Application of ARCH-GARCH Models and Feed-Forward Neural Networks with Bayesian Regularization in Capital Asset Pricing Model PDF Download

Author: Eleftherios Giovanis
Publisher:
ISBN:
Category :
Languages : en
Pages : 15

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Book Description
In this paper we apply CAPM model in the financial time series of the share prices of Technology-Software Sector in Athens Exchange stock market for the period January 1st of 2002 to June 30th of 2008 for the enterprises quot;Dionicquot; and quot;Coca-Colaquot;. In one stock we apply the OLS method and in the other we apply the a GARCH model because of heteroscedasticity presence. In the following phase we simulate the forecasting results with feed-forward neural networks and we compare the new forecasts with that we obtain by OLS and GARCH estimation. The forecasting period is July 1st to July 24th of 2008.

Author: Stefan Nagel
Publisher: Princeton University Press
ISBN: 0691218706
Category : Business & Economics
Languages : en
Pages : 156

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Book Description
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

Author: Evdokia Xekalaki
Publisher: John Wiley & Sons
ISBN: 0470688025
Category : Mathematics
Languages : en
Pages : 561

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Book Description
Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented. Provides step-by-step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models. Accompanied by a CD-ROM containing links to the software as well as the datasets used in the examples. Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.

Author: S. G. Hall
Publisher:
ISBN:
Category : Capital
Languages : en
Pages : 44

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Book Description

Author: Fahed Mostafa
Publisher: Springer
ISBN: 331951668X
Category : Technology & Engineering
Languages : en
Pages : 177

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Book Description
This book demonstrates the power of neural networks in learning complex behavior from the underlying financial time series data. The results presented also show how neural networks can successfully be applied to volatility modeling, option pricing, and value-at-risk modeling. These features mean that they can be applied to market-risk problems to overcome classic problems associated with statistical models.

Author: Eleftherios Giovanis
Publisher:
ISBN:
Category :
Languages : en
Pages : 19

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Book Description
We examine two stocks of Athens Exchange Stock Market, that of 'Coca-Cola' and 'Compucon'. We analyze the arbitrage pricing theory (APT) model and the Capital Asset Pricing Model (CAPM) and we compare the performance between them. Then we develop a neural network model in Synapse Software with the particle swarm optimization algorithm and show the flexibility of hybrid models and the Synapse software, as the superiority in forecasting performance, in relation to the traditional econometric methodology , like Ordinary least square and ARCH-GARCH estimations.

Author: Melike Bildirici
Publisher:
ISBN:
Category :
Languages : en
Pages : 14

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Book Description
The Turkish version of this paper can be found at: "http://ssrn.com/abstract=2222071" http://ssrn.com/abstract=2222071The study aims to investigate linear GARCH, fractionally integrated FI-GARCH and Asymmetric Power APGARCH models and their nonlinear counterparts based on Support Vector Regression (SVR) and Neural Network (NN) models. GARCH family models are extended to NN-GARCH architecture of Donaldson and Kamstra (1997) to various NN-GARCH family models (Bildirici and Ersin, 2009) such as NN-APGARCH model. The study aims to introduce a class of extended NN-GARCH and SVR-GARCH family of models with nonlinear augmentations in modeling both the conditional mean and variance. The SVR-GARCH, SVR-APGARCH and SVR-FIAPGARCH and their Multi-Layer Perceptron architecture based counterparts, MLP-GARCH, MLP-APGARCH and MLP-FIAPGARCH are evaluated. An application to daily returns in Istanbul ISE100 stock index is provided. Results suggest that volatility clustering, asymmetry and nonlinearity characteristics are modeled more efficiently with the models possessing neural network architectures.

Author: Marcus Bendtsen
Publisher: Linköping University Electronic Press
ISBN: 9176855252
Category :
Languages : en
Pages : 245

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Book Description
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.

Author: Achilleas Zapranis
Publisher: Springer Science & Business Media
ISBN: 1447105591
Category : Computers
Languages : en
Pages : 194

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Book Description
Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly stochastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.

Author: Radford M. Neal
Publisher: Springer
ISBN: 9780387947242
Category : Mathematics
Languages : en
Pages : 0

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Book Description
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.