867 research outputs found
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
Modeling and forecasting exchange rate volatility in time-frequency domain
This paper proposes an enhanced approach to modeling and forecasting
volatility using high frequency data. Using a forecasting model based on
Realized GARCH with multiple time-frequency decomposed realized volatility
measures, we study the influence of different timescales on volatility
forecasts. The decomposition of volatility into several timescales approximates
the behaviour of traders at corresponding investment horizons. The proposed
methodology is moreover able to account for impact of jumps due to a recently
proposed jump wavelet two scale realized volatility estimator. We propose a
realized Jump-GARCH models estimated in two versions using maximum likelihood
as well as observation-driven estimation framework of generalized
autoregressive score. We compare forecasts using several popular realized
volatility measures on foreign exchange rate futures data covering the recent
financial crisis. Our results indicate that disentangling jump variation from
the integrated variation is important for forecasting performance. An
interesting insight into the volatility process is also provided by its
multiscale decomposition. We find that most of the information for future
volatility comes from high frequency part of the spectra representing very
short investment horizons. Our newly proposed models outperform statistically
the popular as well conventional models in both one-day and multi-period-ahead
forecasting
The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets
This paper proposes a powerful methodology wavelet networks to investigate the effects of international F/X markets on emerging markets currencies. We used EUR/USD parity as input indicator (international F/X markets) and three emerging markets currencies as Brazilian Real, Turkish Lira and Russian Ruble as output indicator (emerging markets currency). We test if the effects of international F/X markets change across different timescale. Using wavelet networks, we showed that the effects of international F/X markets increase with higher timescale. This evidence shows that the causality of international F/X markets on emerging markets should be tested based on 64-128 days effect. We also find that the effects of EUR/USD parity on Turkish Lira is higher on 17-32 days and 65-128 days scales and this evidence shows that Turkish lira is less stable compare to other emerging markets currencies as international F/X markets effects Turkish lira on shorten time scale.F/X Markets; Emerging markets; Wavelet networks; Wavelets; Neural networks
Can social microblogging be used to forecast intraday exchange rates?
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under
certain assumptions. One of its implications is that the prediction of stock
prices at least in the short run cannot outperform the random walk model. Yet,
recently many studies stressing the psychological and social dimension of
financial behavior have challenged the validity of the EMH. Towards this aim,
over the last few years, internet-based communication platforms and search
engines have been used to extract early indicators of social and economic
trends. Here, we used Twitter's social networking platform to model and
forecast the EUR/USD exchange rate in a high-frequency intradaily trading
scale. Using time series and trading simulations analysis, we provide some
evidence that the information provided in social microblogging platforms such
as Twitter can in certain cases enhance the forecasting efficiency regarding
the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The
final publication is available at
http://www.springer.com/economics/economic+theory/journal/1106
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
Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression
Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations
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