Academic literature on the topic 'Options (Finance) – Prices – Mathematical models'

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Journal articles on the topic "Options (Finance) – Prices – Mathematical models"

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Abraham, Rebecca, and Hani El-Chaarani. "A Mathematical Formulation of the Valuation of Ether and Ether Derivatives as a Function of Investor Sentiment and Price Jumps." Journal of Risk and Financial Management 15, no. 12 (December 8, 2022): 591. http://dx.doi.org/10.3390/jrfm15120591.

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The purpose of this study was to create quantitative models to value ether, ether futures, and ether options based upon the ability of cryptocurrencies to transform existing intermediary-verified payments to non-intermediary-based currency transfers, the ability of ether as a late mover to displace bitcoin as the first mover, and the valuation of ether in the context of investor irrationality models. The risk-averse investor’s utility function is a combination of expectations of the performance of ether, expectations of cryptocurrencies’ transformative power, and expectations of ether supersed
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CARMONA, RENÉ, and SERGEY NADTOCHIY. "TANGENT MODELS AS A MATHEMATICAL FRAMEWORK FOR DYNAMIC CALIBRATION." International Journal of Theoretical and Applied Finance 14, no. 01 (February 2011): 107–35. http://dx.doi.org/10.1142/s0219024911006280.

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Motivated by the desire to integrate repeated calibration procedures into a single dynamic market model, we introduce the notion of a "tangent model" in an abstract set up, and we show that this new mathematical paradigm accommodates all the recent attempts to study consistency and absence of arbitrage in market models. For the sake of illustration, we concentrate on the case when market quotes provide the prices of European call options for a specific set of strikes and maturities. While reviewing our recent results on dynamic local volatility and tangent Lévy models, we present a theory of t
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Kumar Jaiswal, Jitendra, and Raja Das. "Artificial Neural Network Algorithms based Nonlinear Data Analysis for Forecasting in the Finance Sector." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 169. http://dx.doi.org/10.14419/ijet.v7i4.10.20829.

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The involvement of big populace in the quantitative trading has been increased remarkably since the wired and wireless systems have become quite ubiquitous in the fields of finance and economics. Statistical, mathematical and technical analysis in parallel with machine learning and artificial intelligence are frequently being applied to perceive prices moving pattern and forecasting. However stock price do not follow any deterministic regulatory function, factor or circumstances rather than many considerations such as economy and finance, political environments, demand and supply, buying and s
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Eissa, Mahmoud A., and M. Elsayed. "Improve Stock Price Model-Based Stochastic Pantograph Differential Equation." Symmetry 14, no. 7 (July 1, 2022): 1358. http://dx.doi.org/10.3390/sym14071358.

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Although the concept of symmetry is widely used in many fields, it is almost not discussed in finance. This concept appears to be relevant in relation, for example, to mathematical models that can predict stock prices to contribute to the decision-making process. This work considers the stock price of European options with a new class of the non-constant delay model. The stochastic pantograph differential equation (SPDE) with a variable delay is provided in order to overcome the weaknesses of using stochastic models with constant delay. The proposed model is constructed to improve the evaluati
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Fernández, Lexuri, Peter Hieber, and Matthias Scherer. "Double-barrier first-passage times of jump-diffusion processes." mcma 19, no. 2 (July 1, 2013): 107–41. http://dx.doi.org/10.1515/mcma-2013-0005.

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Abstract. Required in a wide range of applications in, e.g., finance, engineering, and physics, first-passage time problems have attracted considerable interest over the past decades. Since analytical solutions often do not exist, one strand of research focuses on fast and accurate numerical techniques. In this paper, we present an efficient and unbiased Monte-Carlo simulation to obtain double-barrier first-passage time probabilities of a jump-diffusion process with arbitrary jump size distribution; extending single-barrier results by [Journal of Derivatives 10 (2002), 43–54]. In mathematical
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Aghabeygi, Mona, Kamel Louhichi, and Sergio Gomez y Paloma. "Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model." Bio-based and Applied Economics 11, no. 1 (July 20, 2022): 55–73. http://dx.doi.org/10.36253/bae-10981.

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The aim of this paper is to assess the potential impacts of different fertilizer subsidy reform options on the performance of the Iranian crops production sector. This is achieved using a Regional Crop Programming (RCP) model, based on Positive Mathematical Programming, which includes in total 14 crop activities and encompasses 31 administrative regions. The RCP model is a collection of micro-economic models, working with exogenous prices, each representing the optimal crop allocation at the regional level. The model is calibrated against observed data on crop acreage, yield responses to nitro
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Giribone, Pier Giuseppe, and Roberto Revetria. "Certificate pricing using Discrete Event Simulations and System Dynamics theory." Risk Management Magazine 16, no. 2 (August 18, 2021): 75–93. http://dx.doi.org/10.47473/2020rmm0092.

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The study proposes an innovative application of Discrete Event Simulations (DES) and System Dynamics (SD) theory to the pricing of a certain kind of certificates very popular among private investors and, more generally, in the context of wealth management. The paper shows how numerical simulation software mainly used in traditional engineering, such as industrial and mechanical engineering, can be successfully adapted to the risk analysis of structured financial products. The article can be divided into three macro-sections: in the first part a synthetic overview of the most widespread option
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Nguyen, Ngoc Quynh Anh, and Thi Ngoc Trang Nguyen. "Risk measures computation by Fourier inversion." Journal of Risk Finance 18, no. 1 (January 16, 2017): 76–87. http://dx.doi.org/10.1108/jrf-03-2016-0034.

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Purpose The purpose of this paper is to present the method for efficient computation of risk measures using Fourier transform technique. Another objective is to demonstrate that this technique enables an efficient computation of risk measures beyond value-at-risk and expected shortfall. Finally, this paper highlights the importance of validating assumptions behind the risk model and describes its application in the affine model framework. Design/methodology/approach The method proposed is based on Fourier transform methods for computing risk measures. The authors obtain the loss distribution b
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Madan, Dilip B., and King Wang. "Risk Neutral Jump Arrival Rates Implied in Option Prices and Their Models." Applied Mathematical Finance 28, no. 3 (May 4, 2021): 201–35. http://dx.doi.org/10.1080/1350486x.2021.2007145.

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SKIADOPOULOS, GEORGE. "VOLATILITY SMILE CONSISTENT OPTION MODELS: A SURVEY." International Journal of Theoretical and Applied Finance 04, no. 03 (June 2001): 403–37. http://dx.doi.org/10.1142/s021902490100105x.

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The developing literature on "smile consistent" no-arbitrage models has emerged from the need to price and hedge exotic options consistently with the prices of standard European options. This survey paper describes the steps through which this literature has evolved by providing a taxonomy of the various models. It highlights the main ideas behind the different models, and it outlines their advantages and limitations. Practical issues in implementing the models are also discussed.
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Dissertations / Theses on the topic "Options (Finance) – Prices – Mathematical models"

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Glover, Elistan Nicholas. "Analytic pricing of American put options." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1002804.

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American options are the most commonly traded financial derivatives in the market. Pricing these options fairly, so as to avoid arbitrage, is of paramount importance. Closed form solutions for American put options cannot be utilised in practice and so numerical techniques are employed. This thesis looks at the work done by other researchers to find an analytic solution to the American put option pricing problem and suggests a practical method, that uses Monte Carlo simulation, to approximate the American put option price. The theory behind option pricing is first discussed using a discrete mod
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Lee, Mou Chin. "An empirical test of variance gamma options pricing model on Hang Seng index options." HKBU Institutional Repository, 2000. http://repository.hkbu.edu.hk/etd_ra/263.

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Song, Na, and 宋娜. "Mathematical models and numerical algorithms for option pricing and optimal trading." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50662168.

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Research conducted in mathematical finance focuses on the quantitative modeling of financial markets. It allows one to solve financial problems by using mathematical methods and provides understanding and prediction of the complicated financial behaviors. In this thesis, efforts are devoted to derive and extend stochastic optimization models in financial economics and establish practical algorithms for representing and solving problems in mathematical finance. An option gives the holder the right, but not the obligation, to buy or sell an underlying asset at a specified strike price on or
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Zhao, Jing Ya. "Numerical methods for pricing Bermudan barrier options." Thesis, University of Macau, 2012. http://umaclib3.umac.mo/record=b2592939.

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Dharmawan, Komang School of Mathematics UNSW. "Superreplication method for multi-asset barrier options." Awarded by:University of New South Wales. School of Mathematics, 2005. http://handle.unsw.edu.au/1959.4/30169.

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The aim of this thesis is to study multi-asset barrier options, where the volatilities of the stocks are assumed to define a matrix-valued bounded stochastic process. The bounds on volatilities may represent, for instance, the extreme values of the volatilities of traded options. As the volatilities are not known exactly, the value of the option can not be determined. Nevertheless, it is possible to calculate extreme values. We show that these values correspond to the best and the worst case scenarios of the future volatilities for short positions and long positions in the portfolio of the op
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Mimouni, Karim. "Three essays on volatility specification in option valuation." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103274.

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Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. However, relatively little is known about the empirical shortcomings of this model. In the first essay, we investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources. We first use realized volatilities to assess the properti
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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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Chan, Ka Hou. "European call option pricing under partial information." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691380.

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Oagile, Joel. "Sequential Calibration of Asset Pricing Models to Option Prices." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29840.

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This paper implements four calibration methods on stochastic volatility models. We estimate the latent state and parameters of the models using three non-linear filtering methods, namely the extended Kalman filter (EKF), iterated extended Kalman filter (IEKF) and the unscented Kalman filter (UKF). A simulation study is performed and the non-linear filtering methods are compared to the standard least square method (LSQ). The results show that both methods are capable of tracking the hidden state and time varying parameters with varying success. The non-linear filtering methods are faster and ge
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蕭德權 and Tak-kuen Siu. "Risk measures in finance and insurance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31242297.

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Books on the topic "Options (Finance) – Prices – Mathematical models"

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Wilmott, Paul. Option pricing: Mathematical models and computation. Oxford, UK: Oxford Financial Press, 1997.

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Katz, Jeffrey Owen. Advanced option pricing models: An empirical approach to valuing options. New York: McGraw-Hill, 2005.

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

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Matthias, Ehrhardt, ed. Nonlinear models in mathematical finance: New research trends in option pricing. New York: Nova Science Publishers, 2008.

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Hughston, L. P., and Matheus R. Grasselli. Finance at Fields. Singapore: World Scientific, 2013.

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Mandler, Martin. Market expectations and option prices: Techniques and applications. Heidelberg: Physica Verlag, 2003.

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Mandler, Martin. Market expectations and option prices: Techniques and applications. New York: Physica-Verlag, 2003.

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Capiński, Marek. The Black-Scholes model. New York: Cambridge University Press, 2013.

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Chriss, Neil. Black-Scholes and beyond: Option pricing models. New York: McGraw-Hill, 1997.

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Chriss, Neil. Black-Scholes and beyond: Option pricing models. Chicago: Irwin, 1997.

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Book chapters on the topic "Options (Finance) – Prices – Mathematical models"

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Hobson, David. "The Skorokhod Embedding Problem and Model-Independent Bounds for Option Prices." In Paris-Princeton Lectures on Mathematical Finance 2010, 267–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14660-2_4.

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Eberlein, Ernst, Kathrin Glau, and Antonis Papapantoleon. "Analyticity of the Wiener–Hopf Factors and Valuation of Exotic Options in Lévy Models." In Advanced Mathematical Methods for Finance, 223–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18412-3_8.

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Nardon, Martina, and Paolo Pianca. "Extracting implied dividends from options prices: Some applications to the Italian derivatives market." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 315–22. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_37.

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Nardon, Martina, and Paolo Pianca. "The Effects of Curvature and Elevation of the Probability Weighting Function on Options Prices." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 149–52. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_35.

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Davis, Mark H. A. "3. The classical theory of option pricing." In Mathematical Finance: A Very Short Introduction, 30–60. Oxford University Press, 2019. http://dx.doi.org/10.1093/actrade/9780198787945.003.0003.

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‘The classical theory of option pricing’ explains the theory of arbitrage pricing, which is closely related to the Dutch Book Arguments, but which brings in a new factor: prices in financial markets evolve over time and participants are able to trade at any time, instead of just taking bets and awaiting the result. In addition to the general theory, pricing models and methods have been developed for specific markets—foreign exchange, interest rates, and credit. The binomial and continuous-time mathematical models for stock prices are introduced along with the Black–Scholes formula, the volatility surface, the difference between European and American options, and the Fundamental Theorem of Asset Pricing.
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"Estimation of models for stock prices." In Mathematical Finance, 168–81. Routledge, 2007. http://dx.doi.org/10.4324/9780203964729.ch9.

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"Estimation of models for stock prices." In Mathematical Finance, 177–90. Routledge, 2007. http://dx.doi.org/10.4324/9780203964729-14.

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"Barrier Options in the BK and Verhulst Models." In Generalized Integral Transforms in Mathematical Finance, 289–308. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/9789811231742_0014.

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"Barrier Options in the Time-Dependent CEV and CIR Models." In Generalized Integral Transforms in Mathematical Finance, 251–87. WORLD SCIENTIFIC, 2021. http://dx.doi.org/10.1142/9789811231742_0013.

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Özel, Gamze. "Stochastic Processes for the Risk Management." In Handbook of Research on Behavioral Finance and Investment Strategies, 188–200. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7484-4.ch011.

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The financial markets use stochastic models to represent the seemingly random behavior of assets such as stocks, commodities, relative currency prices such as the price of one currency compared to that of another, such as the price of US Dollar compared to that of the Euro, and interest rates. These models are then used by quantitative analysts to value options on stock prices, bond prices, and on interest rates. This chapter gives an overview of the stochastic models and methods used in financial risk management. Given the random nature of future events on financial markets, the field of stochastic processes obviously plays an important role in quantitative risk management. Random walk, Brownian motion and geometric Brownian motion processes in risk management are explained. Simulations of these processes are provided with some software codes.
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Conference papers on the topic "Options (Finance) – Prices – Mathematical models"

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Kasparinsky, Felix Osvaldovich. "Complex Indicators of the Multitrading System." In 24th Scientific Conference “Scientific Services & Internet – 2022”. Keldysh Institute of Applied Mathematics, 2022. http://dx.doi.org/10.20948/abrau-2022-14.

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The article summarizes the experience of 4 years of work on setting up the Metatrder 5 Internet terminal tools for a multitrading system, which was developed to increase the effectiveness of price change forecasts in the Forex market by applying a set of interrelated technical analysis indicators to data of different time scales (timeframes), as well as to optimization of work with a variety of financial instruments, with the simultaneous use of multiple accounts. Possible ways of integrating technical and fundamental analysis tools, advantages and disadvantages of using mathematical models to
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