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Über dieses Buch

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.



Chapter 1. Introduction

Technological advances such as the introduction of the internet and increase of mobile devices have allowed for instant information sharing and consumption around the world. This has led world markets to become integrated, thereby increasing trading activity on the stock exchange by local and foreign investors. The purpose of each trading activity is very much dependent on the agenda of the participant. For instance, stocks can be bought or sold for different reasons such as the readjustment of the hedge position or for simple profit realisation. This random behaviour of the investors introduces a source of uncertainty to the markets that can have adverse effects on the evaluation of portfolio risk exposure. This uncertainty in the market variables is known as market risk. By definition, market risk is the potential loss of value of an asset due to movements in market factors.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 2. Time Series Modelling

The fundamentals of time series analysis consists of a series of realisations of jointly distributed random variables, i.e. \({\text{y}}_{ 1} ,\ldots , {\text{y}}_{N}.\) The subscripts 1, …, N are equally spaced time intervals and the observation are drawn from a probability distribution P.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 3. Options and Options Pricing Models

An option is a contract between two parties, the buyer and seller. The buyer purchases from the seller the right but not the obligation to buy or sell an asset at a fixed price in a given time frame. The buyer has to pay the seller a fee (premium) for the purchase of the option.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 4. Neural Networks and Financial Forecasting

In the 1960s, neural networks were one of the most promising and active areas of research. Neural networks were applied to a variety of problems in the hope of achieving major breakthroughs by discovering relationships within the data.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 5. Important Problems in Financial Forecasting

In this chapter, the problems addressed in this book are defined in a clear and concise manner. We start by defining the terms and concepts used and how they are used throughout the book.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 6. Volatility Forecasting

In Sects. 2.​3 and 4.​2, the common volatility modelling oversights that exist in literature were highlighted. In this Chapter, we discuss the potential impact of these oversights on volatility forecasting and provide a methodology for testing the impact of these oversights on the forecasting accuracy of volatility models. In this chapter, we will carry out the experiments needed to evaluate the methodology and we provide an analysis and discussion of the results.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 7. Option Pricing

In Sect. 7.1, we review several methods for option pricing in research. Specifically, in Sect. 7.1.5, we review Neural Net Methods for options pricing; the strengths and weaknesses of each of the applied methods is discussed.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 8. Value-at-Risk

The increase in volatilities in world markets has made risk management tools a necessity for understanding and controlling risk exposure.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang

Chapter 9. Conclusion and Discussion

Market risk is defined as the potential loss of an asset value due to movements in market factors.
Fahed Mostafa, Tharam Dillon, Elizabeth Chang


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