A Contingent Claims Analysis of the Pricing of Rights Issues with Discontinuous Diffusion

Total Page:16

File Type:pdf, Size:1020Kb

A Contingent Claims Analysis of the Pricing of Rights Issues with Discontinuous Diffusion J UNIVERSITY OF CAPE TOWN A CONTINGENT CLAIMS ANALYSIS OF THE PRICING OF RIGHTS ISSUES WITH DISCONTINUOUS DIFFUSION PROCESSES A dissertation submitted in partial satisfaction of the requirements for the Degree of Master of Commerce (by coursework and dissertation). University byof Cape Town Russel John Botha Cape Town December 1998 The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or non- commercial research purposes only. Published by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University of Cape Town ACKNOWLEDGEMENTS AND DECLARATION I, Russel John Botha, do hereby declare that this research report entitled : A Contingent Claims analysis of the Pricing of Rights Issues with Discontinuous Diffusion Processes is my own unaided work save to the extent stated in the acknowledgement. I certify that this work has not been submitted as a dissertation at any other university. I would like to express my sincere gratitude to the following perso.ns for their support in the compilation of this research report: • Professor Derek Botha of UCT for valuable guidance and support throughout; • Francois du Plessis of HSBC Simpson McKie for assistance in the initial stages of the thesis and data collection; • Michael Davidow of the Johannesburg Stock Exchange for current market statistics; • Professor Gewers of the Stellenbosch Graduate School of Business for the final data set obtained; • Praveen Kumar of Lehman Brothers (New York) for practical advice; • Professor Mike Page of the UCT Graduate School of Business for technical assistance. Russel John Botha ABSTRACT This research proposed to identify the most accurate method of pricing rights using option pricing models, including the Black Scholes model, the Cox constant elasticity of variance model and the Merton jump diffusion model, and to determine the set of input parameters that lead to the most optimal results. The empirical results indicated that on average all of the models are able to estimate the actual rights trading prices relatively well. Some models performed better than others and these findings were consistent with the original reasonings. The market was shown to not account for the effect of dilution. The best model prices were obtained when calculating volatility over a one year historical period that included the actual rights trading period. The hypothesis regarding trading volume showed that there is a significant impact of trading volume on the estimation of accurate option prices. The filter rule of rejecting rights prices below IO cents and 100 cents also improved the results thus showing a bias for lower priced rights to be incorrectly valued and possibly some inefficiency in this sector of the market. BRIEF CONTENTS PAGE 1. INTRODUCTION 2. THE illSTORY OF OPTION PRICING 11 3. THE DYNAMICS OF SHARE PRICE DIFFUSIONS IN CONTINUOUS TIME 19 4. THE BLACK-SCHOLES OPTION PRICING MODEL 30 5. SHARE PRICE VOLATILITY 50 6. CONSTANT ELASTICITY OF VARIANCE OPTION PRICING MODELS 67 7. SHARE PRICE DIFFUSION DISTRIBUTIONS 82 8. JUMP DIFFUSION OPTION PRICING MODELS 94 9. WARRANT PRICING AND RIGHTS ISSUES 112 1O.MARKET EFFICIENCY 123 I I .RESEARCH METHODOLOGY 135 12.RESULTS OF EMPIRICAL TESTING 162 13.CONCLUSION 184 14.BIBLIOGRAPHY 190 APPENDIX 1 - J.S.E. LISTING REQUIREMENTS FOR RIGHTS ISSUES 210 APPENDIX2 - CONVERGENCE OF THE MERTON MODEL INFINITE SERIES 217 APPENDIX3 - PROOF OF ITO'S LEMMA 219 APPENDIX4 - DATA SAMPLE 221 APPENDIX 5 - SAMPLE JUMP DIFFUSION PARAMETERS 226 APPENDIX6 - RESTRICTIONS ON RATIONAL OPTION PRICING 231 APPENDIX 7 - PROOF OF THE CONVERGENCE OF THE BINOMIAL MODEL TO THE BLACK SCHOLES MODEL 236 APPENDIX 8 - VISUAL BASIC ROUTINES WRIITEN 239 DETAILED CONTENTS PAGE 1. INTRODUCTION 1 1.1. INTRODUCTION TO CONTINGENT CLAIMS ANALYSIS 1.2. DEFINITION OF AN OPTION 1.3. RAISING CAPITAL IN THE MARKET AND THE LINK TO OPTION PRICING 1.4. ANALYSIS OF PRICING BIASES 1.5. MOTIVATION FOR THE RESEARCH 1.6. THE RESEARCH PROBLEM 1.7. RESEARCH METHODOLOGY 1.8. CONTRIBUTION TO KNOWLEDGE 1.9. STRUCTURE OF THE RESEARCH THESIS 2. THE IDSTORY OF OPTIONS AND OPTION PRICING 11 2.1. INTRODUCTION 2.2. THE ORIGINS OF OPTIONS TRADING 2.2.1. THE INTERNATIONAL ARENA 2.2.2. THE SOUTH AFRICAN BEGINNINGS 2.3. THE ORIGINS OF OPTION PRICING 3. DYNAMICS OF SHARE PRICE DIFFUSIONS IN CONTINUOUS TIME 19 3.1. INTRODUCTION 3.2. DEFINITIONS 3.3. TYPE I AND II OUTCOMES 3.4. TYPE III OUTCOMES II 4. THE BLACK-SCHOLES OPTION PRICING MODEL 30 4.1. INTRODUCTION 4.2. ARBITRAGE PRICING AND THE BLACK SCHOLES MODEL 4.3. BLACK SCHOLES MODEL ASSUMPTIONS 4.4. DERIVATION OF THE MODEL 4.5. SOLUTION TO THE BLACK SCHOLES PARTIAL DIFFERENTIAL EQUATION 4.6. THE BINOMIAL LATTICE MODEL OPTION PRICING MODEL 4.7. THE BINOMIAL OPTION PRICING FORMULA 4.8. A COMPARATIVE STATICS ANALYSIS OF THE BLACK SCHOLES PARAMETERS 5. SHARE PRICE VOLATILITY 50 5.1. INTRODUCTION TO VOLATILITY 5.2. RELATIONSHIP OF VOLATILITY TO SHARE PRICE 5.3. THE VOLATILITY SMILE 5.4. ESTIMATING STOCK VOLATILITY 5.5. IMPLICATIONS OF NON-CONSTANT VOLATILITY ON OPTION PRICING . 5.6. CONCLUSION 6. CONSTANT ELASTICITY OF VARIANCE OPTION PRICING MODEL 67 6.1 . INTRODUCTION 6.2. APPLICABILITY TO THE PRICING OF RIGHTS 6.3 . DEVELOPMENT OF THE MODEL 6.4. INTERPRETATION OF THE CEV FORMULA 6.5. TWO SPECIAL CASES OF THE CEV MODEL 6.5. l. SQUARE ROOT MODEL 6.5.2. ABSOLUTE MODEL 6.6. APPLICABILITY OF THE MODEL Ill 6.7. ESTIMATING THE CHARACTERISTIC PARAMETER AND RESULTS OF EMPIRICAL TESTING 6.8. CONCLUSION 7. SHARE PRICE DIFFUSION DISTRIBUTIONS 82 7.1. INTRODUCTION 7.2. THE SHARE PRICE DIFFUSION DEBATE AND EVIDENCE 7.3 . THE NECESSITY FOR A NEW THEORY 7.4. THE STABLE PARETIAN APPROACH 7.5. THE SUPPORTING EMPIRICAL EVIDENCE 7.6. THE LARGE PRICE CHANGE PHENOMENON EXPLAINED 7.7. IMPLICATIONS OF THE STABLE PARETIAN HYPOTHESIS ON THE BLACK SCHOLES MODEL 8. JUMP DIFFUSION OPTION PRICING MODELS 94 8.1. INTRODUCTION 8.2. THE JUMP DIFFUSION CONCEPT 8.3. DERIVATION OF THE MERTON JUMP DIFFUSION MODEL 8.3.1. THE STOCK PRICE DYNAMICS 8.3.2. THE OPTION PRICE DYNAMICS 8.4. DIVERSIFIABILITY OF THE JUMP RISK 8.5. MODIFICATIONS OF THE MERTON MODEL 8.6. ESTIMATING THE MODEL PARAMETERS 8.7. A COMPARISON TO THE BLACK SCHOLES MODEL 8.8. CONCLUSION 9. WARRANT PRICING AND RIGHTS ISSUES 112 9.1 INTRODUCTION 9.2 THE RELATIONSHIP BETWEEN WARRANTS AND OPTIONS 9.3 THE RELATIONSHIP BETWEEN RIGHTS AND WARRANTS 9.4 THE THEORY OF WARRANT PRICING IV 9.5 THE THEORY OF RIGHTS PRICING 9.6 EMPIRICAL EVIDENCE ON WARRANT PRICING 9.6.1 THE INTERNATIONAL EVIDENCE 9.6.2 THE SOUTH AFRICAN RIGHTS ISSUES MARKET 9.7 CONCLUSION 10. MARKET EFFICIENCY 123 10.1. INTRODUCTION 10.2. THE THEORY OF MARKET EFFICIENCY 10.3. THE THEORY OF OPTIONS MARKET EFFICIENCY TESTS 10.4. THE INTERNATIONAL EVIDENCE 10.5. THE SOUTH AFRICAN EVIDENCE 10.6. CONCLUSION 11. RESEARCH METHODOLOGY 135 11.1. OUTLINE TO METHODOLOGY 11.2. THE RESEARCH PROBLEM 11.3. THE RESEARCH SUB-PROBLEMS 11.3.1 . THE FIRST SUB-PROBLEM 11.3.2. THE SECOND SUB-PROBLEM 11.3.3. THE THIRD SUB-PROBLEM 11.3.4. THE FOURTH SUB-PROBLEM 11.4. THE HYPOTHESIS STATEMENTS 11.4.1. MAIN HYPOTHESIS STATEMENT 11.4.2. SUB-HYPOTHESIS STATEMENTS 11.5 . ASSUMPTIONS AND LIMITATIONS OF THE STUDY 11.6. THE NEED FOR THIS STUDY 11.7. BASIC RESEARCH METHODOLOGY 11.8. SAMPLE SELECTION 11.9. SAMPLE DESCRIPTIVE STATISTICS 11.9.1. VOLUME OF RIGHTS ISSUES 11 .9.2. STOCK PRICE ANALYSIS v 11.9.3. VOLATILITY ANALYSIS 11 .9.4. TRADING PERIOD ANALYSIS 11.10. INPUT PARAMETER ESTIMATION 11.10.1.UNDERLYING SHARE PRICE 11 .10.2.EXERCISE PRICE 11.10.3.RISK FREE RATE 11.10.4.TIME TO MATURITY 11.10.5.VOLATILITY 11.10.6.MERTON JUMP DIFFUSION PARAMETERS 11.11. VALUATION MODELS EMPLOYED 11.11.1.BLACK SCHOLES MODEL 11.11.2.CONSTANT ELASTICITY OF VARIANCE MODEL 11.11.3.MERTON JUMP DIFFUSION MODEL 11.12. STATISTICAL TESTING PROCEDURE 12. RESULTS OF TESTING AND ANALYSIS OF FINDINGS 162 12.1 . RESULT OF PRIMARY HYPOTHESIS TEST 12.2. TESTING OF UNADJUSTED MODELS 12.3. TESTING THE DIFFERENT VOLATILITY ESTIMATION INTERVALS 12.3.1. BLACK SCHOLES MODEL 12.3.2. SQUARE ROOT CEV MODEL 12.3.3. ABSOLUTE CEV MODEL 12.3.4. VOLATILITY SUMMARY 12.4. ADJUSTING FOR DILUTION 12.4.1 . TESTING OF DILUTION ADJUSTED MODELS 12.4.2. CONCLUSION ON DILUTION ADJUSTMENT 12.5. TRADING VOLUME ADJUSTMENTS 12.5.1. TESTING OF MODELS ADJUSTED FOR NIL VOLUME 12.5.2. CONCLUSION ON ADJUSTMENT FOR TRADING VOLUME 12.6. ADJUSTMENT FOR THE ABSOLUTE VALUE OF RIGHTS PRICES 12.6.1. FILTER RULE REJECTING RIGHTS BELOW 10 CENTS 12.6.2. FILTER RULE REJECTING RIGHTS BELOW 100 CENTS VI 12.6.3 . CONCLUSION ON ADJUSTMENT FOR ABSOLUTE VALUE OF RIGHTS PRICES 13. CONCLUSION 184 13 .1. THE RESEARCH PROBLEM 13 .2. THE RESEARCH FINDINGS 13.3. OVERALL SUMMARY OF FINDINGS 13.4. THE PROPOSED RIGHTS PRICING MODEL AND DIRECTION FOR FUTURE RESEARCH 14. BIBLIOGRAPHY 190 APPENDIX 1 - J.S.E. LISTING REQUIREMENTS FOR RIGHTS ISSUES APPENDIX2 - CONVERGENCE OF THE MERTON MODEL INFINITE SERIES APPENDIX3 - PROOF OF ITO'S LEMMA APPENDIX4 - DATA SAMPLE APPENDIXS - SAMPLE JUMP DIFFUSION PARAMETERS APPENDIX6 - RESTRICTIONS ON RATIONAL OPTION PRICING APPENDIX7 - PROOF OF THE CONVERGENCE OF THE BINOMIAL MODEL TO THE BLACK SCHOLES MODEL APPENDIX8 - VISUAL BASIC ROUTINES WRITTEN vii CHAPTER 1 - INTRODUCTION 1.1 INTRODUCTION TO CONTINGENT CLAIMS ANALYSIS Contingent claims analysis involves the valuation of claims whose payoffs are contingent on the value of one or more underlying assets.
Recommended publications
  • Trinomial Tree
    Trinomial Tree • Set up a trinomial approximation to the geometric Brownian motion dS=S = r dt + σ dW .a • The three stock prices at time ∆t are S, Su, and Sd, where ud = 1. • Impose the matching of mean and that of variance: 1 = pu + pm + pd; SM = (puu + pm + (pd=u)) S; 2 2 2 2 S V = pu(Su − SM) + pm(S − SM) + pd(Sd − SM) : aBoyle (1988). ⃝c 2013 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 599 • Above, M ≡ er∆t; 2 V ≡ M 2(eσ ∆t − 1); by Eqs. (21) on p. 154. ⃝c 2013 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 600 * - pu* Su * * pm- - j- S S * pd j j j- Sd - j ∆t ⃝c 2013 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 601 Trinomial Tree (concluded) • Use linear algebra to verify that ( ) u V + M 2 − M − (M − 1) p = ; u (u − 1) (u2 − 1) ( ) u2 V + M 2 − M − u3(M − 1) p = : d (u − 1) (u2 − 1) { In practice, we must also make sure the probabilities lie between 0 and 1. • Countless variations. ⃝c 2013 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 602 A Trinomial Tree p • Use u = eλσ ∆t, where λ ≥ 1 is a tunable parameter. • Then ( ) p r + σ2 ∆t ! 1 pu 2 + ; 2λ ( 2λσ) p 1 r − 2σ2 ∆t p ! − : d 2λ2 2λσ p • A nice choice for λ is π=2 .a aOmberg (1988). ⃝c 2013 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 603 Barrier Options Revisited • BOPM introduces a specification error by replacing the barrier with a nonidentical effective barrier.
    [Show full text]
  • A Fuzzy Real Option Model for Pricing Grid Compute Resources
    A Fuzzy Real Option Model for Pricing Grid Compute Resources by David Allenotor A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba in partial fulfillment of the requirements of the degree of DOCTOR OF PHILOSOPHY Department of Computer Science University of Manitoba Winnipeg Copyright ⃝c 2010 by David Allenotor Abstract Many of the grid compute resources (CPU cycles, network bandwidths, computing power, processor times, and software) exist as non-storable commodities, which we call grid compute commodities (gcc) and are distributed geographically across organizations. These organizations have dissimilar resource compositions and usage policies, which makes pricing grid resources and guaranteeing their availability a challenge. Several initiatives (Globus, Legion, Nimrod/G) have developed various frameworks for grid resource management. However, there has been a very little effort in pricing the resources. In this thesis, we propose financial option based model for pricing grid resources by devising three research threads: pricing the gcc as a problem of real option, modeling gcc spot price using a discrete time approach, and addressing uncertainty constraints in the provision of Quality of Service (QoS) using fuzzy logic. We used GridSim, a simulation tool for resource usage in a Grid to experiment and test our model. To further consolidate our model and validate our results, we analyzed usage traces from six real grids from across the world for which we priced a set of resources. We designed a Price Variant Function (PVF) in our model, which is a fuzzy value and its application attracts more patronage to a grid that has more resources to offer and also redirect patronage from a grid that is very busy to another grid.
    [Show full text]
  • Local Volatility Modelling
    LOCAL VOLATILITY MODELLING Roel van der Kamp July 13, 2009 A DISSERTATION SUBMITTED FOR THE DEGREE OF Master of Science in Applied Mathematics (Financial Engineering) I have to understand the world, you see. - Richard Philips Feynman Foreword This report serves as a dissertation for the completion of the Master programme in Applied Math- ematics (Financial Engineering) from the University of Twente. The project was devised from the collaboration of the University of Twente with Saen Options BV (during the course of the project Saen Options BV was integrated into AllOptions BV) at whose facilities the project was performed over a period of six months. This research project could not have been performed without the help of others. Most notably I would like to extend my gratitude towards my supervisors: Michel Vellekoop of the University of Twente, Julien Gosme of AllOptions BV and Fran¸coisMyburg of AllOptions BV. They provided me with the theoretical and practical knowledge necessary to perform this research. Their constant guidance, involvement and availability were an essential part of this project. My thanks goes out to Irakli Khomasuridze, who worked beside me for six months on his own project for the same degree. The many discussions I had with him greatly facilitated my progress and made the whole experience much more enjoyable. Finally I would like to thank AllOptions and their staff for making use of their facilities, getting access to their data and assisting me with all practical issues. RvdK Abstract Many different models exist that describe the behaviour of stock prices and are used to value op- tions on such an underlying asset.
    [Show full text]
  • Pricing Options Using Trinomial Trees
    Pricing Options Using Trinomial Trees Paul Clifford Yan Wang Oleg Zaboronski 30.12.2009 1 Introduction One of the first computational models used in the financial mathematics community was the binomial tree model. This model was popular for some time but in the last 15 years has become significantly outdated and is of little practical use. However it is still one of the first models students traditionally were taught. A more advanced model used for the project this semester, is the trinomial tree model. This improves upon the binomial model by allowing a stock price to move up, down or stay the same with certain probabilities, as shown in the diagram below. 2 Project description. The aim of the project is to apply the trinomial tree to the following problems: ² Pricing various European and American options ² Pricing barrier options ² Calculating the greeks More precisely, the students are asked to do the following: 1. Study the trinomial tree and its parameters, pu; pd; pm; u; d 2. Study the method to build the trinomial tree of share prices 3. Study the backward induction algorithms for option pricing on trees 4. Price various options such as European, American and barrier 1 5. Calculate the greeks using the tree Each of these topics will be explained very clearly in the following sections. Students are encouraged to ask questions during the lab sessions about certain terminology that they do not understand such as barrier options, hedging greeks and things of this nature. Answers to questions listed below should contain analysis of numerical results produced by the simulation.
    [Show full text]
  • On Trinomial Trees for One-Factor Short Rate Models∗
    On Trinomial Trees for One-Factor Short Rate Models∗ Markus Leippoldy Swiss Banking Institute, University of Zurich Zvi Wienerz School of Business Administration, Hebrew University of Jerusalem April 3, 2003 ∗Markus Leippold acknowledges the financial support of the Swiss National Science Foundation (NCCR FINRISK). Zvi Wiener acknowledges the financial support of the Krueger and Rosenberg funds at the He- brew University of Jerusalem. We welcome comments, including references to related papers we inadvertently overlooked. yCorrespondence Information: University of Zurich, ISB, Plattenstr. 14, 8032 Zurich, Switzerland; tel: +41 1-634-2951; fax: +41 1-634-4903; [email protected]. zCorrespondence Information: Hebrew University of Jerusalem, Mount Scopus, Jerusalem, 91905, Israel; tel: +972-2-588-3049; fax: +972-2-588-1341; [email protected]. On Trinomial Trees for One-Factor Short Rate Models ABSTRACT In this article we discuss the implementation of general one-factor short rate models with a trinomial tree. Taking the Hull-White model as a starting point, our contribution is threefold. First, we show how trees can be spanned using a set of general branching processes. Secondly, we improve Hull-White's procedure to calibrate the tree to bond prices by a much more efficient approach. This approach is applicable to a wide range of term structure models. Finally, we show how the tree can be adjusted to the volatility structure. The proposed approach leads to an efficient and flexible construction method for trinomial trees, which can be easily implemented and calibrated to both prices and volatilities. JEL Classification Codes: G13, C6. Key Words: Short Rate Models, Trinomial Trees, Forward Measure.
    [Show full text]
  • Volatility Curves of Incomplete Markets
    Master's thesis Volatility Curves of Incomplete Markets The Trinomial Option Pricing Model KATERYNA CHECHELNYTSKA Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2019 Thesis for the Degree of Master Science Volatility Curves of Incomplete Markets The Trinomial Option Pricing Model KATERYNA CHECHELNYTSKA Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg SE - 412 96 Gothenburg, Sweden Gothenburg, Sweden 2019 Volatility Curves of Incomplete Markets The Trinomial Asset Pricing Model KATERYNA CHECHELNYTSKA © KATERYNA CHECHELNYTSKA, 2019. Supervisor: Docent Simone Calogero, Department of Mathematical Sciences Examiner: Docent Patrik Albin, Department of Mathematical Sciences Master's Thesis 2019 Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg Telephone +46 31 772 1000 Typeset in LATEX Printed by Chalmers Reproservice Gothenburg, Sweden 2019 4 Volatility Curves of Incomplete Markets The Trinomial Option Pricing Model KATERYNA CHECHELNYTSKA Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg Abstract The graph of the implied volatility of call options as a function of the strike price is called volatility curve. If the options market were perfectly described by the Black-Scholes model, the implied volatility would be independent of the strike price and thus the volatility curve would be a flat horizontal line. However the volatility curve of real markets is often found to have recurrent convex shapes called volatility smile and volatility skew. The common approach to explain this phenomena is by assuming that the volatility of the underlying stock is a stochastic process (while in Black-Scholes it is assumed to be a deterministic constant).
    [Show full text]
  • Robust Option Pricing: the Uncertain Volatility Model
    Imperial College London Department of Mathematics Robust option pricing: the uncertain volatility model Supervisor: Dr. Pietro SIORPAES Author: Hicham EL JERRARI (CID: 01792391) A thesis submitted for the degree of MSc in Mathematics and Finance, 2019-2020 El Jerrari Robust option pricing Declaration The work contained in this thesis is my own work unless otherwise stated. Signature and date: 06/09/2020 Hicham EL JERRARI 1 El Jerrari Robust option pricing Acknowledgements I would like to thank my supervisor: Dr. Pietro SIORPAES for giving me the opportunity to work on this exciting subject. I am very grateful to him for his help throughout my project. Thanks to his advice, his supervision, as well as the discussions I had with him, this experience was very enriching for me. I would also like to thank Imperial College and more particularly the \Msc Mathemat- ics and Finance" for this year rich in learning, for their support and their dedication especially in this particular year of pandemic. On a personal level, I want to thank my parents for giving me the opportunity to study this master's year at Imperial College and to be present and support me throughout my studies. I also thank my brother and my two sisters for their encouragement. Last but not least, I dedicate this work to my grandmother for her moral support and a tribute to my late grandfather. 2 El Jerrari Robust option pricing Abstract This study presents the uncertain volatility model (UVM) which proposes a new approach for the pricing and hedging of derivatives by considering a band of spot's volatility as input.
    [Show full text]
  • The Hull-White Model • Hull and White (1987) Postulate the Following Model, Ds √ = R Dt + V Dw , S 1 Dv = Μvv Dt + Bv Dw2
    The Hull-White Model • Hull and White (1987) postulate the following model, dS p = r dt + V dW ; S 1 dV = µvV dt + bV dW2: • Above, V is the instantaneous variance. • They assume µv depends on V and t (but not S). ⃝c 2014 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 599 The SABR Model • Hagan, Kumar, Lesniewski, and Woodward (2002) postulate the following model, dS = r dt + SθV dW ; S 1 dV = bV dW2; for 0 ≤ θ ≤ 1. • A nice feature of this model is that the implied volatility surface has a compact approximate closed form. ⃝c 2014 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 600 The Hilliard-Schwartz Model • Hilliard and Schwartz (1996) postulate the following general model, dS = r dt + f(S)V a dW ; S 1 dV = µ(V ) dt + bV dW2; for some well-behaved function f(S) and constant a. ⃝c 2014 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 601 The Blacher Model • Blacher (2002) postulates the following model, dS [ ] = r dt + σ 1 + α(S − S ) + β(S − S )2 dW ; S 0 0 1 dσ = κ(θ − σ) dt + ϵσ dW2: • So the volatility σ follows a mean-reverting process to level θ. ⃝c 2014 Prof. Yuh-Dauh Lyuu, National Taiwan University Page 602 Heston's Stochastic-Volatility Model • Heston (1993) assumes the stock price follows dS p = (µ − q) dt + V dW1; (64) S p dV = κ(θ − V ) dt + σ V dW2: (65) { V is the instantaneous variance, which follows a square-root process. { dW1 and dW2 have correlation ρ.
    [Show full text]
  • Local and Stochastic Volatility Models: an Investigation Into the Pricing of Exotic Equity Options
    Local and Stochastic Volatility Models: An Investigation into the Pricing of Exotic Equity Options A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa, in ful¯llment of the requirements of the degree of Master of Science. Abstract The assumption of constant volatility as an input parameter into the Black-Scholes option pricing formula is deemed primitive and highly erroneous when one considers the terminal distribution of the log-returns of the underlying process. To account for the `fat tails' of the distribution, we consider both local and stochastic volatility option pricing models. Each class of models, the former being a special case of the latter, gives rise to a parametrization of the skew, which may or may not reflect the correct dynamics of the skew. We investigate a select few from each class and derive the results presented in the corresponding papers. We select one from each class, namely the implied trinomial tree (Derman, Kani & Chriss 1996) and the SABR model (Hagan, Kumar, Lesniewski & Woodward 2002), and calibrate to the implied skew for SAFEX futures. We also obtain prices for both vanilla and exotic equity index options and compare the two approaches. Lisa Majmin September 29, 2005 I declare that this is my own, unaided work. It is being submitted for the Degree of Master of Science to the University of the Witwatersrand, Johannesburg. It has not been submitted before for any degree or examination to any other University. (Signature) (Date) I would like to thank my supervisor, mentor and friend Dr Graeme West for his guidance, dedication and his persistent e®ort.
    [Show full text]
  • Numerical Methods in Finance
    NUMERICAL METHODS IN FINANCE Dr Antoine Jacquier wwwf.imperial.ac.uk/~ajacquie/ Department of Mathematics Imperial College London Spring Term 2016-2017 MSc in Mathematics and Finance This version: March 8, 2017 1 Contents 0.1 Some considerations on algorithms and convergence ................. 8 0.2 A concise introduction to arbitrage and option pricing ................ 10 0.2.1 European options ................................. 12 0.2.2 American options ................................. 13 0.2.3 Exotic options .................................. 13 1 Lattice (tree) methods 14 1.1 Binomial trees ...................................... 14 1.1.1 One-period binomial tree ............................ 14 1.1.2 Multi-period binomial tree ............................ 16 1.1.3 From discrete to continuous time ........................ 18 Kushner theorem for Markov chains ...................... 18 Reconciling the discrete and continuous time ................. 20 Examples of models ............................... 21 Convergence of CRR to the Black-Scholes model ............... 23 1.1.4 Adding dividends ................................. 29 1.2 Trinomial trees ...................................... 29 Boyle model .................................... 30 Kamrad-Ritchken model ............................. 31 1.3 Overture on stability analysis .............................. 33 2 Monte Carlo methods 35 2.1 Generating random variables .............................. 35 2.1.1 Uniform random number generator ....................... 35 Generating uniform random
    [Show full text]
  • Increasing the Accuracy of Option Pricing by Using Implied Parameters Related to Higher Moments
    Increasing the Accuracy of Option Pricing by Using Implied Parameters Related to Higher Moments Dasheng Ji and B. Wade Brorsen* Paper presented at the NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management Chicago, Illinois, April 17-18, 2000 Copyright 2000 by Dasheng Ji and B. Wade Brorsen. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. *Graduate research assistant ([email protected]) and Regents Professor (brorsen@ okway.okstate.edu), Department of Agricultural Economics, Oklahoma State University. Increasing the Accuracy of Option Pricing by Using Implied Parameters Related to Higher Moments The inaccuracy of the Black-Scholes formula arises from two aspects: the formula is for European options while most real option contracts are American; the formula is based on the assumption that underlying asset prices follow a lognormal distribution while in the real world asset prices cannot be described well by a lognormal distribution. We develop an American option pricing model that allows non-normality. The theoretical basis of the model is Gaussian quadrature and dynamic programming. The usual binomial and trinomial models are special cases. We use the Jarrow-Rudd formula and the relaxed binomial and trinomial tree models to imply the parameters related to the higher moments. The results demonstrate that using implied parameters related to the higher moments is more accurate than the restricted binomial and trinomial models that are commonly used. Keywords: option pricing, volatility smile, Edgeworth series, Gaussian Quadrature, relaxed binomial and trinomial tree models.
    [Show full text]
  • Trinomial Or Binomial: Accelerating American Put Option Price on Trees
    TRINOMIAL OR BINOMIAL: ACCELERATING AMERICAN PUT OPTION PRICE ON TREES JIUN HONG CHAN, MARK JOSHI, ROBERT TANG, AND CHAO YANG Abstract. We investigate the pricing performance of eight tri- nomial trees and one binomial tree, which was found to be most effective in an earlier paper, under twenty different implementation methodologies for pricing American put options. We conclude that the binomial tree, the Tian third order moment matching tree with truncation, Richardson extrapolation and smoothing performs bet- ter than the trinomial trees. 1. Introduction Various types of binomial and trinomial trees have been proposed in the literature for pricing financial derivatives. Since tree models are backward methods, they are effective for pricing American-type deriva- tives. Joshi (2007c) conducted an empirical investigation on the perfor- mance of eleven different binomial trees on American put options using twenty different combinations of acceleration techniques; he found that the best results were obtained by using the \Tian third order moment tree" (which we refer to as Tian3 binomial tree henceforth) together with truncation, smoothing and Richardson extrapolation. Here we per- form a similar analysis for trinomial trees and also compare the pricing results to the Tian3 binomial tree. We use the same four acceleration techniques implemented by Joshi (2007c): control variate due to Hull and White (1988), truncation due to Andicropoulos, Widdicks, Duck and Newton (2004), smoothing and Richardson extrapolation due to Broadie and Detemple (1996). These four techniques can be implemented independently or collectively and therefore yield 16 combinations of acceleration techniques one can use when pricing a derivative using trees. For detailed discussion and merits of these acceleration techniques, we refer reader to Joshi (2007c).
    [Show full text]