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A novel way to speed up calculation of American Style Options pricing Bhooma Lalitha Shivakumar Adlai Stevenson High School Grade 10 Shivakumar Table of Contents HYPOTHESIS ............................................................................................................................................................. 3 REVIEW OF THE LITERATURE ........................................................................................................................... 4 MATERIALS ............................................................................................................................................................. 14 METHODS OF PROCEDURE ................................................................................................................................ 15 NOT SURE HOW THIS SHOULD BE DONE. HOW DO I RECORD METHODS OF PROCEDURE FOR MATHEMATICAL MODEL APPLICATION? .................................................................................................... 15 CONCLUSIONS ........................................................................................................................................................ 16 REFERENCE LIST .................................................................................................................................................. 17 1 Shivakumar Acknowledgements I would like to thank the Science Fair team, including Mrs. Ragusa and Mrs. Palffy, for helping me out with the paper and guiding in my research. I would also like to thank them for making the WikiSpace available to facilitate the entire process. My thanks also go to Mr. XXX of the Mathematics department for answering my questions on my approach and clarifying my doubts. Lastly, I would like to thank my father for reviewing the write up and helping me with testing the program. Thank you all! 2 Shivakumar Purpose The purpose of this experiment was to determine if slight changes to the Binomial model could be used to speed up the pricing of American style options, which can be exercised at any time. The binomial model breaks down the time to expiration into potentially a very large number of time intervals, or steps making it useful for American style options. Hypothesis Changing the Binomial model, by skipping select nodes, to calculate options pricing will improve the speed of the calculation by 20% on a standard personal computer. 3 Shivakumar Review of the Literature The material wealth of a nation depends on the productive capacity of its economy. The economy in turn entails the goods and services that can be provided to its citizens and those living in that nation. This wealth, in turn, depends on the real assets, which include land, building, machines, etc. Financial assets on the other hand, do not directly represent a nation’s wealth. They, however, contribute indirectly by separating the ownership from management of firms and enterprises and facilitate the transfer of funds to enterprises with attractive investment opportunities. Real assets generate income, whereas financial assets define the allocation of this income or wealth among investors, who collectively are the owner of the enterprise. Investment, as per the Oxford dictionary, means an act or process of investing money for profit. However, many invest not only with money but with time, devotion, energy, etc. However, the end goal of investment is reaping a reward at a specific time in the future. Financial assets such as stocks and bonds are claims to the income generated by real assets or claims on the income from the government. A relatively recent, but extremely important class of financial assets is derivative securities or derivatives. Like the name suggests derivatives’ price is derived from the price of other, underlying securities. Derivatives are also called “contingent claims” because their payoffs is dependent or contingent upon the price of other securities. Because of this property, derivatives are used as a powerful tool to hedge, whereby one invests to reduce the overall risk of a set of financial assets, and speculation. 4 Shivakumar An option, as the name suggests, gives the holder of the option to buy or sell an asset for a specified price on a specified date for European style options or on or before a specific expiration date for US style options. A CALL option gives its holder the right to purchase an asset for a specified – previously determined – price called the EXERCISE or STRIKE price on or before a specific date depending on whether the option is a European or American style. Likewise, a PUT option gives the holder the right to sell an asset for a specified – previously determined – price. The purchase price of the option itself is called the PREMIUM. This PREMIUM is the compensation the purchaser of the CALL must pay for the right to exercise the option. Holder of a CALL option makes money only when the market value of the asset to be purchased as part of the options is higher than the STRIKE price. Likewise the holder of a PUT option makes money only when the market value of the asset to be sold as part of the option is lower than the STRIKE price. And there is no obligation to buy or sell the asset. One can simply let the options expire without exercising it on or before the expiration date. An option is in the money if the exercise price would produce a profit for its holder. For a CALL, the exercise price should be lower than the market price and for a PUT, the exercise price must be higher than the market price. An option is out of the money when exercising a CALL or a PUT produces a loss. An option is at the money when exercising the options breaks even, i.e., the asset price and the exercise price are equal. In 1973, the trading of standardized options contracts on a national exchanged started. The Chicago Board of Options Exchange or CBOE for short began listing CALL options. These were very successful and the volume of trade – number of contracts traded – increased rapidly 5 Shivakumar from 1973 to the late 1980s. Today options are traded in several exchanges worldwide. Options are now written on common stock, stock indexes, foreign exchange, commodities including agricultural and metals and interest rate futures. An American style option allows its holder to sell (if a PUT) or buy (if a CALL) the underlying asset on or before the expiration date. European options allow for exercise only on the expiration date. Since an American option offers more flexibility, they are generally more valuable than their European counterpart. There are some exceptions to this rule. Prime among them are the foreign currency and stock index options, which though traded on the CBOE, follow the European style (Zvi Bodi, Alex Kane, Alan J. Marcus, 1999). Options as an investment tool, offers many advantages over more traditional stocks or bonds. Cost efficiency is a major advantage of using stock option as in investment tool. The investor only pays for the options and not the underlying asset, which could cost a great deal more. Since there is less money involved, risk is greatly reduced. As an investment tool it provides variety to one’s portfolio. However, there are some notable disadvantages too. Firstly, the trading volume is much lower than stocks or bonds, lowering the liquidity of options. Options usually entail higher commissions, although these days options and stocks tend to incur similar charges. Unlike stocks or bonds, options are complicated and not readily available to entry level investors. Unlike stocks or bonds, options are priced differently based on the underlying assets. The story of options prices really begins with the French mathematician Louis Bachelier, who derived a closed formula for the pricing of standard CALLs and PUTs in his 1900 PhD thesis dissertation. His primary assumption was that stock prices follow an arithmetic Brownian 6 Shivakumar motion, where one can imagine rapid movements about the origin. In contrast to what is used today, returns are normal as opposed to lognormal. Louis Bachelier showed that for a non-dividend paying stock and zero interest rate, the price of a European style option should be as follows: c(S, T) = SN (S – K) – KN (S – K) – σ √T n(S – K) (σ √T) Where S is the stock spot price (price at which a stock can be bought or sold at a specified time and place), K is the STRIKE price, σ the volatility of the stock price (the instantaneous standard deviation of the stock price), T the time to the options maturity, N the cumulative normal density function and n the probability density function. However, the Bachelier formula above ignores any discounting and assumes that stock prices can be negative and as such not suitable for stock prices. Sprenkle, who in 1961, first started to adapt the approach of Bachelier to non-negative prices by assuming lognormal returns. Investors risk averseness was another factor he assumed and modified the above formula to the following: C(S,T) = eρT SN(d1) – (1 – A) KN (d2) Where d1 = 1 / σ√T [ ln (S/K) + (ρ + σ2 / 2)T ] , d2 = d1 - σ√T and ρ is the average rate of growth of the stock price and A is the degree of risk aversion. However, there were parameters to estimate such as degree of risk aversion and average growth of stock price and as such this pricing formula did not receive much attention. Later, Boness (1964) improved the formula by 7 Shivakumar accounting for the time value of money through discounting of the terminal stock price, using expected rate of return of the stock. The formula was then changed to C(S,T) = SN(d1) – Ke-ρT N(d2) With the component definitions remaining the same (Boness A.J.., 1973). Although the above approaches provided formulae close to the now used Black Scholes formula, it was the groundbreaking work of Black, Scholes and Merton that the option price was explicitly connected to hedging strategy. In 1973, Black Scholes was to realize that the expected return of the option price should be the risk free rate and that by holding a certain amount of stock, referred to as delta, the option position could be completely hedged (Black, F. and Scholes, M., (1973). Compared to the previous works, the Black Scholes formula had distinct advantages over the previously formulae. It had an explicit hedging strategy for the replication of the CALL, which only depends on the volatility of the stock price and other observable quantities like risk free rate, time to maturity of the option, its strike and spot price. It also had a universal price in that despite the investor risk aversion the price of the option should be the same for all investors. And above all the formula was easy to use with volatility being the only not easy to calculate parameter (Black, F., 1989). The famous formula is as follows: C(S,T) = SN(d1) – Ke-rT N(d2) 8 Shivakumar Where d1 = 1 / σ√T [ ln (S/K) + (r + σ2 / 2)T ] and d2 = d1 - σ√T. Like many academic breakthroughs, it took a while for the formula to be acknowledged and Black, Scholes and Merton were awarded the Nobel Prize in 1997. Despite its widespread use, the Black Scholes formula has many limitations associated with the assumptions it makes. These assumptions can be considered as non-real life assumptions about the market and how the market behaves. The first assumption that volatility is a constant over time has been challenged and empirically proved to be incorrect. While volatility – a measure of how much a stock can move in the near term – is relatively constant in very short term, it never is constant in the medium to long term. Large price changes are generally followed by large price changes and vice versa, leading to a property called volatility clustering. The Black Scholes formula also assumes that the price of the underlying stock moves up or down with equal probability. This is not true as the price of a given stock is determined by many economic factors that cannot be assigned the same probability. And it is important to note that the price of stock at time t+1 is independent from the price of stock at t or t+2. More importantly, there may not be a single source of uncertainty or risk driving two assets, even if one is derived from the other. Third assumption is reasonable and assumes that the returns of log normally distributed underlying stock prices are normally distributed. Although reasonable in the real world, it does not fit observed data accurately. Asset returns, unlike log normal, have finite variance and heavy tails. As the time scale over which the returns are calculate increases, the distribution of the asset 9 Shivakumar prices look more like normal distribution with heavy tails. This is despite the fact that the autocorrelation of asset prices is often insignificant (Rama, Cont, Peter et. al, 2003). The Black Scholes formula uses the risk-free rate to represent the interest rate and assumes it to be constant. However, in real life there is no such thing as risk-free and even the US Government treasury bills 30-day rate, which is supposed to be credible enough, can change in times of volatility. In the real world, most firms pay dividends to their stockholders. The basic BlackScholes model assumed that the underlying stock does not pay any dividends during the option’s life. This assumption was corrected for dividends and a workaround was added. The workaround subtracts the discounted value of a future dividend from the underlying stock price. The Black-Scholes model assumes that there is no fee or commission for buying or selling options and stocks and there are no barriers to trading. We all know that there are nontrivial fees for buying and selling any derivative. The Black-Scholes model is limited to the European-style options, which can be exercised only on expiration date of the option. The American-style options can be exercised at any time during leading up to the expiration date, making the American-style option more valuable due to this flexibility. The assumption that the financial markets are perfectly liquid and it is possible to buy or sell any amount of stock or options at any time is not plausible. Investors are limited by the amount of money they can invest, policies of the company they work and the wish of sellers to sell. In addition, volume of trade also depends on market volatility at a given time. Moreover, it 10 Shivakumar is not possible to sell a fraction of an option. Options, generally comes in bundles of one hundred shares. The above assumptions, which have limitations, relate to the fundamental aspects of the financial market. In order to make the model more accurate and fitting, it is necessary to develop models that take into consideration assumptions not addressed or fully addressed by the BlackScholes model. Many models, which are variants of the Black-Scholes model, have been proposed over time, with each trying to mimic the market characteristics fully. Each and every aspect of the financial market cannot be mimicked and cannot be considered in a single model. It is not possible to mathematically model every factor affecting the price of a financial security. This is a limitation and most advanced models only attempt to capture most of the aspects. Also, the Black-Scholes model’s basis is Brownian motion, which itself is affected by scaling, timechange properties and other factors (Teneng, D, 2011). The original Black-Scholes option pricing model assumes that stock returns follow Brownian motion. Stock returns, however, differ from this assumption in at least three important ways. These assumptions were briefly described above. Firstly, stock prices jump leading to nonnormal returns. Secondly, return volatilities vary over time. This variation is primarily due to conjecture and does not follow a pattern. Finally, returns and the volatilities are correlated. The correlation is generally negative for stocks. In order to price assets, in the case of options underlying assets, and manage risks it is important to consider the stochastic process that governs the asset dynamics. Although Brownian motion is a good model to follow, it has its shortcomings. The Time-Change Lévy process addresses these by using a continuous time stochastic process with stationary independent 11 Shivakumar increments. This model also captures the stochastic volatility by using stochastic time change to the process itself (Peter Carr, Liuren Wu, 2002). Considering the European style option pricing, which is modeled on Brownian motion implies constant drift and volatility. In addition, stock prices are considered to be independent of stock price history. We know that investors look at the stock price history before investing. In order to better model real-life conditions, drift and volatility were tied to stock price history using delayed responses and finite different approximation for an infinite dimensional BlackScholes equation (Mou-Hsiung Chang, Tao Pang, Moustapha Pemy, 2009). The Black-Scholes model assumes that in a perfectly competitive, liquid (ability to buy or sell any amount of stock) market there exist no opportunities to earn a risk-free profit. This can be considered as no opportunities for arbitrage. Although all of the assumptions gave been tweaked in some way to make the model more meaningful, some assumptions are easier to tweak than others. The assumption of stock prices following Brownian motion is easier to tweak than the assumption of perfectly liquid markets (Samuel W. Malone, 2002). It has also been shown that the Binomial option pricing model converges to the BlackScholes model for European style options and some American style options. The Binomial option pricing model is essentially a tree construction with branches that indicates the possible value of the underlying stock and the corresponding option value. This tree is constructed on the assumption that the underlying stock can go up or down by a factor that is related to the length of time and the volatility of the stock (Simon Benninga and Zvi Wiener, 1997). 12 Shivakumar An improved version of the Binomial option pricing model is the Trinomial option pricing model. Unlike, the Binomial model which considers underlying stock price going up or down, the Trinomial model includes a third option where the stock price remains unchanged. Trinomial trees also afford the advantage of preserving the Binomial trees within them. Binomial model suffers from speed and for increased accuracy, where more nodes have to be created then speed does become an issue. For some types of options, inaccuracies creep in until a large number of time intervals are used. Trinomial model is faster than Binomial and is better equipped to handle exotic options – more complex than vanilla or commonly traded options (Paul Clifford, Oleg Zaboronski, 2008). 13 Shivakumar Materials 1 calculator for data analysis. Computer hardware and software to record, develop, analyze, print data charts, and graphs. 14 Shivakumar Methods of Procedure Not sure how this should be done. How do I record methods of procedure for mathematical model application? 15 Shivakumar Conclusions 16 Shivakumar Reference List Zvi Bodie, Alex Kane, Alan J. Marcus (1999). Investments (Fourth Edition). Irwin McGraw-Hill Boness A.J.., 1973, Elements of a Theory of Stock Option Value. Journal of Political Economy, 72 (April) 163-75 Black, F., Scholes, M., (1973), The Pricing of Options and Corporate Liabilities, Journal of Political Economy, Vol. 81, pp. 637-654. Black, F., (1989), How We Came Up with the Option Formula, The Journal of Portfolio Management, Vol. 15, pp. 4-8.Austad, S.N. (1997). Why we age. John Wiley & Sons, Inc. Espen Gaarder Haug, Nassim Nicholas Taleb (2008), Why We Have Never Used the BlackScholes-Merton Option Pricing Formula. Joss, Dick (2011), A fresh look at the two of the embedded Black Scholes assumptions, Investment Symposium, 2011, Society of Actuaries. Gencay, R. and Salih A., (2003), Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures, ANNALS OF ECONOMICS AND FINANCE 4, 73–101 (2003). Gillula, Alex (2008), Analysis of hedging strategies using the Black Scholes framework, Dept. of Mathematics, Washington University, St. Louis. Fouque, J. P., PapaniColaou, G., Sircar R. (2000), From the implied volatility skew to a robust correction to the Black Scholes American option prices. Teneng, D (2011), Limitations of the Black Scholes model, International Research Journal of Finance and Economics, ISSN 1450-2887 Issue 68 (2011). Jacquinot P., Sukhomlim N., (2010), A direct formulation of implied volatility in the Black Scholes model, Journal of Economics and International Finance Vol. 2(6), pp. 95-101, June 2010. 17 Shivakumar Lamy J., (2008), Testing Geometric Brownian motion: An analytical look at the Black Scholes Option pricing model, Dept. of Economics, University of Maryland College Park. Rama, Cont, Peter, Tankov, 2003. “Financial Modelling with Jump processes” Chapman Hall/CRC Press, 2003. Peter Carr,Liuren Wu, 2002. “Time change Levy processes and option pricing” [Online], August 2002, accessed 20. 11. 2010, http://www.math.nyu.edu/research/carrp/papers/pdf/jfetchgepaper.pdf Stephen P Lalley, n.d. ”Levy Processes, Stable Processes and Subordinator's”, [Online] accessed 10. 01. 2011, http://galton.uchicago.edu/~lalley/Courses/385/LevyProcesses.pdf. Mou-Hsiung Chang, Tao Pang, Moustapha Pemy, 2009 “An Approximation Scheme for BlackScholes Equations with Delays”, AMS 2000 subject classi_cations: primary 90A09, 60H30; secondary 60G44, 90A16 Samuel W. Malone, 2002, “Alternative price processes for Black Scholes: Empirical evidence and theory”, NSF VIGRE grant number DMS-9983320. Simon Benninga and Zvi Wiener, 1997, “Binomial Option Pricing, the Black-Scholes Option Pricing Formula, and Exotic Options”, Mathematica in Education and Research Vol. 6 No. 4 1997. Paul Clifford, Oleg Zaboronski, 2008, “Option Pricing Model using Trinomial tree”, [online] accessed 11.23.2011, University of Warwick, United Kingdom. http://www2.warwick.ac.uk/fac/sci/maths/people/staff/oleg_zaboronski/fm/trinomial_tree_2010_ kevin.pdf 18