Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 135 for “"Option pricing"”.
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Towards efficient nonlinear option pricing
… numerical solvers for a wide range of nonlinear option pricing problems, including European options, Asian options and a multi-asset case. The chosen research methodology is the numerical PDE approach which essentially is to solve the nonlinear Black-Scholes equations with the relevant nonlinear …
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Sensitivities in Option Pricing Models
… of determining the unknown parameters of the pricing equation from the values quoted from the market. We formulate the inverse problem as a minimization problem for an appropriate cost function to minimize the difference between the solution of the model and the market observations. Efficient …
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Option Pricing in Non-Competitive Markets
In the classic option pricing theory, the market is assumed to be competitive. The relaxation of the competitive market assumption introduces two features: liquidity cost and feedback effects. In our study, investors in non-competitive markets are divided into two categories: small investors and …
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Essays on Banking and Option Pricing
… procedure to estimate state-price densities from option prices. The existing nonparametric kernel regression estimator in Ait-Sahalia and Lo (1998) does not satisfy a requirement of a probability density function: that it be non-negative on its domain. In this paper, we implement a one-step …
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Gram-Charlier expansions and option pricing
… is implemented with standard foreign exchange options and gives an exact fit when enough moments are included in the calibration process. GramCharlier expansions also result in analytic solutions for many exotic option prices through an extremely general framework. This relies on representing …
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Deep Calibration of Option Pricing Models
… involves a direct inversion of the standard option pricing function using neural network. The indirect framework uses two consecutive steps; the first step estimates the option pricing function using a neural network. This is followed by applying the pre-trained model in a calibration …
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Option pricing with non-constant volatility
… researchers have developed models to price options with non-constant asset price volatility. These models can be divided into deterministic volatility models and stochastic volatility models. Deterministic volatility models assume that volatility is determined by some variables observable in …
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Option pricing using hidden Markov models
This work will present an option pricing model that accommodates parameters that vary over time, whilst still retaining a closed-form expression for option prices: the Hidden Markov Option Pricing Model. This is possible due to the macro-structure of this model and provides the added advantage of …
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Option pricing in a path integral framework
… is an examination of methods for computing an option price using a path integral framework. The framework, developed by Chiarella, El-Hassan and Kucera, is based on the Black and Scholes paradigm. The path integral is backward recursive with the payoff known at expiry and has no closed form …
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No-Arbitrage Option Pricing with Neural SDEs
… and CEV models, using European call option prices computed from neural SDE generated stock prices. The numerical experiment results suggest that neural SDEs are a promising tool for understanding the behaviour of complex, dynamic systems, and may offer improved accuracy and …
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Finite activity jump models for option pricing
This thesis aims to look at option pricing under affine jump diffusion processes, with particular emphasis on using Fourier transforms. The focus of the thesis is on using Fourier transform to price European options and Barrier options under the Heston stochastic volatility model and the Bates …
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Parallelizing Tree Traversals for Binomial Option Pricing
… to develop efficient software implementations of pricing models, hedging tools, and other financial algorithms in order to support their research. Some of the most commonly used quantitative analysis tools include binomial trees, which are useful to represent possible future model states and …
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Robust option pricing : An [epsilon]-arbitrage approach
… aims to provide tractable approaches to price options using robust optimization. The pricing problem is reduced to a problem of identifying the replicating portfolio which minimizes the worst case arbitrage possible for a given uncertainty set on underlying asset returns. We construct …
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Essays on Portfolio Optimization, Simulation and Option Pricing
… cover the efficient Monte Carlo simulation in option pricing, the application of realized volatility in trading strategies and geometrical analysis of a four asset mean variance portfolio optimization problem. The first paper studies different efficient simulation methods to price options with …
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Option Pricing models with Stochastic Volatility and Jumps
Exotic equity options are specialized instruments which are typically traded over the counter. Their prices are primarily determined by option pricing models which should be able to price exotic options consistently with the market prices of corresponding vanilla options. Additionally, option …
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Gaussian Process Regression for Option Pricing and Hedging
… numerical calculations including derivative pricing, fitting Greek profiles, constructing volatility surfaces and modelling counterparty credit risk, to name a few. This dissertation aims to investigate the accuracy and efficiency of Gaussian process regression (GPR) compared to traditional …
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Option pricing with physics-informed neutral networks (PINNS)
… of physics-informed neural networks (PINNs) to option pricing. PINNs are neural networks that are trained to numerically solve partial differential equations (PDEs) by obeying the dynamics induced by the PDE as well as the initial/terminal conditions of the PDE. They are mesh-free to an extent …
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An option pricing model for R&D projects
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.
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Calibration of Option Pricing in Reproducing Kernel Hilbert Space
… problem known as the calibration problem for option pricing. This problem is shown to be ill-posed. We propose a regularization method and reformulate our calibration problem as a problem of finding the local volatility in a reproducing kernel Hilbert space. We defined a new volatility …
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