financial time series modelling
Financial time series modelling with hybrid model based on customized RBF neural network combined with genetic algorithm
... In this paper, forecasts of USD/CAD exchange rate through customized RBFGA neural network with Mov- ing Average errors (RBFGA-SMA) was performed The standard RBF model will be extended by using Moving Average for ...
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Financial Time Series Modelling of Trends and Patterns in the Energy Markets
... employed time series [3] [4], financial [5] and structural models [6] in forecasting crude oil ...prices. Financial studies involve asset returns [7]-[9], instead of prices, since, firstly, ...
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Feed forward neural networks and genetic algorithms for automated financial time series modelling
... Formal and applied methods are investigated for combining feed-forward Neural Networks and Genetic Algorithms (GAs) into a single adaptive/learning system for automated time series forec[r] ...
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Essays in Financial Time Series
... memory dynamics might be presented due to the non-linearity of the conditional variance process, and an integrated conditional heteroskedastic model could induce a spurious estima- tion under the constant unconditional ...
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Neural Network Modelling With Discrete Wavelet Transform Pre-Processing for Financial Time Series Data
... Abstract — This paper discuss about Feed Forward Neural Network (FFNN) modelling by using the discrete wavelet transform (DWT) as pre-processing to the input and target. Before the training to the FFNN be done, ...
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Modelling and Analysis on Noisy Financial Time Series
... of time series has at- tracted much attention from statistical and machine learning perspectives [1,2], with a variety of applications in different fields ...analyzing financial time ...
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Essays on Forecasting Financial and Economic Time Series
... and/or modelling time-varying volatility improve predictive performance of Nelson-Siegel ...forecasting time series under structural ...a time series in the presence of ongoing ...
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Concepts and tools for nonlinear time series modelling
... Whereas forecasting conditional volatility is fairly straightforward, evaluat- ing the forecasting performance is a more challenging task. Important con- tributions in assessing the correctness of out-of-sample ...
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Modelling financial time series with SEMIFAR GARCH model
... A class of semiparametric fractional autoregressive GARCH models (SEMIFAR- GARCH), which includes deterministic trends, difference stationarity and station- arity with short- and long-range dependence, and ...
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Time-series modelling in financial markets : new approaches and exchange rate applications
... where d t , i = 1,..., p+1 are diagonal block entries of the block diagonal matrix resulting from a block Choleski decomposition for Cp+i. This outcome indicates that in the course of computing Vp for the VAR (p) model, ...
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Testing extreme dependence in financial time series
... initial time for a period of 4 weeks are shown in Figure 6, Figure 7, and in Figure 8, with a moving window of 3 years (about 156 data points) and 4 years (about 208 data points), and step of one month (about 4 ...
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Time series modelling of the Kobe Osaka earthquake recordings
... 3. AR models with time-dependent error variance. Let (Ω,β,p) denote a probabil- ity space and let ᏸ (·) = ᏸ (Ω,β,p) denote the space of all real-valued random variables on (Ω,β,p) with zero mean and finite ...
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Cointegration Analysis of Financial Time Series Data
... of time series’, thus, data points/ values of a time series are equivalent to price ...price series’ in the financial markets as most of them follow somewhat a random walk - a topic that ...
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Nonlinear time series analysis in financial applications
... of financial theory, has two competing economic explanations - the "leverage effect" hypothesis and the "volatility feedback effect" ...over time (which has been documented extensively in ...
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Modelling multiple time series via common factors
... We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The ...
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Modelling comovements of economic time series: a selective survey
... The presence of short-run comovements has also consequences for the VMA part of FEs. Cubadda et al. (2009) show that in a stationary VAR(p), the existence of s SCCFs implies that in the FEs the VMA coefficient matrices ...
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Statistical modelling of agrometeorological time series by exponential smoothing
... logical time series include analyses of simple exponential smoothing, random walk, moving average, autoregressive integrated moving average (ARIMA), and artificial intel- ligence techniques such as Fuzzy ...
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A Hybrid Approach for Modeling Financial Time Series
... forecasting time series in financial ...for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced ...models. Time ...
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Performance evaluation of series and parallel strategies for financial time series forecasting
... in financial time series forecasting is ...on time series forecasting and modeling widely use a combination of different models and metaheuristic optimization ...on time ...
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