Estimating and Forecasting APARCH‐Skew‐<i>t</i> Model by Wavelet Support Vector Machines

Li, Yushu

doi:10.1002/for.2275

Cited by 12 publications

(9 citation statements)

References 41 publications

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“…Therefore, to examine the contemporaneous relationship between crude oil price volatility and stock markets we use an student- t -AR(1)-APARCH model. This model are also used by researchers like Laurent (2003), Li (2012) and Zhou (2009) as because this model is found to fit the data better than other models and also gives a better result in the forecasting The general structure of student- t -AR(1)-APARCH model is as follows: and where δ> 0 and −1< γ j <1, j =1, 2, … , q .…”

Section: Methodsmentioning

confidence: 99%

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

Bagchi¹

2017

IJOEM

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Purpose The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times. Design/methodology/approach The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects. Findings For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries. Originality/value The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

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Section: Methodsmentioning

confidence: 99%

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

Bagchi¹

2017

IJOEM

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“…This is a type of multilevel case-indexing strategy (Liu & Dong, 2010). SVM is a frequently used model in prediction because it requires fewer assumptions than traditional models (Haerdle, Lee, & Schaefer, 2009;Li, 2014;Rosillo, Giner, & Fuente, 2014). For example, CBR, fuzzy decision trees, and genetic algorithms are first utilized to match the current samples with the clusters; next, decision rules generated from the matched knowledge group can be employed in decision making (Chang, Fan, & Dzan, 2010).…”

Section: The Possible Use Of Supervised Modeling Integrated With Unmentioning

confidence: 99%

“…Empirical research with chemical and biochemical samples of this mechanism (Hancock & Smyth, 2009) reveals the advantage of the supervised tree-based ensembles enhanced by unsupervised knowledge grouping. SVM is a frequently used model in prediction because it requires fewer assumptions than traditional models (Haerdle, Lee, & Schaefer, 2009;Li, 2014;Rosillo, Giner, & Fuente, 2014 3 | REBALANCED AND CLUSTERED SUPPORT VECTOR MACHINE 3.1 | The base function of forecasting the effectiveness of asset restructuring…”

Section: The Possible Use Of Supervised Modeling Integrated With Unmentioning

confidence: 99%

Restructuring performance prediction with a rebalanced and clustered support vector machine

Hong

et al. 2018

Journal of Forecasting

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This paper discusses whether asset restructuring can improve firm performance over decades. Variation in the stock price or the financial ratio is used as the dependent variable of either short‐ or long‐term effectiveness to evaluate the variance both before and after asset restructuring. The result is varied. It is necessary to develop a foresight approach for the mixed situation. This work pioneers to forecast effectiveness of asset restructuring with a rebalanced and clustered support vector machine (RCS). The profitability variation 1 year before and after asset restructuring is used as the dependent variable. The current financial indicators of the year of asset restructuring are used as independent variables. Specially treated listed companies are used as research samples, as they frequently adopt asset restructuring. In modeling, the skew distribution of samples achieving and failing to achieve performance improvement with asset restructuring is handled with rebalancing. The similar experienced knowledge of asset restructuring to the current asset restructuring is filtered out with clustering. With the help from rebalancing and clustering, a support vector machine is constructed for prediction, together with other forecasting models of multivariate discriminant analysis, logistic regression, probit regression, and case‐based reasoning. These models' standalone modes are used as benchmarks. The empirical results demonstrate the applicability of the RCS for forecasting effectiveness of asset restructuring.

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“…The wavelet transformation is widely used in modern prediction or forecasting (Li, 2014;Ortega and Khashanah, 2014;Rua, 2011). Figure 4 shows the low-frequency signal decomposed from the first wind speed time series by bior6.8 (the biorthogonal wavelet 6.8).…”

Section: The Improvement Of Wavelet Denoisingmentioning

confidence: 99%

Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation

Yang

Liu

Zhu

et al. 2016

Journal of Forecasting

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An analytical model has been developed in the present paper based on a square root transformation of white Gaussian noise. The mathematical expectation and variance of the new asymmetric distribution generated by white Gaussian noise after a square root transformation are analytically deduced from the preceding four terms of the Taylor expansion. The model was first evaluated against numerical experiments and a good agreement was obtained. The model was then used to predict time series of wind speeds and highway traffic flows. The simulation results from the new model indicate that the prediction accuracy could be improved by 0.1–1% by removing the mean errors. Further improvement could be obtained for non‐stationary time series, which had large trends. Copyright © 2016 John Wiley & Sons, Ltd.

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Estimating and Forecasting APARCH‐Skew‐t Model by Wavelet Support Vector Machines

Cited by 12 publications

References 41 publications

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

Volatility spillovers between crude oil price and stock markets: evidence from BRIC countries

Restructuring performance prediction with a rebalanced and clustered support vector machine

Removing Forecasting Errors with White Gaussian Noise after Square Root Transformation

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