Computational intelligence applications to option pricing, volatility forecasting and value at risk
The book describes how to deal with the different sorts of financial market risk. In the book, market risk is grouped into four main categories: volatility forecasting, option pricing, hedging and portfolio risk management. When developing these quantitative methods, the focus in the book has been twofold: first, to build on the existing methodologies such as the GARCH and Black-Scholes models and second to develop approaches to overcome the disadvantages inherent in the models arising from the underlying assumptions which have been found to not properly reflect the behaviours inherent in the markets. The book consists of 9 chapters. Chapter 1 provides a broad introduction to some of the important concepts involved in market risk. Time series models are reviewed in Chapter 2. The weakness of each of the modelling techniques is highlighted and explained with reference to research. Chapter 3 introduces options, existing option-pricing models and hedging, Chapter 4 provides a review of neural networks. Chapter 5 outlines important problems in financial forecasting including volatility forecasting, options pricing and hedging. Volatility forecasting models are considered and evaluated in Chapter 6 including the GARCH, EGARCH and mixture density models. Chapter 7 considers option-pricing models including GARCH option-pricing model (GOPM), BSOPM model, implied volatility and existing neural net models. Value-at-risk is considered in Chapter 8 including definitions and models, and Chapter 9 provides a recapitulation and conclusions. The book can be used by advanced undergraduate students and graduate students in its entirety. It is also interesting for the specialists in financial market risk and is of considerable importance to practitioners in the field.
GARCH, EGARCH and mixture density models
scientific article; zbMATH DE number 6816564