Research on the relationship between volume and price allows market regulators and policymakers to more accurately assess the trading activity in the market and provide guidance for effective market regulation, trend analysis and decision-making. Therefore, research on the relationship between volume (volume and open interest) and price volatility has important theoretical and practical significance. Following Bessembinder and Seguin (1993), volume and open interest are divided into expected and unexpected components. The basic price-volume relation model, price-volume relation model based on the decomposition of the trading volume and open interest, and the asymmetric model on the price-volume relation to account for the positive and negative realized semi-variance and the different shock of trading volume and open interest to futures market are constructed, and the empirical research is made using high frequency data of Shanghai Futures Exchange copper and aluminum futures. The results show that there is positive correlation between the volatility and trading volume and between relative trading volume, but there is significantly negative correlation between volatility and open interest and between relative open interest in Chinese non-ferrous metals futures market. The expected and unexpected trading volume have positive influence on price volatility. Furthermore, the unexpected trading volume has a bigger influence on price volatility. The expected and unexpected open interest, on the contrary, have negative influence on price volatility. Furthermore, the unexpected open interest has a bigger influence on price volatility. That is to say, the price volatility of non-ferrous metals futures market is mainly induced by unexpected trading volume and unexpected open interest, which represent the new information. The trading volume and open interest have stronger explanatory power in upside realized semi-variance than downward realized semi-variance. The influence of positive trading volume shock on price volatility is bigger than that of negative trading volume shock, which is the same with open interest. The research findings have important reference value for judging and forecasting the financial market trend and risk avoidance.
[1] Karpoff J M. The relation between price changes and trading volume:A survey[J]. Journal of Financial and Quantitative Analysis, 1987, 22(1):109-126.
[2] Mann S C, O'Hara M. market microstructure theory[J]. Journal of Finance, 1997, 51(2):770.
[3] Clark P K. A subordinated stochastic process model with finite variance for speculative prices[J]. Econometrica, 1973,41(1):135-155.
[4] Bessembinder H, Seguin P J. Price volatility, trading volume, and market depth:Evidence from futures markets[J]. Journal of Financial and Quantitative Analysis, 1993, 28(1):21-39.
[5] Chana C C, Fong W M. Realized volatility and transactions[J]. Journal of Banking and Finance, 2006, 30(7):2063-2085.
[6] Lamoureux C G, Lastrapes W D. Heteroskedasticity in stock return Data:Volume versus GARCH effects[J]. Journal of Finance, 1990, 45(1):221-229.
[7] Li Jinliang, Wu Chunchi. Daily return volatility, bid-ask spreads, and information flow:Analyzing the information content of volume[J]. Journal of Business, 2006, 79(5):2697-2739.
[8] Umutlu M, Shackleton M B. Stock-return volatility and daily equity trading by investor groups in Korea[J]. Pacific-Basin Finance Journal, 2014, 34:43-70.
[9] Boudt K, Petitjean M. Intraday liquidity dynamics and news releases around price jumps:Evidence from the DJIA stocks[J]. Journal of Financial Markets, 2014, 17(1):121-149.
[10] Chevallier J, Sévi B. On the volatility-volume relationship in energy futures markets using intraday data[J]. Energy Economics, 2012, 34(6):1896-1909.
[11] Giot P, Laurent S, Petitjean M. Trading activity, realized volatility and jumps[J]. Journal of Empirical Finance, 2010, 17(1):168-175.
[12] Jawadi F, Louhichi W, Cheffou A I, et al. Intraday jumps and trading volume:A nonlinear Tobit specification[J]. Review of Quantitative Finance and Accounting, 2016, 47(4):1-20.
[13] Chan K, Fong W M. Trade size, order imbalance, and the volatility-volume relation[J]. Journal of Financial Economics, 2000, 57(2):247-273.
[14] Kao E H, Fung H G. Intraday trading activities and volatility in round-the-clock futures markets[J]. International Review of Economics and Finance, 2012, 21(1):195-209.
[15] Rajvanshi V. Intraday trading activity and volatility:Evidence from energy and metal futures[J]. Iup Journal of Applied Finance, 2014, 20(2):57-74.
[16] Slim S, Dahmene M. Asymmetric information, volatility components and the volume-volatility relationship for the CAC40 stocks[J]. Global Finance Journal, 2015, 29:70-84.
[17] 王承炜,吴冲锋. 中国股市价格-交易量的线性及非线性因果关系研究[J]. 管理科学学报, 2002,5(4):7-12.
[18] 张维,闫冀楠. 关于上海股市量价因果的实证探测[J]. 系统工程理论与实践, 1998, 18(6):111-114.
[19] 文凤华,刘晓群,唐海如,等. 基于LHAR-RV-V模型的中国股市波动性研究[J]. 管理科学学报, 2012,15(6):59-67.
[20] 郭梁,周炜星. 基于高频数据的中国股市量价关系研究[J]. 管理学报, 2010,7(8):1242-1247.
[21] 黄健柏,程慧,郭尧琦,等. 金属期货量价关系的多重分形特征研究——基于MF-DCCA方法[J]. 管理评论, 2013,25(4):77-85.
[22] 翟爱梅,周彤. 基于市场参与者行为假设的股票市场量价关系研究[J]. 中国管理科学, 2011,19(4):31-37.
[23] 任燕燕,李劭珉. 中国股市收益率与成交量动态关系的研究——基于工具变量的分位数回归(IVQR)模型[J]. 中国管理科学, 2017,25(8):11-18.
[24] 吴吉林. 基于机制转换Copula模型的股市量价尾部关系研究[J]. 中国管理科学, 2012,20(5):16-23.
[25] 石建勋,王盼盼,何宗武. 中国牛市真的是"水牛"吗?——不确定性视角下股市价量关系的实证研究[J]. 中国管理科学, 2017,25(9):71-80.
[26] Girma P B, Mougoué M. An empirical examination of the relation between futures spreads volatility, volume, and open interest[J]. Journal of Futures Markets, 2002, 22(11):1083-1102.
[27] Ripple R D, Moosa I A. The effect of maturity, trading volume, and open interest on crude oil futures price range-based volatility[J]. Global Finance Journal, 2009, 20(3):209-219.
[28] 王杉,宋逢明. 中国股票市场的简单量价关系模型[J]. 管理科学学报, 2006,9(4):65-72.
[29] 田新民,沈小刚. 基于交易量和持仓量的期货日内价格波动研究[J]. 经济与管理研究, 2005,(7):78-80.
[30] Avramov D, Chordia T, Goyal A. The impact of trades on daily volatility[J]. Review of Financial Studies, 2006, 19(4):1241-1277.
[31] Xie Wenjie, Li Mingxia, Xu Haichuan, et al. Quantifying immediate price impact of trades based on the k-shell decomposition of stock trading networks[J]. EPL (Europhysics Letters), 2016, 116(2):28006-p1-28006-p6.
[32] Xu Haichuan, Jiang Zhiqiang, Zhou Weixing. Immediate price impact of a stock and its warrant:Power-law or logarithmic model?[J]. International Journal of Modern Physics B, 2017, 31(8):1750048.
[33] Zhou Weixing, Mu G H, Kertész J. Random matrix approach to the dynamics of stock inventory variations[J]. New Journal of Physics, 2012, 14(9):4420-4425.
[34] Zhou Weixing. Universal price impact functions of individual trades in an order-driven market[J]. Quantitative Finance, 2012, 12(8):1253-1263.
[35] Hansen P R, Lunde A. Consistent ranking of volatility models[J]. Journal of Econometrics, 2006, 131(1-2):97-121.
[36] Barndorff-Nielsen O E, Kinnebrock S, Shephard N. Measuring downside risk-realised semivariance[R]. Research Paper No.2008-42, Center for Research in Econometric Analysis of Times Series, 2008.