Multiscale analysis of high frequency exchange rate time series

Imperial Users onl

Using wavelets for time series forecasting: Does it pay off?

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components. --Forecasting,Wavelets,ARIMA,Denoising,Multiscale Analysis

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model

Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH-) based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models

Long memory in volatility, modelling and forecasting in the wavelet domain

Imperial Users onl

A fresh engineering approach for the forecast of financial index volatility and hedging strategies

This thesis attempts a new light on a problem of importance in Financial Engineering. Volatility is a commonly accepted measure of risk in the investment field. The daily volatility is the determining factor in evaluating option prices and in conducting different hedging strategies. The volatility estimation and forecast are still far from successfully complete for industry acceptance, judged by their generally lower than 50% forecasting accuracy. By judiciously coordinating the current engineering theory and analytical techniques such as wavelet transform, evolutionary algorithms in a Time Series Data Mining framework, and the Markov chain based discrete stochastic optimization methods, this work formulates a systematic strategy to characterize and forecast crucial as well as critical financial time series. Typical forecast features have been extracted from different index volatility data sets which exhibit abrupt drops, jumps and other embedded nonlinear characteristics so that accuracy of forecasting can be markedly improved in comparison with those of the currently prevalent methods adopted in the industry. The key aspect of the presented approach is "transformation and sequential deployment": i) transform the data from being non-observable to observable i.e., from variance into integrated volatility; ii) conduct the wavelet transform to determine the optimal forecasting horizon; iii) transform the wavelet coefficients into 4-lag recursive data sets or viewed differently as a Markov chain; iv) apply certain genetic algorithms to extract a group of rules that characterize different patterns embedded or hidden in the data and attempt to forecast the directions/ranges of the one-step ahead events; and v)apply genetic programming to forecast the values of the one-step ahead events. By following such a step by step approach, complicated problems of time series forecasting become less complex and readily resolvable for industry application. To implement such an approach, the one year, two year and five year S&PlOO historical data are used as training sets to derive a group of 100 rules that best describe their respective signal characteristics. These rules are then used to forecast the subsequent out-of-sample time series data. This set of tests produces an average of over 75% of correct forecasting rate that surpasses any other publicly available forecast results on any type of financial indices. Genetic programming was then applied on the out of sample data set to forecast the actual value of the one step-ahead event. The forecasting accuracy reaches an average of 70%, which is a marked improvement over other current forecasts. To validate the proposed approach, indices of S&P500 as well as S&P 100 data are tested with the discrete stochastic optimization method, which is based on Markov chain theory and involves genetic algorithms. Results are further validated by the bootstrapping operation. All these trials showed a good reliability of the proposed methodology in this research work. Finally, the thus established methodology has been shown to have broad applications in option pricing, hedging, risk management, VaR determination, etc

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Modeling and forecasting financial log-returns as locally stationary wavelet processes

ABSTRACT In this article, we model financial log-return series in the Locally Stationary Wavelet (LSW) framework proposed b