DOCSLIB.ORG

Ch 12. Interest Rate and Credit Models

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ch 12. Interest Rate and Credit Models Ch 12. Interest Rate and Credit Models I. Equilibrium Interest Rate Models II. No-Arbitrage Interest Rate Models III. Forward Rate Models IV. Credit Risk Models • This chapter introduces interest rate models first. Two categories of the interest rate models, the equilibrium and no-arbitrage models, will be introduced. In addition, the forward rate models is also discussed, in which the risk factor is the instantaneous forward rate rather than the instantaneous short rate in the interest rate models. Finally, two classical credit risk models, the reduced-form and structural models, are introduced. I. Equilibrium Interest Rate Models • After the emergence of the Black-Scholes model, some academics try to model the bond price following the lognormal or normal distribution and thus derive the pricing formulae of the bond options following the method in the Black-Scholes model. However, the critical problem of these model is that the bond price is neither lognormally nor normally distributed. • Furthermore, for all bonds and bonds options, their prices are affected by the change of the term structure of interest rates, which implies that they share a common source of risk factor. So it is inappropriate to model the movement of the price of each bond separately, i.e., the price of each bond is assumed to follow an individual stochastic process. • It is known that there exist a relationship between the bond price and the interest rate theoretically. Hence, some academics turn to consider the stochastic process of interest rates. They try to model the short rate process dr, which is by definition the instantaneous interest rate for an infinitesimal period of time. • It is worth noting that any interest rate we can observe in the market is associated with a time to maturity and thus the interest rates we can observe in the market is called the long rates. Unlike the long rate, the short rate is unobservable. 12-1 • To model the short rate, there are two streams of models: the equilibrium and no-arbitrage models. In this section, two famous equilibrium interest rate models are introduced: the Vasicek and Cox-Ingersoll-Ross (CIR) models. • Vasicek model dr = β(µ − r)dt + σdZ, where µ is the long-term mean of r, and β measures the speed of mean reversion. By performing the stochastic integral, we can express r(T ) as follows. −β(T −t) β(T −t) R T β(τ−t) r(T ) = e (r(t) + µ(e − 1) + σ e dZ(τ)); t where r(t) is the level of short rate today. From the above equation, we know that r(T ) is normally distributed, and −β(T −t) Et[r(T )] = µ + (r(t) − µ)e ; σ2 −2β(T −t) vart(r(T )) = 2β (1 − e ): ∗ Denote P to be the current price of a zero coupon bond with 1 dollar payoff at maturity, and P is a function of t (the time point today), r, and T (the maturity date). According to the It^o'sLemma, @P @P 1 2 @2P @P dP (t; r; T ) = ( @t + β(µ − r) @r + 2 σ @r2 )dt + σ @r dZ ≡ P · µ(t; r; T )dt + P · σ(t; r; T )dZ. (Note that the maturity date T is a constant in the bond contract, so it is not necessary to consider the differentiation with respect to T in the It^o'sLemma.) longing σ(t; r; T )P (t; r; T ) shares of P (t; r; T ) Construct a portfolio W by 2 2 1 ; shorting σ(t; r; T1)P (t; r; T1) shares of P (t; r; T2) ) dW = f[σ(t; r; T2)P (t; r; T2)]µ(t; r; T1)P (t; r; T1)−[σ(t; r; T1)P (t; r; T1)]µ(t; r; T2)P (t; r; T2)gdt = r(t)W dt = r(t)[σ(t; r; T2)P (t; r; T2)P (t; r; T1)−σ(t; r; T1)P (t; r; T1)P (t; r; T2)]dt ) µ(t; r; T1)σ(t; r; T2) − r(t)σ(t; r; T2) = µ(t; r; T2)σ(t; r; T1) − r(t)σ(t; r; T1) ) µ(t;r;T1)−r(t) = µ(t;r;T2)−r(t) = λ σ(t;r;T1) σ(t;r;T2) (For zero-coupon bonds with different maturity dates, the ratio of their excess returns over volatilities are identical. It is reasonable since all these bonds share a common risk factor dr and they should be with the same market price of risk, which is, by definition, the (excess return)-volatility ratio of any derivatives based on r.) ) µ(t; r; T ) − r(t) = λσ(t; r; T ). Substitute µ(t; r; T ) and σ(t; r; T ) of dP (t; r; T ) to derive 2 ( @P +β(µ−r) @P + 1 σ2 @ P ) @P @t @r 2 @r2 σ @r P − r(t) = λ P 12-2 @P @P 1 2 @2P ) @t + (β(µ − r) − λσ) @r + 2 σ @r2 = rP . (Based on the Vasicek short rate model, the price of any zero coupon bond should satisfy the above partial differential equation.) ∗ To solve the above partial differential equation, we can derive the theoretical value of any zero coupon bond as follows. P (t; r; T ) = A(t; T )e−B(t;T )r(t); where r(t) is the current short rate level, and 2 ( (B(t;T )−T +t)(β2µ− σ ) σ2B(t;T )2 exp[ 2 + σλ (B(t; T ) − (T − t)) − ] if β 6= 0 A(t; T ) = β2 β 4β , σ2(T −t)3 exp[ 6 ] if β = 0 −β(T −t) 1−e if β 6= 0 B(t; T ) = β . T − t if β = 0 ∗ Note that in the risk-neutral world, the market price of risk, λ, is zero, and the above pricing formula becomes identical to that presented in Hull (2011). ∗ The formula of the zero coupon bond price P (t; r; T ) makes it possible to derive the whole term structure based on the Vasicek short rate process. This is because given the zero coupon bond price P (t; r; T ) and the identity function P (t; r; T ) = exp(−r(t; T )(T − t)), the zero rate corresponding to the maturity date T , r(t; T ), can be derived through r(t; T ) = − ln(P (t; r; T ))=(T − t). ∗ In a word, once you specify the values of the parameters β, µ, and σ and the intial value of the short rate r(t), you can derive a corresponding term structure. The Vasicek model can generate normal, inverted, and humped-shape term structures. ∗ One possible way to calibrate the estimations of β, µ, σ, and r(t) is to employ the least squares approach such that the generated term structure can best fit the real term sturcture in the market. 12-3 The binomial tree to simulate the Vasicek interest rate process: Since the volatility term is constant in the Vasicek model, it is straightforward to apply the method in Cox and Rubinstein (1985) to constructing the corresponding binomial tree. Figure 12-1 r t r r t p 1 β(µ−r) ∆t p(r) = 2 + 2σ . If p(r) is not in [0; 1], consider a smaller ∆t to fix this problem. When ∆t approaches zero, p(r) will approach 0:5. • Cox-Ingersoll-Ross (CIR) model p dr = β(µ − r)dt + σ rdZ −β(T −t) Et[r(T )] = µ + (r(t) − µ)e ; σ2 −β(T −t) −2β(T −t) σ2 −β(T −t) 2 vart(r(T )) = r(t) β [e − e ] + µ 2β (1 − e ) : ∗ In comparison with the Vasicek interest rate process, the only difference is the volatility term, which is from a constant σ to become σpr(t). The modification decreases the volatility of the short rate when the interest rate is low. When the short rate reaches zero (the volatility term approaching zero as well), the positive drift makes the subsequent short rate become positive. Hence, the CIR model remedies the negative interest rate problem in the Vasicek model. ∗ Technically speaking, if σ2 > 2βµ, r(t) can reach zero; if σ2 ≤ 2βµ, the upward drift is sufficiently large to make the origin inaccessible. ∗ It is worth noting that Cox, Ingersoll, and Ross (1985) do not develop their model by simply modifying the Vasicek interest rate process. Instead, they derive this interest rate model based on a general equilibrium asset pricing model. ∗ In addition, the CIR process can be defined as the sum of squared Ornstein-Uhlinbeck processes as follows. X 1 1 r(t) = X2(t), where dX (t) = − αX (t)dt + σdZ (t): i i 2 i 2 i Consequently, the short rate r(t) follows a noncentral Chi-square distribution and thus is always positive. 12-4 ∗ Following the similar procedure in the Vasicek model and the assumption of λ = 0, the partial differential equation of the zero coupon bond based on the CIR model is as follows: @P @P 1 2 @2P @t + β(µ − r) @r + 2 σ r @r2 = rP: Solve the above partial differential equation, the theoretical value of the zero-coupon bond today is P (t; r; T ) = A(t; T )e−B(t;T )r(t), where r(t) is the short rate level today, and 2υe(β+υ)(T −t)=2 2βµ/σ2 A(t; T ) = [ (β+υ)[eυ(T −t)−1]+2υ ] ; 2[eυ(T −t)−1] B(t; T ) = (β+υ)[eυ(T −t)−1]+2υ ; υ = pβ2 + 2σ2: The binomial tree approach to simulate the CIR interest rate process: Because the CIR model is with a non-constant volatility, we need to apply the method in Nelson and Ramaswamy (1990) to construct the corresponding binomial tree.
Load more

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]