Pricing Options Using Trinomial Lattice Method
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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. -
Up to EUR 3,500,000.00 7% Fixed Rate Bonds Due 6 April 2026 ISIN
Up to EUR 3,500,000.00 7% Fixed Rate Bonds due 6 April 2026 ISIN IT0005440976 Terms and Conditions Executed by EPizza S.p.A. 4126-6190-7500.7 This Terms and Conditions are dated 6 April 2021. EPizza S.p.A., a company limited by shares incorporated in Italy as a società per azioni, whose registered office is at Piazza Castello n. 19, 20123 Milan, Italy, enrolled with the companies’ register of Milan-Monza-Brianza- Lodi under No. and fiscal code No. 08950850969, VAT No. 08950850969 (the “Issuer”). *** The issue of up to EUR 3,500,000.00 (three million and five hundred thousand /00) 7% (seven per cent.) fixed rate bonds due 6 April 2026 (the “Bonds”) was authorised by the Board of Directors of the Issuer, by exercising the powers conferred to it by the Articles (as defined below), through a resolution passed on 26 March 2021. The Bonds shall be issued and held subject to and with the benefit of the provisions of this Terms and Conditions. All such provisions shall be binding on the Issuer, the Bondholders (and their successors in title) and all Persons claiming through or under them and shall endure for the benefit of the Bondholders (and their successors in title). The Bondholders (and their successors in title) are deemed to have notice of all the provisions of this Terms and Conditions and the Articles. Copies of each of the Articles and this Terms and Conditions are available for inspection during normal business hours at the registered office for the time being of the Issuer being, as at the date of this Terms and Conditions, at Piazza Castello n. -
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. -
Futures and Options Workbook
EEXAMININGXAMINING FUTURES AND OPTIONS TABLE OF 130 Grain Exchange Building 400 South 4th Street Minneapolis, MN 55415 www.mgex.com [email protected] 800.827.4746 612.321.7101 Fax: 612.339.1155 Acknowledgements We express our appreciation to those who generously gave their time and effort in reviewing this publication. MGEX members and member firm personnel DePaul University Professor Jin Choi Southern Illinois University Associate Professor Dwight R. Sanders National Futures Association (Glossary of Terms) INTRODUCTION: THE POWER OF CHOICE 2 SECTION I: HISTORY History of MGEX 3 SECTION II: THE FUTURES MARKET Futures Contracts 4 The Participants 4 Exchange Services 5 TEST Sections I & II 6 Answers Sections I & II 7 SECTION III: HEDGING AND THE BASIS The Basis 8 Short Hedge Example 9 Long Hedge Example 9 TEST Section III 10 Answers Section III 12 SECTION IV: THE POWER OF OPTIONS Definitions 13 Options and Futures Comparison Diagram 14 Option Prices 15 Intrinsic Value 15 Time Value 15 Time Value Cap Diagram 15 Options Classifications 16 Options Exercise 16 F CONTENTS Deltas 16 Examples 16 TEST Section IV 18 Answers Section IV 20 SECTION V: OPTIONS STRATEGIES Option Use and Price 21 Hedging with Options 22 TEST Section V 23 Answers Section V 24 CONCLUSION 25 GLOSSARY 26 THE POWER OF CHOICE How do commercial buyers and sellers of volatile commodities protect themselves from the ever-changing and unpredictable nature of today’s business climate? They use a practice called hedging. This time-tested practice has become a stan- dard in many industries. Hedging can be defined as taking offsetting positions in related markets. -
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. -
Finance: a Quantitative Introduction Chapter 9 Real Options Analysis
Investment opportunities as options The option to defer More real options Some extensions Finance: A Quantitative Introduction Chapter 9 Real Options Analysis Nico van der Wijst 1 Finance: A Quantitative Introduction c Cambridge University Press Investment opportunities as options The option to defer More real options Some extensions 1 Investment opportunities as options 2 The option to defer 3 More real options 4 Some extensions 2 Finance: A Quantitative Introduction c Cambridge University Press Investment opportunities as options Option analogy The option to defer Sources of option value More real options Limitations of option analogy Some extensions The essential economic characteristic of options is: the flexibility to exercise or not possibility to choose best alternative walk away from bad outcomes Stocks and bonds are passively held, no flexibility Investments in real assets also have flexibility, projects can be: delayed or speeded up made bigger or smaller abandoned early or extended beyond original life-time, etc. 3 Finance: A Quantitative Introduction c Cambridge University Press Investment opportunities as options Option analogy The option to defer Sources of option value More real options Limitations of option analogy Some extensions Real Options Analysis Studies and values this flexibility Real options are options where underlying value is a real asset not a financial asset as stock, bond, currency Flexibility in real investments means: changing cash flows along the way: profiting from opportunities, cutting off losses Discounted -
Seeking Income: Cash Flow Distribution Analysis of S&P 500
RESEARCH Income CONTRIBUTORS Berlinda Liu Seeking Income: Cash Flow Director Global Research & Design Distribution Analysis of S&P [email protected] ® Ryan Poirier, FRM 500 Buy-Write Strategies Senior Analyst Global Research & Design EXECUTIVE SUMMARY [email protected] In recent years, income-seeking market participants have shown increased interest in buy-write strategies that exchange upside potential for upfront option premium. Our empirical study investigated popular buy-write benchmarks, as well as other alternative strategies with varied strike selection, option maturity, and underlying equity instruments, and made the following observations in terms of distribution capabilities. Although the CBOE S&P 500 BuyWrite Index (BXM), the leading buy-write benchmark, writes at-the-money (ATM) monthly options, a market participant may be better off selling out-of-the-money (OTM) options and allowing the equity portfolio to grow. Equity growth serves as another source of distribution if the option premium does not meet the distribution target, and it prevents the equity portfolio from being liquidated too quickly due to cash settlement of the expiring options. Given a predetermined distribution goal, a market participant may consider an option based on its premium rather than its moneyness. This alternative approach tends to generate a more steady income stream, thus reducing trading cost. However, just as with the traditional approach that chooses options by moneyness, a high target premium may suffocate equity growth and result in either less income or quick equity depletion. Compared with monthly standard options, selling quarterly options may reduce the loss from the cash settlement of expiring calls, while selling weekly options could incur more loss. -
The Recovery Theorem
The Recovery Theorem The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Ross, Steve. “The Recovery Theorem.” The Journal of Finance 70, no. 2 (April 2015): 615–48. As Published http://dx.doi.org/10.1111/jofi.12092 Publisher American Finance Association/Wiley Version Original manuscript Citable link http://hdl.handle.net/1721.1/99126 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike Detailed Terms http://creativecommons.org/licenses/by-nc-sa/4.0/ The Recovery Theorem Journal of Finance forthcoming Steve Ross Franco Modigliani Professor of Financial Economics Sloan School, MIT Abstract We can only estimate the distribution of stock returns but from option prices we observe the distribution of state prices. State prices are the product of risk aversion – the pricing kernel – and the natural probability distribution. The Recovery Theorem enables us to separate these so as to determine the market’s forecast of returns and the market’s risk aversion from state prices alone. Among other things, this allows us to recover the pricing kernel, the market risk premium, the probability of a catastrophe, and to construct model free tests of the efficient market hypothesis. I want to thank the participants in the UCLA Finance workshop for their insightful comments as well as Richard Roll, Hanno Lustig, Rick Antle, Andrew Jeffrey, Peter Carr, Kevin Atteson, Jessica Wachter, Ian Martin, Leonid Kogan, Torben Andersen, John Cochrane, Dimitris Papanikolaou, William Mullins, Jon Ingersoll, Jerry Hausman, Andy Lo, Steve Leroy, George Skiadopoulos, Xavier Gabaix, Patrick Dennis, Phil Dybvig, Will Mullins, Nicolas Caramp, Rodrigo Adao, the referee and the associate editor and editor. -
Implied Volatility Modeling
Implied Volatility Modeling Sarves Verma, Gunhan Mehmet Ertosun, Wei Wang, Benjamin Ambruster, Kay Giesecke I Introduction Although Black-Scholes formula is very popular among market practitioners, when applied to call and put options, it often reduces to a means of quoting options in terms of another parameter, the implied volatility. Further, the function σ BS TK ),(: ⎯⎯→ σ BS TK ),( t t ………………………………(1) is called the implied volatility surface. Two significant features of the surface is worth mentioning”: a) the non-flat profile of the surface which is often called the ‘smile’or the ‘skew’ suggests that the Black-Scholes formula is inefficient to price options b) the level of implied volatilities changes with time thus deforming it continuously. Since, the black- scholes model fails to model volatility, modeling implied volatility has become an active area of research. At present, volatility is modeled in primarily four different ways which are : a) The stochastic volatility model which assumes a stochastic nature of volatility [1]. The problem with this approach often lies in finding the market price of volatility risk which can’t be observed in the market. b) The deterministic volatility function (DVF) which assumes that volatility is a function of time alone and is completely deterministic [2,3]. This fails because as mentioned before the implied volatility surface changes with time continuously and is unpredictable at a given point of time. Ergo, the lattice model [2] & the Dupire approach [3] often fail[4] c) a factor based approach which assumes that implied volatility can be constructed by forming basis vectors. Further, one can use implied volatility as a mean reverting Ornstein-Ulhenbeck process for estimating implied volatility[5]. -
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. -
A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model
risks Article A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model Emilio Russo Department of Economics, Statistics and Finance, University of Calabria, Ponte Bucci cubo 1C, 87036 Rende (CS), Italy; [email protected] Received: 29 November 2019; Accepted: 25 January 2020 ; Published: 29 January 2020 Abstract: This paper provides a discrete-time approach for evaluating financial and actuarial products characterized by path-dependent features in a regime-switching risk model. In each regime, a binomial discretization of the asset value is obtained by modifying the parameters used to generate the lattice in the highest-volatility regime, thus allowing a simultaneous asset description in all the regimes. The path-dependent feature is treated by computing representative values of the path-dependent function on a fixed number of effective trajectories reaching each lattice node. The prices of the analyzed products are calculated as the expected values of their payoffs registered over the lattice branches, invoking a quadratic interpolation technique if the regime changes, and capturing the switches among regimes by using a transition probability matrix. Some numerical applications are provided to support the model, which is also useful to accurately capture the market risk concerning path-dependent financial and actuarial instruments. Keywords: regime-switching risk; market risk; path-dependent derivatives; insurance policies; binomial lattices; discrete-time models 1. Introduction With the aim of providing an accurate evaluation of the risks affecting financial markets, a wide range of empirical research evidences that asset returns show stochastic volatility patterns and fatter tails with respect to the standard normal model. -
The Promise and Peril of Real Options
1 The Promise and Peril of Real Options Aswath Damodaran Stern School of Business 44 West Fourth Street New York, NY 10012 [email protected] 2 Abstract In recent years, practitioners and academics have made the argument that traditional discounted cash flow models do a poor job of capturing the value of the options embedded in many corporate actions. They have noted that these options need to be not only considered explicitly and valued, but also that the value of these options can be substantial. In fact, many investments and acquisitions that would not be justifiable otherwise will be value enhancing, if the options embedded in them are considered. In this paper, we examine the merits of this argument. While it is certainly true that there are options embedded in many actions, we consider the conditions that have to be met for these options to have value. We also develop a series of applied examples, where we attempt to value these options and consider the effect on investment, financing and valuation decisions. 3 In finance, the discounted cash flow model operates as the basic framework for most analysis. In investment analysis, for instance, the conventional view is that the net present value of a project is the measure of the value that it will add to the firm taking it. Thus, investing in a positive (negative) net present value project will increase (decrease) value. In capital structure decisions, a financing mix that minimizes the cost of capital, without impairing operating cash flows, increases firm value and is therefore viewed as the optimal mix.