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 49 for “"financial time series"”.
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Multiresolution models of financial time series
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Learning connections in financial time series
Much of modern financial theory is based upon the assumption that a portfolio containing a diversified set of equities can be used to control risk while achieving a good rate of return. The basic idea is to choose equities that have high expected returns, but are unlikely to move together. …
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Nonparametric Methods in Financial Time Series Analysis
The fundamental objective of the analysis of financial time series is to unveil the random mechanism, i.e. the probability law, underlying financial data. The effort to identify the truth that governs the observations involves proposing and estimating reasonable statistical models that well explain …
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Studies on break detection in financial time series volatility
… the volatility persistence and/or long memory in financial time series. In the second chapter a Monte Carlo simulation experiment it is employed to examine the performance of a CUSUM type statistic for break detection. In particular, we study the statistical properties of a non-parametric approach …
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Modelling financial time series using discrete and continuous paradigms
… models, to describe the fluctuation of prices of financial assets. First, we shall concentrate on the multiplicative binomial model, where, after each succeeding period, the price can either increase or decrease by certain amounts and with certain probabilities. Subsequently, we generalise the …
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Higher order neural networks for financial time series prediction
… shown to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so …
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Modelling and valuing multivariate interdependencies in financial time series
… stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in …
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Online Non-linear Prediction of Financial Time Series Patterns
… machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows …
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Applications of Deep Learning to Financial Time Series Forecasting
… high-dimensional data. In the particular case of financial modeling, one of the most important data analysis problems consists of predicting the future volatility of a given asset. In this thesis, we investigate how the Transformer architecture performs at the task of volatility forecasting by …
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Modelling signal interactions with application to financial time series
… of reasoning over a set of objects evolving over time that are coupled through interaction structures that are themselves changing over time. We focus on inferring time-varying interaction structures among a set of objects from sequences of noisy time series observations with the caveat that the …
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On the analysis tools of turbulent and financial time series
… on some of the important tools used in analyzing time series. Since turbulence turned out to be a rather complex phenomenon, a variety of different models and analysis tools have been devised to simulate, analyze and address the different questions that are raised by the behaviour of these …
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Essays on forward portfolio theory and financial time series modeling
… to the problem of optimal portfolio choice and financial time series analysis. The first essay presents turnpike-type results for the risk tolerance function in an incomplete Ito-diffusion market setting under time-monotone for- ward performance criteria. We show that, contrary to the classical …
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Machine Learning-Driven Decision Making based on Financial Time Series
L'abstract è presente nell'allegato / the abstract is in the attachment
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A radial basis function approach to financial time series analysis
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Jump detection tests in financial time series ? a deep learning approach
In most financial market models, the asset price is driven by continuous Brownian motion. An additional complexity to such a model is the inclusion of a discontinuous jump process. Jumps are theorised to be rare, sudden, and thought to be the result of the market reacting to new information. Jump …
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Identifying jumps in financial time series: a comparative study of jump detection tests
There is consensus in the financial literature that traded asset prices may be subject to rare, but sudden movements, resulting in asset price discontinuities, known as jumps. It is therefore important to not only incorporate jumps into diffusion models but also to disentangle the diffusion …
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Empirical Analysis of Neural Architectures and Side Information in Financial Time Series Forecasting
… predictive capabilities of neural networks in financial time series forecasting, focusing on predicting the weekly close price of the SPY index. We explore the integration of options-derived features alongside traditional price data, compare recurrent architectures and transformer-based models, …
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DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION
… networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables …
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Optimization Of The GARCH Model Parameters Using A Genetic Algorithm
<p>Financial time series are often characterized by nonlinearity and volatility bunching. Standard regression analysis models cannot capture changing volatilities, potentially leading to erroneous results. The need to more completely model the characteristic volatilities inherent to financial time …
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