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 16 of 16 for “"Quantitative finance"”.
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Applications of machine learning in finance: analysis of international portfolio flows using regime-switching models
… explores applications of machine learning in quantitative finance through two approaches. The current state of the art is evaluated through an extensive review of recent quantitative finance literature. Themes and technologies are identified and classified, and the key use cases highlighted …
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An examination and implementation of the libor market model
The relatively young field of quantitative finance has grown over the past thirty years with the cherry-picking of a wide variety of techniques from the disciplines of finance, mathematics and computer science. The Libor Market Model, a model for pricing and risk-managing interest rate derivatives, …
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Gaussian Process Regression for a Single Underlying Autocallable Security
Traditionally in Quantitative Finance, in order to price exotic options, particu- larly with path dependency, time consuming Monte Carlo simulations are done. This dissertation considers the use of the machine learning technique Gaussian Process Regression (GPR) as a faster pricing alternative to …
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Gaussian Process Regression for Option Pricing and Hedging
Recent literature in the field of quantitative finance has employed machine learning methods to speed up typical numerical calculations including derivative pricing, fitting Greek profiles, constructing volatility surfaces and modelling counterparty credit risk, to name a few. This dissertation …
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Stochastic Homogenization of Nonconvex Hamilton-Jacobi Equations in One Dimension
… various fields such as optimal control theory, quantitative finance, and game theory. Stochastic homogenization is a phenomenon used to study the behavior of solutions to partial differential equations in stationary ergodic media, aiming to understand how these solutions average out or …
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Long-memory stochastic volatility model calibration using deep neural nets
… they present. Since the seminal work in quantitative finance by Black et al. and Merton, the infamous Black-Scholes model has been extensively used in the industry for vanilla and exotic option pricing. Although the model assumes constant volatility which is not observed in the market, …
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High frequency trading system design and process management
… and manufacturing industries. However, the finance industry has not yet fully adopted high-standard systems engineering frameworks and process management approaches that have been successful in the software and manufacturing industries. Many of the traditional methodologies for product …
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Essays in volatility research
… written during my time as a Ph.D. candidate in Finance at Lancaster University and as a visiting Ph.D. scholar at the Econometrics Department of the University of Amsterdam. This Ph.D. thesis starts with an introduction to finance for a general audience. Followed by an extensive literature …
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Parallelizing Tree Traversals for Binomial Option Pricing
Quantitative finance analysts and software developers often need 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, …
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A Real Options Valuation of Renewable Energy Projects
… thesis we use well developed theory taken from quantitative finance, more specifically real options theory, as well as various mathematical and statistical techniques and models used in option pricing to determine the economic value of these resources. Market design and policy are key …
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Deep Learning for data analysis on specific contexts (Automotive, Medical Imaging)
… we reported our findings in the aerobiology and quantitative finance domains, where Deep Learning approaches have been applied for solving many complex tasks.
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High performance digital signal processing: Theory, design, and applications in finance
… findings are applied to well-known problems in quantitative finance (QF). First, an efficient method to derive the explicit KLT kernel for AR(1) processes that utilizes a simple root finding method for the transcendental equations is introduced. Performance improvement over a popular numerical …
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Adaptation of the MapReduce programming framework to compute-intensive data-analytics kernels
… some high performance computing problems, quantitative finance and simulation among others. These computational problems deal with massive data sets, and require performing lots of computation per data element. This thesis presents a vision of CIDA applications programmed in a MapReduce …
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Advances in Risk Parity Portfolio Optimization
Risk parity is an asset allocation strategy that seeks to equalize the risk contributions of the constituent assets in a portfolio. The resulting portfolio is fully diversified from a risk perspective. However, like other asset allocation strategies, risk parity is susceptible to estimation errors. …
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Quantum Computing: analysis and design of quantum algorithms for financial applications
L'abstract è presente nell'allegato / the abstract is in the attachment
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Novel Invariant Features of the US Stock Market
Price fluctuations in financial markets are influenced by a multitude of economic, societal, and other factors. Rather than attempt to understand the cause and effect of such myriad complexities, we use traditional tools of physics and mathematics in addressing large systems. Specifically, we model …