Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression

By Lili Li, S. Leng, Jun Yang, Meihua Yu. 2016

37 citations recorded by the source corpus.

We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7) and China. The quantile autoregression model (QAR) enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, methodology): “One of the most widely used model in this direction is the Autoregressive (AR) model for linear and stationary time-series [15].”
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “For example, [17] applied the quantile AR model to analyze the dynamics of stock index returns in China; [31] decomposed the stock return into a couple of components and utilized the AR model to make prediction.”