments and Optimization with Polynomials The general problem of moments is defined as the problem where we want to derive the existence of a measure given some of its moments. In this section we give an introduction of the general problem of moments applied in finding extreme points of a non-convex polynomial and demonstrate how we can convert it into semi definite optimization problem through relaxations. We focus our analysis in unconstrained optimization problems of polynomials. For more details, we refer the reader to (J. B. Lasserre 2001). This section of the background is necessary in order to understand the method for pricing exotics in Chapter 5. Given a multiindex we define the moment of order of a Borel measure on as The sequence of moments of measure up to order is given by. Let and to be the set of all Borel measures with finite moments of all orders supported on. Given a real valued polynomial : we are interested in solving the problem The polynomial, where cannot be written as a sum of squares. Let be of degree. Hence we can write,, The b asis of the polynomial is given by 1,,,,,,,,,,,,,,,, The terms are the coefficient vectors of. The theory of moment states that we can convert the above optimization problem into an equivalent problem of the form y is the sequence of moments of µ

19 4BBackground 19 In order to express the constraint in mathematical terms, we need to introduce the concepts of moment and localizing matrices. According to (J. B. Lasserre 2001), given a sequence y α, we introduce the sequence,, which is obtained by ordering the sequence with the same indices as the basis of the polynomial given above. For instance, when 2 and 2 we ha ve The moment matrix y 0 0,y 10,y 01,y 20,y 11,y 02,y 30,y 21,,y 04 of is defined as 1,, 1, 1 1 1, y α, 1 y, y αβ β So to construct a moment matrix for 2 and 2, we put on the first column and row of the matrix all the first elements of defined above and then for each, entry, add the indices of the first elements of the th row and th column, i.e. y 00 y 10 y 01 y 20 y 11 y 02 y 10 y 20 y 11 y 30 y 21 y 12 y 01 y 11 y 02 y 21 y 12 y 03 y 20 y 30 y 21 y 40 y 31 y 22 y 11 y 21 y 12 y 31 y 22 y 13 y 02 y 12 y 03 y 2 y 13 y 04 If the elements of are moments of a measure defined on and is of order maximum then 0, because for all polynomials we have 2 0 To define the localizing matrix, consider the set, where a polynomial with coefficients in the same basis as 0 Now a localizing matrix, is given by,,, where, is the subscript o f in the entry 1,. For example, consider the moment matrix given above. The corresponding localizing matrix for the polynomial 1 is given by

20 20 Numerical Methods for Pricing Exotic Options y 00 y 10 y 10 y 20 y 01 y 11 y 20 y 30 y 11 y 21 y 02 y!2 y 10 y 10 y 20 y 30 y 11 y 21 y 30 y 40 y 21 y 31 y 12 y 22 y 01 y 10 y 11 y 21 y 02 y 12 y 21 y 31 y 12 y 22 y 03 y 13, y 20 y 30 y 30 y 40 y 21 y 31 y 40 y 50 y 31 y 41 y 22 y 32 y 11 y 21 y 21 y 31 y 12 y 22 y 31 y 41 y 22 y 32 y 13 y 23 y 02 y 12 y 12 y 22 y 03 y 13 y 22 y 32 y 13 y2 3 y 04 y 14 If the elements of are moments of a measure defined on and is of order maximum then, 0, because for all polynomials we have, 0 The conditions, 0 and 0 are necessary but not sufficient, meaning that even if both matrices are positive semidefinite at the solution, may not be the sequence of moments of. Returning now to the original problem, if we know that 0, then for polynomials,, 1,., 1. we can write Hence we can convert the original problem to a semi definite optimization problem 0, Mathematical Models for Asset Prices Given an index set, a stochastic process is a sequence of random variables defined on a probability space,,. Thinking the index set as time, we can model the price of a security using stochastic processes. We will start our discussion by taking the time as a discrete parameter followed by continuous time and derive 3 pricing models. Random Walks are special random functions of time (Luenberger 1998). Suppose we have a time period 0, and this period is divided into intervals. Then a Random Walk is defined as

21 4BBackground 21 0 ~0,1 The standardized normal random variables are mutually uncorrelated, meaning 0,. Furthermore, for any we have This difference is a random variable with the interesting properties i) For any, variables and are ii) mutually uncorrelated 0 iii) Random Walks are a discrete time process since time is divided into discrete time intervals. A Weiner process is a continuous time process, derived by taking the limit of a Random Walk as 0 Then a Weiner process is a process which satisfies the following properties: i) ~0, ii) iii), 0 For any, variables and are mutually uncorrelated An Ito process is a stochastic differential equation which takes the form,, The functions, and, are called the expected drift rate and the variance rate respectively and is a Weiner process.

22 22 Numerical Methods for Pricing Exotic Options Figure 1 :Random Walk Figure 2: Weiner Process Ito s lemma states that given an Ito process and a process,, then the following equation is satisfied, 1, 2, Proof. Using Taylor s expansion on and ignoring the terms whose order is higher than we have,,, 1 2,,, 1 2,,,, 1 2,,, Hence we have 1 2 lim lim,,, ,,,,, Now we can define the three models which we will assume that the underlying securities of the options follow. All of them are Ito processes with different expected drift and variance rates. Geometric Brownian motion is a standard Ito form for describing a stock price model given by the stochastic differential equation

23 4BBackground 23 The Ornstein-Uhlenbeck process which is used to model the price dynamics of interest rates is defined as The Cox-Ingersoll-Ross interest rate model is give n by the following SDE It is a one factor model (one source of market risk). The factor ensures mean reversion of interest rates towards the expected value with rate of adjustment. ensures that is always positive. The mean reverting property of stochastic processes is a tendency experienced by some processes to return over time to their long run expected value. For example, interest rates and implied vo latility exhibit this property. The infinitesimal generator of an n-dimentional Ito processes is an operator acting on the set of functions :, which are twice differentiable, have continuous second derivatives and the following lim it exist s fo r all lim fx 1 2 fx 2.5 Financial Background In this section we explain the notions of derivative securities related to this project, focusing on their payoff structures and their role in the financial system. A derivative is a contract, a security, whose payoff depends on the value of some other security, called the underlying asset. The underlying asset can be a stock, a currency or a tangible asset such as corn. Options are derivative securities giving one of the parties special rights, depending on the nature of the option. We refer to the writer as the party who is selling the option and the holder as the one who is purchasing the option. The price paid by the holder to the writer in exchange for those special rights is called the premium Options The basic types of options are the European Call and Put options. A European Call option gives its holder the right, but not the obligation, to purchase from the writer a prescribed asset for a prescribed price at a prescribed time in the future (Higham 2004). On the other hand, a European Put option gives its holder the right, but not the obligation, to sell to the writer a prescribed asset for a prescribed price at a prescribed time in the future (Higham 2004). Suppose we have an underlying asset currently trading at, where are the days elapsed since the issue of the option. The maturity date of the option, i.e. the date which the holder of the option has the right to exercise the option, is. The price of the

24 24 Numerical Methods for Pricing Exotic Options asset at maturity is and the interest rate is. The strike price of the option is the price at which the holder of the option can buy/sell the underlying asset from the writer. The Call option on the expiry day is worth 0 if the underlying asset is trading at a lower price than because the holder can buy the asset on the stock market cheaper. Contrary, if it is selling higher than (in the money option), the holder can buy the asset at and sell it again making profit. Hence the present value of a Euro pean Call option on the expiry day can be written as max,0 Similarly, the present value of a European Put option on the expiry day is max,0 The payoff diagrams for the holder of European Call (figure 3) and Put (figure 4) options at maturity are shown below. Figure 3 Figure 4 Figures 5 & 6 show the payoff structures for the writer of European Call and Put options. Figure 5 Figure 6

25 4BBackground 25 According to the theory of interest (the time value of money) a 1 received today is not worth the same as a 1 received in a year. For example, if someone gives you a 1 today, you can deposit it in a bank account paying 8% interest per year. At the end of the year, you will have in the account Furthermore, if the account earns interest on interest every year, i.e. has compound interest rate, then at the end of the second year you will have on your account Now if the compounding takes place every six months with an interest rate, which is the yearly rate or equivalently the nominal rate, for example, after six months of depositing 1, you will have a balance of 1. If the compounding takes place periods every year, after periods, the account grows by 1 Consider the case where gets bigger and bigger, hence compounding more frequently during a year. If we allow to go to infinity, then we refer to continuous compounding. Measuring time in years, i.e., the growth rate becomes lim 1 lim 1 Hence, under continuous compounding, if we deposit in a bank with a nominal interest rate, we will receive back in time. We refer to as the future value of, or is the present value of. Going back to options, for a Call option, the holder will receive max,0 on the expiration day. The term, called the discount factor, is added in order to find the present value of the payoff. An Asian option is an option whose payoff depends on the average price of the underlying asset during the period since the issue of the option until its expiry day. We will consider only fixed strike arithmetic Asian options. The present values of the payoffs of Asian Call and Put options on their maturity day are defined as max 1,0 max 1,0 In a down-and-out Barrier option, the writer specifies a barrier for the underlying asset, and in case the price S t of the asset drops below this barrier anytime before the maturity date of the option, i.e. during the period 0,, the option is worth nothing. In the case the price of the asset stays above the barrier

26 26 Numerical Methods for Pricing Exotic Options throughout the duration of the option, the option has a payoff structure similar to a European option. Thus, the present values of the payoffs of down-and-out Barrier Call and Put options on the maturity day are defined as 0, max,0, 0, max,0,, 0, 0, 0, Role of Options We can think of options as insurance. Looking at the profit diagram (figure 7) of a Call option, with premium 5 and strike price 50, the holder of such an option is only liable for the premium he has paid no matter how badly the underlying asset performs. Similarly, the maximum amount a holder of Put option (figure 8) can lose is the premium independently of how good the underlying performs. This is called downside protection. Figure 7

27 4BBackground 27 Figure 8 The most important feature of options is that an investor can buy a lot of price action with a limited amount of capital, while always enjoying limited exposure to risk (Gitman and Joehnk 2008). The value and hence the price of Call options is positively correlated with the price of the underlying asset and the price of Put options is inversely related with the price of the underlying security. Investors use options for speculation while minimizing the risk of their portfolio. Suppose an investor has reasons to believe that the price of a stock will rise. While there is always a risk that he is proved wrong, if he goes in the stock market and buys the stock he can potentially lose lots of money. Instead, if he chooses to buy a Call option on that stock, due to the downside protection offered by a Call option, he can only lose the premium he paid. Investors use options for hedging, which is the process of reducing the financial risks associated with investments by combining securities (Luenberger 1998). If an investor owns a stock and he fears the price of the stock will fall, he can reduce his risk and his losses and potentially make profits by buying a put option on that stock. As we saw, options play a very important role in financial investments and hence finding a way of pricing those securities is crucial. The factors that affect option prices are: i) Time to Maturity: buying an option which is in the money a day before the expiration date has more chances of being profitable than buying the same option a month before expiration.

28 28 Numerical Methods for Pricing Exotic Options ii) iii) iv) Price movements of the underlying security: as the price of a stock rises, the Call option gains in value. A Call option, whose underlying asset is falling, is losing value. Price volatility of the underlying security: the more volatile the price of the underlying security, the greater the risk of an option being out of money. Interest rates: as we discussed, the present value of a cash flow depends on the level of interest rates. Hence, option pricing models need to take into account those factors. Due to the importance of options, a lot of research is focused on finding the most accurate models. Monte Carlo simulations can be potentially applied to any type of option to determine an average price but the variance of the result is usually high, due to the probabilistic nature of the error estimates. There are techniques for variance reduction such as control variant which are out of the scope of this project. Instead, researchers are interested in deriving more accurate models each one reflecting the properties of each type of option.

29 5BBlack-Scholes and Monte Carlo Methods 29 Chapter 3 Black-Scholes and Monte Carlo Methods In this chapter we give the details of some standard methods for pricing European and exotic options. We present the Black-Scholes PDE and explain how we adapt Monte Carlo simulations to price exotics. 3.1 Black-Scholes Robert C. Merton proposed a mathematical model for pricing stock options in This model was named Black-Scholes (BS). Black-Scholes PDE is a method for pricing options in which the underlying stock is priced using the Black- Scholes model. The Black Scholes formula is the formula for pricing European Call and Put options using the Black-Scholes PDE. The BS model is based on the assumption that the stock price follows a Brownian motion, using the risk neutral probability. The Black-Scholes equation tries to estimate the price of the underlying security of an option as an Ito process. To do that, we construct a portfolio with two products that try to replicate the current behavior of the derivate. The portfolio consists of a risk-free asset, for example a Bond, of value at time and interest rate such that and a stock whose price follows a geometric Brownian motion Then the payoff function, of the derivative with the underlying security being the stock and measured at time must satisfy the following partial differential equation: 1 2 Proof. Applying Ito s lemma on we have 1 2 (3.1) The portfolio that replicates the derivative payoff function at time is given by

30 30 Numerical Methods for Pricing Exotic Options The coefficients and are selected in such a way so that replicates,. The instantaneous change in the value of the portfolio is thus (3.2) Since replicates,, the coefficients of and in (3.1) and (3.2) must be equal, hence 1, Replacing an in (3.2) and using the fact that, , 1 2 The problem with the Black-Scholes PDE is that it is almost impossible to express it in an analytic formula, especially for exotic options. Furthermore, Black-Scholes PDE model works when we are pricing a security under risk neutrality. The theory states that and an investor regards an investment with rate of return and a risky investment with expected rate of return as equally attractive (Higham 2004). The cumulative function of a standard normal distribution is given by 1 2, 0, 1, The analytic solution of the Black-Scholes PDE where we take,,, i.e. the payoff of a European Call option with strike price and exercise date is

31 5BBlack-Scholes and Monte Carlo Methods 31, ln 2 The pricing formula for a European put option uses and as defined above and is given by, 3.2 Monte Carlo Method The Monte Carlo is a famous algorithm for pricing European as well as exotic options hence it is used widely in the financial industry. Before we get into the details of the method, we need to find the analytic solutions of the stochastic differential equations of the Geometric Brownian motion and the Ornstein- Uhlenbeck process Geometric Brownian motion Suppose is the price of an asset at time and is the risk free interest rate. follows a Geometric Brownian motion given by the stochastic differential equation We apply Ito s lemma usi ng the process ln. Then 1 2 ln 1 2 ln Since is a Weiner process, we have with ~0,1. Hence Using the central limit theorem we see that is normally distributed with mean and variance.

32 32 Numerical Methods for Pricing Exotic Options Ornstein-Uhlenbeck process The derivation of an analytic solution for the Ornstein-Uhlenbeck process is similar to the Geometric Brownian motion. Given the price of an asset following the Ornstein-Uhlenbeck process we use Ito s lemma again with,. 1 2 Solving the differential equation we have Hence is normally distributed , Monte Carlo method for European Options In the Monte Carlo method we try to find an estimate for the expected value of an option. By taking random samples of the price of the underlying asset and then applying the value function of the option to each of these samples results in an unbiased estimate of the expected value of this option. In order to express the randomness for the price of the underlying asset, using either the Geometric Brownian motion or the Ornstein-Uhlenbeck process, we can take samples from the standard normal distribution and then calculate the resulting. The algorithm for approximating the value European options is then for =1...a large number, 10 ~10 Generate sample Calculate find expected value of, if,,,, 0 if,, 0,, Monte Carlo for Asian options Recall that the Asian option has the same payoff function as European options but instead of using the price of the asset at the exercise day, we use the mean of the path of the price during the life of the option. We now need to simulate the

33 5BBlack-Scholes and Monte Carlo Methods 33 price of the underlying asset to find its expected value and repeat this experiment 10 ~10 times in order to find a good approximation for the value of an Asian option. Hence the drawback of this method is that it is extremely computationally demanding thus inefficient. for =1...a large number, 10 ~10 for j=1...a large number, 10 ~10 Generate sample Calculate find expecte d value of, find expected value of all, if,,,, 0 if,, 0,, Monte Carlo for Barrier options The algorithm is similar to the one used for pricing Asian options. We need to add a constraint such that if, where is the barrier, the option is worth nothing. Here is an outline for the algorithm for =1...a large number, 10 ~10 for j=1...a large number, 10 ~10 Generate sample Calculate find minimum value of, if if,,,, 0 if,, 0,, find expected values of, and,

34 34 Numerical Methods for Pricing Exotic Options Chapter 4 Bounding Option Prices by Semi definite Programming In this section we focus on a numerical valuation technique for European options. The method was originally proposed by Bertsimas and Popescu and later modified by Gotoh and Konno, who also proposed a cutting plane algorithm for solving the problem. The objective of the method is to approximate the upper and lower bounds, producing a tight set of possible values, for a European Call option price, given the first n moments of the risk neutral distribution of the price of the underlying asset. The method can also be applied to European Put options. The approximation is found by solving two semi definite programs, one for the upper bound and one for the lower bound. 4.1 Formulation of the problem for a European Call option Suppose is the price of the underlying asset at time, is the number of days remaining until the expiry day of the option and is the strike price of a European Call option. Let also be the interest rate (risk free). Since the asset price at expiry day is not known, we need to use the expected value of under the probability measure, which is the risk neutral distribution of, to calculate the price of the option at time. max,0 To solve this problem, we would need to know the distribution of. Alternatively, we can approximate this distribution if we are given some of its moments. Let, 0, be the first moments of the probability measure. We can formulate the following linear programming problems in order to find upper and lower bounds on the price of the Call option. max 0 Upper Bound Problem max,0., min 0 Lower Bound Problem max,0., 0 0 0

35 6BBounding Option Prices by Semi definite Programming 35 To understand why these problems are linear, we need to explain the integration as a process of summation technique. An integral is in essence the area between a curve and the -axis. If we split the area under the curve into equal vertical rectangles, the sum of the areas of the rectangles is an estimation of the total area under the curve between two points The area of a rectangle is approximately. Hence the total area of all the rectangles between two points is From the above, it is clear, that the smaller the width of each rectangle, the greater the precision of the calculation of the area under the curve. Taking the limit as 0 the area of a rectangle becomes lim Hence the total area under the curve between points and is given by

36 36 Numerical Methods for Pricing Exotic Options We can now convert the linear problems defined above into two equivalent problems, replacing the integrals by summations max Upper Bound Problem max,0 0., min Lower Bound Problem max,0 0., The drawback of the above formulations is that the values of the underlying asset are infinite meaning there are an infinite number of objective variables hence the problems are unsolvable. The next step is to formulate the duals of the problem and investigate how we can make them finite. Let and define to be the dual variable, the dual problems are then Dual Upper Bound Problem min 0. max, 0, 0 Dual Lower Bound Problem max 0. max, 0, 0 By this formulation, we have managed to make the objective variables finite with the cost of having infinitely many constraints in each problem. To overcome this problem, Bertsimas and Popescu (Gotoh and Konno 2002) use the following propositions and convert the dual infinite linear problems into equivalent semi definite programming problems. Proposition 1: Given the polynomial, then 0, if and only if there exists a positive semidefinite matrix,, 0, such that, 0 2.,: Proof: Replacing the condition in the polynomial and taking into account that is positive semidefinite,,: 0 Proposition 2: Given the polynomial, then 0, if and only if there is a positive semidefinite matrix,, 0, such that

37 6BBounding Option Prices by Semi definite Programming 37 0,:,:, 0, 1 Proof: If the condition holds and using proposition 1 we have 0 0,,,,,, 0,,, We use which is always greater than or equal to zero for all. Proposition 3: Given the polynomial, 0, 0, iff there exists a positive semidefinite matrix,, 0, such that 0,:,:, 1, 0 Proof. Given the conditions defined above and using proposition 2, we have

38 38 Numerical Methods for Pricing Exotic Options, : 0 Proposition 4: Given the polynomial, 0,, if and only if there exists a positive semidefinite matrix,, 0, such that 0,:,:, 0, 1 Proof. Using the conditions above, the binomial theorem and proposition 2, we have,: 0 We use shifted by to ensure that,. Proposition 5: Given the polynomial, 0,, if and only if there exists a positive semidefinite matrix,, 0, such that 1 0,:,:, 1, 0 Proof. Using the conditions above, the binomial theorem and proposition 2, we have

39 6BBounding Option Prices by Semi definite Programming 39 1,: 0 We use which tends to shifted by to ensure that, Dual upper bound problem conversion to SDP Let. The constraints of the dual upper bound problem can be written as 0, 0, (4.1) ,, 2 (4.2) Using propositions 1 to 4 we will convert the infinite upper bound dual problem into an equivalent semi definite programming problem. Applying proposition 3 to (4.1) we obtain the following constraints 0,:,:, 1, 0 (4.3) (4.4) 0,, 0, (4.5) Applying proposition 4 to (4.2), which is the polynomial 0, 0 we have 0, 0,:, 1 (4.6) 0,, 0, (4.7) Furthermore, we split constraint (4.2) in 3 cases:

40 40 Numerical Methods for Pricing Exotic Options. For 0 by proposition 4,,: therefore 00 (4.8) 0 1. For 1 by proposition 4, 1,: therefore 1,:. For 2.. by proposition 4,,: (4.9), 2 (4.10) Hence we have converted the dual upper bound problem in the following semi definite problem min..,: 0, 1 0,:,:, 1, 0 1,:,:, 2 0, Dual lower bound problem conversion to SDP Let. The constraints of the dual lower bound problem can be written as

41 6BBounding Option Prices by Semi definite Programming 41 0, 0, (4.11) ,, 2 (4.12) Applying proposition 3 to (4.11) we obtain the following constraints 0,:,:, 1, 0 (4.13) (4.14) 0,, 0, (4.15) Applying proposition 4 to (4.12), which is the polynomial 0, 0, 0 we have 0,:, 1 (4.16) 0,, 0, (4.17) Furthermor e, we split constra int (4.12) in 3 cases:. For 0 by proposition 4,,: therefore 00 (4.18) 0 1. For 1 by proposition 4, 1,: therefore 1,:. For 2.. by proposition 4,,: (4.19), 2 (4.20) Hence we have converted the dual lower bound problem in the following semi definite problem

42 42 Numerical Methods for Pricing Exotic Options max..,: 0, 1 0,:,:, 1, 0 1,:,:, 2 0, Formulating the SDP for European Put options Suppose is the price of the underlying asset at time, is the number of days remaining until the expiry day of the option and is the strike price of a European Put option. Let also be the interest rate (risk free). We will use again for the same reasons stated in the previous section the expected value of under the probability measure to calculate the price of the option at time. max,0 Let, 0, be the first moments of the probability measure. We can formulate the following linear programming problems in order to find upper and lower bounds on the price of the Pall option. Upper Bound Problem max max,0 0., Lower Bound Problem min max,0 0., 0 0 0

43 6BBounding Option Prices by Semi definite Programming 43 The next step is to formulate the duals of the problems. Let and define to be the dual variable, the dual problems are then Dual Upper Bound Problem min 0. max,0, 0 Dual Lower Bound Problem min 0. max,0, Dual upper bound problem conversion to SDP Let. The constraints of the dual upper bound problem can be written as 0,, (4.21) ,, 2 (4.22) Using propositions 1 to 5 we will try to convert the infinite upper bound dual problem into an equivalent semi definite programming problem. Applying proposition 5 to (4.21) we obtain the following constraints 1 0,:,:, 1, 0 (4.23) (4.24) 0,, 0, (4.25) Applying proposition 4 on (4.22), which is the polynomial 0, 0, 0 we have 0,:, 1 (4.26) 0,, 0, (4.27)

44 44 Numerical Methods for Pricing Exotic Options Furthermore, we split constraint (4.22) in 3 cases:. For 0 by proposition 4,,: therefore 00 (4.28) 0 1. For 1 by proposition 4, 1,: therefore 1,:. For 2.. by proposition 4,,: (4.29), 2 (4.30) Hence we have converted the dual upper bound problem in the following semi definite problem min.. 1,: 0, 1 0,:,:, 1, 0 1,:,:, 2 0, 0

45 6BBounding Option Prices by Semi definite Programming Dual lower bound problem conversion to SDP Let. The constraints of the dual lower bound problem can be written as 0,, (4.31) ,, 2 (4.32) Applying proposition 5 to (4.31) we obtain the following constraints 1 0,:,:, 1, 0 (4.33) (4.34) 0,, 0, (4.35) Applying proposition 4 on (4.32), which is the polynomial 0, 0, 0 we have 0,:, 1 (4.36) 0,, 0, (4.37) Furthermor e, we split constra int (4.32) in 3 cases:. For 0 by proposition 4,,: therefore For 1 by proposition 4, 1,: therefore 1,:. For 2.. by proposition 4,,: (4.38) (4.39), 2 (4.40)

46 46 Numerical Methods for Pricing Exotic Options Hence we have converted the dual lower bound problem in the following semi definite problem max.. 1,: 0, 1 0,:,:, 1, 0 1,:,:, 2 0, Calculating the moments In chapter 3, we have shown that under the risk neutral probability the analytic solution of a Geometric Brownian motion is The expectation, i.e. the 1 st order moment of is thus Using the binomial theorem and the fact that 1 a lognormal random variable, the th central moment of, is given by where is

47 6BBounding Option Prices by Semi definite Programming Then Conclusions 1 We have presented the technique proposed by Gotoh and Konno for pricing European Call options and have shown the derivations of the Put options. Gotoh and Konno presented in the same paper a cutting plane algorithm that solves the above problems. They also compared their algorithm with the Black-Scholes formula. According to these, using only the first and second order moments, when the strike price is close to the current stock price, the value of the lower bound deteriorates compared to the B-S formula. The results from using up to the 4rth order moment of the distribution show that the values of the upper and lower bound are very near to the B-S formula when the strike price is close to the price of the underlying asset. In addition, they noted that the effects of using even higher order moments are really significant but in reality computing those moments is very difficult.