Relevant bibliographies by topics / Option Pricing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Option Pricing'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Option Pricing"

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Jensen, Bjarne Astrup, and Jørgen Aase Nielsen. "OPTION PRICING BOUNDS AND THE PRICING OF BOND OPTIONS." Journal of Business Finance & Accounting 23, no. 4 (June 1996): 535–56. http://dx.doi.org/10.1111/j.1468-5957.1996.tb01025.x.

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Li, Feng. "Option Pricing." Journal of Derivatives 7, no. 4 (May 31, 2000): 49–65. http://dx.doi.org/10.3905/jod.2000.319134.

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Lord, Richard. "Option pricing." Journal of Banking & Finance 10, no. 1 (March 1986): 157–61. http://dx.doi.org/10.1016/0378-4266(86)90028-2.

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Mitra, Sovan. "Multifactor option pricing: pricing bounds and option relations." International Journal of Applied Decision Sciences 3, no. 1 (2010): 15. http://dx.doi.org/10.1504/ijads.2010.032238.

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Blake, D. "Option pricing models." Journal of the Institute of Actuaries 116, no. 3 (December 1989): 537–58. http://dx.doi.org/10.1017/s0020268100036696.

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Guo, Yuanyuan. "Comparative Analysis of the Application of Monte Carlo Model and BSM Model in European Option Pricing." BCP Business & Management 32 (November 22, 2022): 43–48. http://dx.doi.org/10.54691/bcpbm.v32i.2856.

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At present, the expansion of China's domestic options market brings positive factors and risks, and in order to avoid risks, it is crucial to choose a suitable model for option pricing. This article provides an example of an option for the underlying asset of the SSE 50 ETF. Using the BSM model and the Monte Carlo model for the selected option pricing, and comparing the actual option price. It is found that the pricing efficiency of the Monte Carlo model is higher than that of the BSM model when the number of simulations reaches 30,000 times in the call option, and there is little difference between the two in the put option pricing. It is recommended to prefer the Monte Carlo model when pricing call options and the more convenient BSM model when pricing put options.
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Ryszard, Kokoszczyński, Sakowski Paweł, and Ślepaczuk Robert. "Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options." Central European Economic Journal 4, no. 51 (April 1, 2019): 18–39. http://dx.doi.org/10.1515/ceej-2018-0010.

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Abstract In this study, we analyse the performance of option pricing models using 5-minutes transactional data for the Japanese Nikkei 225 index options. We compare 6 different option pricing models: the Black (1976) model with different assumptions about the volatility process (realized volatility with and without smoothing, historical volatility and implied volatility), the stochastic volatility model of Heston (1993) and the GARCH(1,1) model. To assess the model performance, we use median absolute percentage error based on differences between theoretical and transactional options prices. We present our results with respect to 5 classes of option moneyness, 5 classes of option time to maturity and 2 option types (calls and puts). The Black model with implied volatility (BIV) comes as the best and the GARCH(1,1) as the worst one. For both call and put options, we observe the clear relation between average pricing errors and option moneyness: high error values for deep OTM options and the best fit for deep ITM options. Pricing errors also depend on time to maturity, although this relationship depend on option moneyness. For low value options (deep OTM and OTM), we obtained lower errors for longer maturities. On the other hand, for high value options (ITM and deep ITM) pricing errors are lower for short times to maturity. We obtained similar average pricing errors for call and put options. Moreover, we do not see any advantage of much complex and time-consuming models. Additionally, we describe liquidity of the Nikkei225 option pricing market and try to compare the results we obtain here with a detailed study for Polish emerging option market (Kokoszczyński et al. 2010b).
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Behera, Prashanta kumar, and Dr Ramraj T. Nadar. "Dynamic Approach for Index Option Pricing Using Different Models." Journal of Global Economy 13, no. 2 (June 26, 2017): 105–20. http://dx.doi.org/10.1956/jge.v13i2.460.

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Option pricing is one of the exigent and elementary problems of computational finance. Our aims to determine the nifty index option price through different valuation technique. In this paper, we illustrate the techniques for pricing of options and extracting information from option prices. We also describe various ways in which this information has been used in a number of applications. When dealing with options, we inevitably encounter the Black-Scholes-Merton option pricing formula, which has revolutionized the way in which options are priced in modern time. Black and Scholes (1973) and Merton (1973) on pricing European style options assumes that stock price follows a geometric Brownian motion, which implies that the terminal stock price has a lognormal distribution. Through hedging arguments, BSM shows that the terminal stock price distribution needed for pricing option can be stated without reference to the preference parameter and to the growth rate of the stock. This is now known as the risk-neutral approach to option pricing. The terminal stock price distribution, for the purpose of pricing options, is now known as the state-price density or the risk-neutral density in contrast to the actual stock price distribution, which is sometimes referred to as the physical, objective, or historical distribution.
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Stamatopoulos, Nikitas, Daniel J. Egger, Yue Sun, Christa Zoufal, Raban Iten, Ning Shen, and Stefan Woerner. "Option Pricing using Quantum Computers." Quantum 4 (July 6, 2020): 291. http://dx.doi.org/10.22331/q-2020-07-06-291.

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We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.
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Shao, Zeyuan. "Pricing Technique for European Option and Application." Highlights in Business, Economics and Management 14 (June 12, 2023): 14–18. http://dx.doi.org/10.54097/hbem.v14i.8930.

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In financial mathematics, the pricing technique for derivatives is constantly debated. In this paper, the pricing technique of the European Option is mainly discussed, and the binomial tree (BN) model is first applied to the pricing process of European options. The previous results show that carbon credit index can be traded as an option, and BN model can correctly simulate the future price of call option constructed by consuming the carbon credit index. Secondly, the Black-Scholes (BN) model is also a crucial technique for pricing European options, and it is successfully applied to predicting the future three months' CSI 300 index option price. Finally, BN model is compared with BS model, and the result reflects that BN model can perform as well as BS model for pricing European Option When the step reaches 2000. However, the efficiency of the BN model is stable under low volatility. Under higher volatility, such as 1.5 sigmas, the required steps will increase to achieve the same accuracy level. For American options, the BN simulator of a put option is close to the actual value, but the call option simulator will fluctuate. For the stock-pricing process, both models estimate far above Monte-Carlo method. The result of this paper is to provide some clues for pricing European options with different methods.
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Dissertations / Theses on the topic "Option Pricing"

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Bieta, Volker, Udo Broll, and Wilfried Siebe. "Strategic option pricing." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71719.

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In this paper an extension of the well-known binomial approach to option pricing is presented. The classical question is: What is the price of an option on the risky asset? The traditional answer is obtained with the help of a replicating portfolio by ruling out arbitrage. Instead a two-person game from the Nash equilibrium of which the option price can be derived is formulated. Consequently both the underlying asset’s price at expiration and the price of the option on this asset are endogenously determined. The option price derived this way turns out, however, to be identical to the classical no-arbitrage option price of the binomial model if the expiration-date prices of the underlying asset and the corresponding risk-neutral probability are properly adjusted according to the Nash equilibrium data of the game.
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劉伯文 and Pak-man Lau. "Option pricing: a survey." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31977911.

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Gu, Chenchen. "Option Pricing Using MATLAB." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/382.

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This paper describes methods for pricing European and American options. Monte Carlo simulation and control variates methods are employed to price call options. The binomial model is employed to price American put options. Using daily stock data I am able to compare the model price and market price and speculate as to the cause of difference. Lastly, I build a portfolio in an Interactive Brokers paper trading [1] account using the prices I calculate. This project was done a part of the masters capstone course Math 573: Computational Methods of Financial Mathematics.
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Lau, Pak-man. "Option pricing : a survey /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14386057.

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Matsumoto, Manabu. "Options on portfolios of options and multivariate option pricing and hedging." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324627.

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Neset, Yngvild. "Spectral Discretizations of Option Pricing Models for European Put Options." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-26546.

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The aim of this thesis is to solve option pricing models efficiently by using spectral methods. The option pricing models that will be solved are the Black-Scholes model and Heston's stochastic volatility model. We will restrict us to pricing European put options. We derive the partial differential equations governing the two models and their corresponding weak formulations. The models are then solved using both the spectral Galerkin method and a polynomial collocation method. The numerical solutions are compared to the exact solution. The exact solution is also used to study the numerical convergence. We compare the results from the two numerical methods, and look at the time consumptions of the different methods. Analysis of the methods are also given. This includes coercivity, continuity, stability and convergence estimates.For Black-Scholes equation, we study both the original equation and the log transformed equation, and we also compare the results to a solution obtained by using a finite element method solver.
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Compiani, Vera. "Particle methods in option pricing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13896/.

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Lo scopo di questa tesi è la calibrazione del modello di volatilità locale-stocastico (SLV) usando il metodo delle particelle. Il modello SLV riproduce il prezzo di un asset finanziario descritto da un processo stocastico. Il coefficiente di diffusione o volatilità del processo è costituito da una parte stocastica, la varianza, e da una parte locale chiamata funzione di leva che dipende dal processo stesso e che dà origine ad un'equazione differenziale alle derivate parziali (PDE) non lineare. La funzione di leva deve essere calibrata alla tipica curva che appare nella volatilità implicita dei dati di mercato, il volatility-smile. Per fa ciò si utilizza un metodo computazionale preso dalla fisica: il metodo delle particelle. Esso consiste nell'approssimare la distribuzione di probabilità del processo con una distribuzione empirica costituita da N particelle. Le N particelle consistono in N variabili aleatorie indipendenti e identicamente distribuite che seguono ciascuna l'equazione differenziale stocastica del prezzo con N moti Browniani indipendenti. La funzione di leva dipenderà così da una misura di probabilità casuale e la PDE non-lineare si ridurrà ad una PDE lineare con N gradi di libertà. Il risultato finale è una funzione di leva determinata dall'interazione tra tutte le particelle. La simulazione al computer viene eseguita tramite la tecnica di implementazione in parallelo che accelera i calcoli sfruttando l'architettura grafica della GPU.
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Belova, Anna, and Tamara Shmidt. "Meshfree methods in option pricing." Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16383.

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A meshfree approximation scheme based on the radial basis function methods is presented for the numerical solution of the options pricing model. This thesis deals with the valuation of the European, Barrier, Asian, American options of a single asset and American options of multi assets. The option prices are modeled by the Black-Scholes equation. The θ-method is used to discretize the equation with respect to time. By the next step, the option price is approximated in space with radial basis functions (RBF) with unknown parameters, in particular, we con- sider multiquadric radial basis functions (MQ-RBF). In case of Ameri- can options a penalty method is used, i.e. removing the free boundary is achieved by adding a small and continuous penalty term to the Black- Scholes equation. Finally, a comparison of analytical and finite difference solutions and numerical results from the literature is included.
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Pour, Abdollah Farshchi Elham. "Option Pricing with Extreme Events." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-161963.

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Wiklund, Erik. "Asian Option Pricing and Volatility." Thesis, KTH, Matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-93714.

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Abstract   An Asian option is a path-depending exotic option, which means that either the settlement price or the strike of the option is formed by some aggregation of underlying asset prices during the option lifetime. This thesis will focus on European style Arithmetic Asian options where the settlement price at maturity is formed by the arithmetic average price of the last seven days of the underlying asset. For this type of option it does not exist any closed form analytical formula for calculating the theoretical option value. There exist closed form approximation formulas for valuing this kind of option. One such, used in this thesis, approximate the value of an Arithmetic Asian option by conditioning the valuation on the geometric mean price. To evaluate the accuracy in this approximation and to see if it is possible to use the well known Black-Scholes formula for valuing Asian options, this thesis examines the bias between Monte-Carlo simulation pricing and these closed form approximate pricings. The bias examination is done for several different volatility schemes. In general the Asian approximation formula works very well for valuing Asian options. For volatility scenarios where there is a drastic volatility shift and the period with higher volatility is before the average period of the option, the Asian approximation formula will underestimate the option value. These underestimates are very significant for OTM options, decreases for ATM options and are small, although significant, for ITM options. The Black-Scholes formula will in general overestimate the Asian option value. This is expected since the Black-Scholes formula applies to standard European options which only, implicitly, considers the underlying asset price at maturity of the option as settlement price. This price is in average higher than the Asian option settlement price when the underlying asset price has a positive drift. However, for some volatility scenarios where there is a drastic volatility shift and the period with higher volatility is before the average period of the option, even the Black-Scholes formula will underestimate the option value. As for the Asian approximation formula, these over-and underestimates are very large for OTM options and decreases for ATM and ITM options.
Sammanfattning En Asiatisk option är en vägberoende exotisk option, vilket betyder att antingen settlement-priset eller strike-priset beräknas utifrån någon form av aggregering av underliggande tillgångens priser under optionens livstid. Denna uppsats fokuserar på Aritmetiska Asiatiska optioner av Europeisk karaktär där settlement-priset vid lösen bestäms av det aritmetiska medelvärdet av underliggande tillgångens priser de sista sju dagarna. För denna typ av option finns det inga slutna analytiska formler för att beräkna optionens teoretiska värde. Det finns dock slutna approximativa formler för värdering av denna typ av optioner. En sådan, som används i denna uppsats, approximerar värdet av en Aritmetisk Asiatisk option genom att betinga värderingen på det geometriska medelpriset. För att utvärdera noggrannheten i denna approximation och för att se om det är möjligt att använda den väl kända Black-Scholes-formeln för att värdera Asiatiska optioner, så analyseras differenserna mellan Monte-Carlo-simulering och dessa slutna formlers värderingar i denna uppsats. Differenserna analyseras utifrån ett flertal olika scenarion för volatiliteten. I allmänhet så fungerar Asiatapproximationsformeln bra för värdering av Asiatiska optioner. För volatilitetsscenarion som innebär en drastisk volatilitetsförändring och där den perioden med högre volatilitet ligger innan optionens medelvärdesperiod, så undervärderar Asiatapproximationen optionens värde. Dessa undervärderingar är mycket påtagliga för OTM-optioner, avtar för ATM-optioner och är små, om än signifikanta, för ITM-optioner. Black-Scholes formel övervärderar i allmänhet Asiatiska optioners värde. Detta är väntat då Black-Scholes formel är ämnad för standard Europeiska optioner, vilka endast beaktar underliggande tillgångens pris vid optionens slutdatum som settlement-pris. Detta pris är i snitt högre än Asiatisk optioners settlement-pris när underliggande tillgångens pris har en positiv drift. Men, för vissa volatilitetsscenarion som innebär en drastisk volatilitetsförändring och där den perioden med högra volatilitet ligger innan optionens medelvärdesperiod, så undervärderar även Black-Scholes formel optionens värde. Som för Asiatapproximationen så är dessa över- och undervärderingar mycket påtagliga för OTM-optioner och avtar för ATM och ITM-optioner.
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Books on the topic "Option Pricing"

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K, Sarkar Salil, ed. Option pricing. Hull: MCB University Press, 1995.

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Clark, Iain J. Commodity Option Pricing. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118871782.

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Clark, Iain J., ed. Foreign Exchange Option Pricing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119208679.

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Perrakis, Stylianos. Stochastic Dominance Option Pricing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11590-6.

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Bates, David S. Testing option pricing models. Cambridge, MA: National Bureau of Economic Research, 1995.

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1950-, Bookstaber Richard M., ed. Option pricing & investment strategies. Chicago, Ill: Probus Pub. Co., 1987.

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Friedman, Michael. Option pricing - the binomial. Oxford: Oxford Brookes Univerisity, 2004.

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Garleanu, Nicolae. Demand-based option pricing. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Rajan, Raghuram. Pricing commodity bonds using binomial option pricing. Washington, DC (1818 H St., N.W., Washington 20433): International Economics Dept., the World Bank, 1988.

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High performance options trading: Option volatility & pricing strategies. Hoboken, N.J: J. Wiley, 2003.

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Book chapters on the topic "Option Pricing"

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Pilbeam, Keith. "Option Pricing." In Finance and Financial Markets, 388–411. London: Macmillan Education UK, 2005. http://dx.doi.org/10.1007/978-1-349-26273-1_15.

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Mostafa, Fahed, Tharam Dillon, and Elizabeth Chang. "Option Pricing." In Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk, 113–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51668-4_7.

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Zumbach, Gilles. "Option Pricing." In Springer Finance, 233–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31742-2_16.

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De Luca, Pasquale. "Option Pricing." In Springer Texts in Business and Economics, 549–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18300-3_27.

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Lindquist, W. Brent, Svetlozar T. Rachev, Yuan Hu, and Abootaleb Shirvani. "Option Pricing." In Dynamic Modeling and Econometrics in Economics and Finance, 197–226. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15286-3_12.

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Korn, Ralf, and Elke Korn. "Option pricing." In Graduate Studies in Mathematics, 79–151. Providence, Rhode Island: American Mathematical Society, 2000. http://dx.doi.org/10.1090/gsm/031/03.

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Pilbeam, Keith. "Option Pricing." In Finance & Financial Markets, 371–92. London: Macmillan Education UK, 2010. http://dx.doi.org/10.1007/978-1-137-09043-0_15.

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Kallsen, Jan. "Option Pricing." In Handbook of Financial Time Series, 599–613. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_26.

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Pilbeam, Keith. "Option Pricing." In Finance & Financial Markets, 352–72. London: Macmillan Education UK, 2018. http://dx.doi.org/10.1057/978-1-137-51563-6_15.

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Dempsey, Michael. "Option pricing." In Financial Risk Management and Derivative Instruments, 200–212. Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Routledge advanced text in economics and finance: Routledge, 2021. http://dx.doi.org/10.4324/9781003132240-15.

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Conference papers on the topic "Option Pricing"

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Suo, Simon, Ruiming Zhu, Ryan Attridge, and Justin Wan. "GPU option pricing." In SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2830556.2830564.

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Cutland, N. J., P. E. Kopp, and W. Willinger. "Nonstandard methods in option pricing." In Proceedings of the 30th IEEE Conference on Decision and Control. IEEE, 1991. http://dx.doi.org/10.1109/cdc.1991.261595.

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Wang, Zhaohai. "Option Pricing in Incomplete Markets." In 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013). Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icaiees-13.2013.52.

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Aboura, Khalid, and Johnson I. Agbinya. "Option pricing with informed judgment." In 2013 Pan African International Conference on Information Science, Computing and Telecommunications (PACT). IEEE, 2013. http://dx.doi.org/10.1109/scat.2013.7055092.

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SAMMARTINO, MARCO. "ASYMPTOTIC METHODS IN OPTION PRICING." In Proceedings of the 12th Conference on WASCOM 2003. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702937_0056.

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Guo, Xin. "Some Lookback Option Pricing Problems." In Proceedings of the International Conference on Mathematical Finance. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799579_0004.

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Solomon, S., R. K. Thulasiram, and P. Thulasiraman. "Option Pricing on the GPU." In 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC 2010). IEEE, 2010. http://dx.doi.org/10.1109/hpcc.2010.54.

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Jianhua Wang and Dan Li. "Stable distribution and option pricing." In 2011 International Conference on Multimedia Technology (ICMT). IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002644.

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Primbs, J. A. "Option pricing bounds via semidefinite programming." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1656391.

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Yuzhanin, Artur, Ivan Gankevich, Eduard Stepanov, and Vladimir Korkhov. "Efficient Asian option pricing with CUDA." In 2015 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2015. http://dx.doi.org/10.1109/hpcsim.2015.7237103.

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Reports on the topic "Option Pricing"

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Chalasani, P., I. Saias, and S. Jha. Approximate option pricing. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/373883.

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Bates, David. Testing Option Pricing Models. Cambridge, MA: National Bureau of Economic Research, May 1995. http://dx.doi.org/10.3386/w5129.

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Garleanu, Nicolae, Lasse Heje Pedersen, and Allen Poteshman. Demand-Based Option Pricing. Cambridge, MA: National Bureau of Economic Research, December 2005. http://dx.doi.org/10.3386/w11843.

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Bates, David. Empirical Option Pricing Models. Cambridge, MA: National Bureau of Economic Research, December 2021. http://dx.doi.org/10.3386/w29554.

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5

Asea, Patrick, and Mthuli Ncube. Heterogeneous Information Arrival and Option Pricing. Cambridge, MA: National Bureau of Economic Research, March 1997. http://dx.doi.org/10.3386/w5950.

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6

Rosenberg, Joshua, and Robert Engle. Option Hedging Using Empirical Pricing Kernels. Cambridge, MA: National Bureau of Economic Research, October 1997. http://dx.doi.org/10.3386/w6222.

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7

Ait-Sahalia, Yacine, and Jefferson Duarte. Nonparametric Option Pricing under Shape Restrictions. Cambridge, MA: National Bureau of Economic Research, May 2002. http://dx.doi.org/10.3386/w8944.

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8

Rojas-Bernal, Alejandro, and Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/be.1156.

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Abstract:
We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.
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Dumas, Bernard, L. Peter Jennergren, and Bertil Naslund. Currency Option Pricing in Credible Target Zones. Cambridge, MA: National Bureau of Economic Research, November 1993. http://dx.doi.org/10.3386/w4522.

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Lo, Andrew, and Jiang Wang. Implementing Option Pricing Models When Asset Returns Are Predictable. Cambridge, MA: National Bureau of Economic Research, April 1994. http://dx.doi.org/10.3386/w4720.

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