01488nas a2200181 4500008004100000245011200041210006900153260000900222300001600231490000700247520082500254653002301079100001801102700001401120700001901134700001501153856013801168 2020 eng d00aRealized Volatility Forecasting and Volatility Spillovers: Evidence from Chinese Non-Ferrous Metals Futures0 aRealized Volatility Forecasting and Volatility Spillovers Eviden c2020 a2713–27310 v263 aWe study the prediction of realized volatility of non-ferrous metals futures traded on the Shanghai Futures Exchange from March 2011 to December 2017. A dynamic model averaging model is employed to combine multiple prediction models using time-varying weights based on individual model performance. Empirical results also reveal that models incorporating volatility spillovers across metals are important for forecast combinations, and short-term spillovers have a stronger impact than long-term spillovers. This approach offers the best forecasting performance and allows users to identify the most dominant model at any given time and demonstrate when and how volatility transmission from another metal is valuable for forecasting. We also find evidence of distinct trading behaviors in emerging and developed markets.10aBusiness Analytics1 aWang, Donghua1 aXin, Yang1 aChang, Xiaohui1 aSu, Xingze uhttps://business.oregonstate.edu/biblio/realized-volatility-forecasting-and-volatility-spillovers-evidence-chinese-non-ferrous-metals01377nas a2200181 4500008004100000245007600041210006900117260000900186300001600195490000700211520077900218653002300997100001801020700001701038700001901055700001601074856010501090 2017 eng d00aThe Lead-Lag Relationship between the Spot and Futures Markets in China0 aLeadLag Relationship between the Spot and Futures Markets in Chi c2017 a1447–14560 v173 aBased on daily and one-minute high-frequency returns, this paper examines the
lead-lag dependence between the CSI 300 index spot and futures markets from 2010 to 2014. The
nonparametric and nonlinear thermal optimal path method is adopted. Empirical results of the
daily data indicate that the lead-lag relationship between the two markets is within one day but
this relationship is volatile since neither of the two possible situations (the futures leads or lags
behind the spot market) takes a dominant place. Besides, our results from high-frequency data
demonstrate that there is a price discovery in the Chinese futures market: the intraday one-minute
futures return leads the cash return by 0~5 minutes regardless of the price trend of the market.10aBusiness Analytics1 aWang, Donghua1 aTu, Jingqing1 aChang, Xiaohui1 aLi, Saiping uhttps://business.oregonstate.edu/biblio/lead-lag-relationship-between-spot-and-futures-markets-china02960nas a2200181 4500008004100000245015600041210006900197260000900266300001200275490000600287520226000293653002302553100001802576710001802594700001302612700001902625856013402644 2015 eng d00aDynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform: New evidence from a two-state Markov-switching approach0 aDynamic relation of Chinese stock pricevolume pre and post the S c2015 a386-4010 v53 aPurpose – The purpose of this paper is to identify the bull and bear regimes in Chinese stock market and empirically analyze the dynamic relation of Chinese stock price-volume pre- and post- the Split Share Structure Reform.

Design/methodology/approach – The authors investigate the price-volume relationship in the Chinese stock market before and after the Split Share Structure Reform using Shanghai Composite Index daily data from July 1994 to April 2013. Using a two-state Markov-switching autoregressive model and a modified two-state Markov-switching vector autoregression model, this study identifies bull or bear market and also examine the existence of regime-dependent Granger causality.

Findings – Using a two-state Markov-switching autoregressive model, the authors detect structural changes in the market volatility due to the reform, and find evidence of a positive rather than an asymmetric price-volume contemporaneous correlation. There is a strong dynamic Granger causal relation from stock returns to trading volume before and after the reform regardless of the market conditions, but the causal effects of volume on returns are only seen in the bear markets before the reform. The model is robust when using different stock indices and time periods.

Originality/value – The work is different from previous studies in the following aspects: most of the existing empirical literature focus on the well-developed economies, but our interest lies in the emerging Chinese market that has witnessed rapid growth in the past decade; in contrast to many works in the literature that examine the price-volume relationship during one market condition, the authors compare the relationship in a bull market with that in a bear market, using a two-state MS-AR model; the authors also employ a modified two-state Markov-switching vector autoregression model to examine the existence of regime-dependent Granger causality; as the most massive systematic reform for the Chinese stock market since its inception in 2005, the Split Share Structure Reform has a profound impact on the Chinese stock market, thus it is of vital importance to explore its effects on both the price-volume relationship and the market structure.10aBusiness Analytics1 aWang, Donghua1 aEmptyAuthNode1 aLei, Man1 aChang, Xiaohui uhttps://business.oregonstate.edu/biblio/dynamic-relation-chinese-stock-price-volume-pre-and-post-split-share-structure-reform-new