Asymmetric Impact of Oil Price Shock on Stock Market in China: A Combination Analysis Based on SVAR Model and NARDL Model

Hu, Chunyan; Liu, Xinheng; Pan, Bin; Chen, Bin; Xia, Xiaohua

doi:10.1080/1540496x.2017.1412303

Cited by 111 publications

(45 citation statements)

References 59 publications

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“…However, stock price series has the characteristics of complexity, nonlinearity and nonstationarity. [7][8][9][10][11][12][13] Thus, these traditional models often fail to accurately forecast the stock price. In contrast, various arti¯cial intelligence (AI) models have been proposed dependent on the powerful adaptive computer learning and become increasingly popular for stock price prediction, such as arti¯cial neural network (ANN), 14 adaptive network-based fuzzy inference system (ANFIS) 15 and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Xiao

Lee

Huang

et al. 2019

Int. J. Info. Tech. Dec. Mak.

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Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

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Section: Introductionmentioning

confidence: 99%

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Xiao

Lee

Huang

et al. 2019

Int. J. Info. Tech. Dec. Mak.

View full text Add to dashboard Cite

show abstract

“…Authors in [31] proposed a nonlinear ARDL (NARDL) model to detect nonlinear long-run equilibrium relations among variables, which allows long-run and short-run asymmetry in testing variables and is robust to small samples [21]. Many related studies such as [21,[32][33][34][35] all use this method in their empirical studies. Along with this reason, this paper adopts this method to examine the nonlinear long-run relationships between oil prices and two fear gauges (VIX and OVX).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information

2019

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This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between oil prices and OVX (VIX) using the linear autoregressive distributed lag (ARDL)-bounds test. Likewise, while using the nonlinear autoregressive distributed lag (NARDL)-bounds test, not only does a long-run equilibrium relationship exist, but also the rising OVX (VIX) has a greater negative influence on oil prices than the declining OVX (VIX), thus indicating that a long-run, asymmetric cointegration exists between the variables. Furthermore, OVX (VIX) oil prices have a linear Granger causality, while for the nonlinear Granger causality test, oil prices have a bidirectional relation with OVX (VIX). In addition, we find that once major international political and economic events occur, structural changes in oil prices change the behavior of oil prices, and thus panic indices, thereby switching from a linear relationship to a nonlinear one. The empirical results of this study provide market participants with more valuable information.

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“…First, banks are classified into different groups according to their internal characteristics (such as state-owned commercial banks, joint-stock commercial banks, and foreign banks), including some stylized environments [ 49 – 52 ] to estimate a group-specific production frontier for each group. Admittedly, external environments such as the spatial effect of tourism building investments on tourist revenues [ 53 ], the asymmetric impact of oil price shock on the stock market [ 54 ], and portfolio optimization problems [ 55 ] also exert influences on the group division. Second, the metafrontier is estimated by enveloping the group-specific frontiers [ 29 , 56 , 57 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China

Yin

Huang

2018

PLoS ONE

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The performances of different types of banks may vary due to heterogeneous technology, which can be examined by metafrontier analysis. However, the metafrontier constructed in most existing literature is concave, resulting in a biased estimation of efficiency. Based on 93 Chinese commercial banks over the period of 2005–2016, we first evaluate the banking efficiency by using the proposed data envelopment analysis (DEA) model, NCMeta-US-NSBM, which simultaneously incorporates a non-concave metafrontier technique, undesirable outputs, and super efficiency into a network slacks-based measure (NSBM) model. Subsequently, the evolution of banking efficiency during the study period is investigated on the basis of the Dagum Gini index and kernel density estimation methods. The main empirical results show the following. 1) There exists significant disparity/heterogeneity in banking efficiency for overall efficiency, productivity efficiency, and profitability efficiency. 2) The results of the technology gap ratio (TGR) and the evaluation of stated-owned banks (SOB), joint-stock banks (JSB), and city commercial banks (CCB) in the productivity stage are higher than those in the profitability stage, indicating that most of the banks have a large space for improvement, especially for SOB and JSB in the profitability stage. 3) The major contribution of the overall difference of banking efficiency in China is the intensity of the transvariation. 4) Although the kernel density estimations for different efficiency scores have similar distributions in corresponding years, the multilevel differentiation phenomenon of banking efficiency may appear after 2008.

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Asymmetric Impact of Oil Price Shock on Stock Market in China: A Combination Analysis Based on SVAR Model and NARDL Model

Cited by 111 publications

References 59 publications

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information

Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China

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