Bibliografie tematiche / Option Pricing

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Letteratura scientifica selezionata sul tema "Option Pricing"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Option Pricing"

1

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

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2

Li, Chenwei. "A Study of Option Pricing Models with Market Price Adjustments: Empirical Analysis Beyond the Black-Scholes Model." Advances in Economics, Management and Political Sciences 137, no. 1 (2024): 94–98. https://doi.org/10.54254/2754-1169/2024.18702.

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Abstract (sommario):
In 1973, Fischer Black and Myron Scholes unveiled the Black-Scholes option pricing model, a groundbreaking contribution that profoundly influenced the domain of option pricing theory. The introduction of the Black-Scholes pricing formula has garnered substantial acclaim across both academic and industrial spheres, leading to its widespread dissemination and application. This formula not only underscores its vital significance but also exemplifies its unique position as a cornerstone of financial theory, reshaping how options are valued and traded in markets worldwide. However, in the real fina
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Li, Feng. "Option Pricing." Journal of Derivatives 7, no. 4 (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 (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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6

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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Abstract (sommario):
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 b
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Song, Peihang. "Research on the Development of Implied Volatility in Option Pricing." Advances in Economics, Management and Political Sciences 17, no. 1 (2023): 7–13. http://dx.doi.org/10.54254/2754-1169/17/20231048.

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Abstract (sommario):
Option, as an important financial derivative, has received more and more attention from investors in recent years. This paper includes three parts, the first part introduces the concept of options, including the birth and history of options, the trading of options, and different types of options. Then it discusses the history of option pricing and two typical and classic option pricing models and then introduces implied volatility. The second part of this paper introduces the specific development of the option pricing models, the correction process of option pricing models, and volatility mode
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Blake, D. "Option pricing models." Journal of the Institute of Actuaries 116, no. 3 (1989): 537–58. http://dx.doi.org/10.1017/s0020268100036696.

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9

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 (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
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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 (2017): 105–20. http://dx.doi.org/10.1956/jge.v13i2.460.

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Abstract (sommario):
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 Mer
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Tesi sul tema "Option Pricing"

1

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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Abstract (sommario):
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
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2

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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Abstract (sommario):
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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5

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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6

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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Abstract (sommario):
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 numerica
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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
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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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Abstract (sommario):
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 functio
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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 (sommario):
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 thi
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Libri sul tema "Option Pricing"

1

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

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2

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

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

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

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5

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

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6

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

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7

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

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8

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

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9

Rajan, Raghuram. Pricing commodity bonds using binomial option pricing. International Economics Dept., the World Bank, 1988.

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10

Gibson, Rajna. Option valuation: Analyzing and pricing standardized option contracts. Georg, 1988.

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Capitoli di libri sul tema "Option Pricing"

1

Pilbeam, Keith. "Option Pricing." In Finance and Financial Markets. 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. 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. 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. 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. 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. 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. 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. 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. 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. Routledge, 2021. http://dx.doi.org/10.4324/9781003132240-15.

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Atti di convegni sul tema "Option Pricing"

1

Premsundar, Sangeetha, Vishalakshi Prabhu H, and Vikram N Bahadurdesai. "Deep Learning Model for Option Pricing - Review." In 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2024. https://doi.org/10.1109/csitss64042.2024.10816734.

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Bitar, Ahmad W. "Robust European Call Option Pricing via Linear Regression." In 2025 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics Companion (CiFer Companion). IEEE, 2025. https://doi.org/10.1109/cifercompanion65204.2025.10980400.

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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. 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). 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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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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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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Rapporti di organizzazioni sul tema "Option Pricing"

1

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

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

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

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

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

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

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

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

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Abstract (sommario):
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 highl
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Dumas, Bernard, L. Peter Jennergren, and Bertil Naslund. Currency Option Pricing in Credible Target Zones. National Bureau of Economic Research, 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. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4720.

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