Download PDF Computational Intelligence Applications to Option Pricing, Volatility Forecasting and Value at Risk

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Financial market volatility forecasting is one of today's most important areas Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single Sold by Amazon. Sold by Amazon Marketplace.

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Calvet and Fisher present a powerful, new technique for volatility forecasting Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and mathematics and provides a unified treatment of the use of multifractal techniques in This book demonstrates the power of neural networks in learning complex behavi 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 See it. Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and In the aftermath of the Financial crisis the volatility market started to have In the aftermath of the Financial crisis the volatility market started to have an important role in the financial panorama.

Indeed, hedge fund and Investment banks decided to create desks to deal with this issue. The aim of this book is to analyse the This book is a contribution to the knowledge concerning the volatility and for This book is a contribution to the knowledge concerning the volatility and forecasting of exchange rates in the emerging markets. It focuses on the exchange rates of the leading trading blocs in that part of the world and examines exchange rates of Our model takes predictors, or inputs, and outputs the daily price change of Bitcoin and attempts to learn a pattern from all the data.

It continues to test its patterns until it reaches an optimal point where further testing is redundant.

Original Research ARTICLE

These advanced models form the backbone of many AI learning programs that are used in business and engineering. By combining Bitcoin technical analysis and neural networks, we hoped that the ANN would find a pattern among the data that allowed us to more accurately predict future returns. Our ANN model did indeed succeed in reducing the prediction error of the random walk by about five to 10 per cent over the full observation period.

These forecast improvements are statistically significant, indicating that predicting Bitcoin prices on a daily basis is no longer guesswork.

PriceGrabber - Forecasting volatility roles sampling frequency Business & Economics Books

Our results show that Bitcoin is unaffected by how the stock market changes, which suggests that traditional market investors and investors in Bitcoin are two distinct groups. We also separated the data into four subsamples of similar time frames to further zoom in on market inefficiencies. One subsample, running from October to June , provided the best results of the study. The isolated day signal model outperformed the random walk by We noted that this subsample had low volatility compared to the other three subsamples and was the steadiest period of data we observed.


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In essence, greater market volatility makes learning data patterns and training of the ANN model more difficult. Along with price accuracy, we also observed how often our ANN models correctly predicted whether prices would increase or decrease. Our main comprehensive model over the entire period had nearly 63 per cent prediction accuracy. Put differently, Bitcoin trading with our model would be on average more profitable than placing random buy and sell orders that have a 50 per cent chance of making a profit.

2015 - FRM : VAR Methods Part I (of 2)

Compared to other predictive models, our ANN provided the most accurate and reliable predictive method for Bitcoin. We concluded that the historical evolution of daily Bitcoin prices followed predictive trends or bubbles that potentially arise from the speculative nature of cryptocurrency trading. Read more: How low will Bitcoin now go?

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The history of price bubbles provides some clues. We believe that the future of forecasting Bitcoin — and perhaps investing in general — lies in the abilities of artificial intelligence and artificial neural networks. While people may argue over the merits of Bitcoin as a currency, we can at least appreciate it as a fascinating — and now easier-to-predict — commodity.

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