# Intraday price information flows between the CSI300 and futures market: an application of wavelet analysis

Article

First Online:

Received:

Accepted:

- 79 Downloads

## Abstract

This study investigates linear and nonlinear price information flows between the Chinese Stock Index 300 (CSI300) and futures market using high-frequency data and their wavelet transformed series for three regimes for which stock short-selling restrictions in China are different. Empirical results generally indicate information feedback between these two markets regardless of assumptions of linear and nonlinear causality and regimes for original series and wavelet transformed data at different scales.

## Keywords

CSI300 Spot Futures Information flow Causality Wavelet method## JEL Classification

C32 G13 G14## References

- Almasri A, Shukur G (2003) An Illustration of the causality relation between government spending and revenue using wavelets analysis on finnish data. J Appl Stat 30:571–584. doi: 10.1080/0266476032000053682 CrossRefGoogle Scholar
- Alzahrani M, Masih M, Al-Titi O (2014) Linear and non-linear Granger causality between oil spot and futures prices: a wavelet based test. J Int Money Financ 48:175–201. doi: 10.1016/j.jimonfin.2014.07.001 CrossRefGoogle Scholar
- Beck SE (1994) Cointegration and market efficiency in commodities futures markets. Appl Econ 26:249–257. doi: 10.1080/00036849400000006 CrossRefGoogle Scholar
- Baek EG, Brock WA (1992) A general test for nonlinear granger causality: bivariate model. Working paper, Korea Development Institute and Iowa State University, and University of Wisconsin-MadisonGoogle Scholar
- Bekiros SD, Diks CGH (2008) The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality. Energy Econ 30:2673–2685. doi: 10.1016/j.eneco.2008.03.006 CrossRefGoogle Scholar
- Bohl MT, Salm CA, Schuppli M (2011) Price discovery and investor structure in stock index futures. J Futures Mark 31:282–306. doi: 10.1002/fut.20469 CrossRefGoogle Scholar
- Brock WA, Scheinkman JA, Dechert WD, LeBaron B (1996) A test for the independence based on the correlation dimension. Econ Rev 15:197–235. doi: 10.1080/07474939608800353 CrossRefGoogle Scholar
- Chowdhury AR (1991) Futures market efficiency: evidence from cointegration tests. J Futures Mark 11:577–589. doi: 10.1002/fut.3990110506 CrossRefGoogle Scholar
- Dalkir M (2004) A new approach to causality in the frequency domain. Econ Bull 44:1–14Google Scholar
- Daubechies I (1992) Ten lectures on wavelets. In: CBMS-NSF regional conference series in applied mathematics, vol 61, Society for Industrial and Applied Mathematics, Philadelphia. doi: 10.1137/1.9781611970104
- Davidson R, Labys WC, Lesourd JB (1998) Wavelet analysis of commodity price behavior. Comput Econ 11:103–128CrossRefGoogle Scholar
- Dickey D, Fuller W (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072. doi: 10.2307/1912517 CrossRefGoogle Scholar
- Diks C, Panchenko V (2006) A new statistic and practical guidelines for nonparametric Granger causality testing. J Econ Dyn Control 30:1647–1669. doi: 10.1016/j.jedc.2005.08.008 CrossRefGoogle Scholar
- Engle R, Granger C (1987) Co-integration and error correction: representation, estimation and testing. Econometrica 55:251–276. doi: 10.2307/1913236 CrossRefGoogle Scholar
- Gençay R, Selçuk F, Whitcher B (2002) An introduction to wavelets and other filtering methods in finance and economics. Academic Press, LondonGoogle Scholar
- Gençay R, Selçuk F, Whitcher B (2003a) Systematic risk and time scales. Quant Financ 3:108–116CrossRefGoogle Scholar
- Gençay R, Selçuk F, Whitcher B (2003b) Multiscale systematic risk. J Int Money Financ 24:55–70. doi: 10.1016/j.jimonfin.2004.10.003 CrossRefGoogle Scholar
- Gonzalo J, Granger C (1995) Estimation of common long-memory components in cointegrated systems. J Bus Econ Stat 13:27–35. doi: 10.2307/1392518 Google Scholar
- Hakkio CS, Rush M (1989) Market efficiency and cointegration: an application to the Sterling and Deutschemark exchange markets. J Int Money Financ 8:75–88. doi: 10.1016/0261-5606(89)90015-6 CrossRefGoogle Scholar
- Hansen H, Johansen S (1999) Some tests for parameter constancy in cointegrated VAR-models. Econom J 2:306–333. doi: 10.1111/1368-423X.00035 CrossRefGoogle Scholar
- Hasbrouck J (1995) One security, many markets: determining the contributions to price discovery. J Financ 50:1175–1199. doi: 10.1111/j.1540-6261.1995.tb04054.x CrossRefGoogle Scholar
- Hiemstra C, Jones JD (1994) Testing for linear and non-linear Granger causality in the stock price-volume relationship. J Financ 49:1639–1664. doi: 10.1111/j.1540-6261.1994.tb04776.x Google Scholar
- Hou Y, Li S (2013) Price discovery in chinese stock index futures market: new evidence based on intraday data. Asia Pac Financ Mark 20:49–70. doi: 10.1007/s10690-012-9158-8 CrossRefGoogle Scholar
- In F, Kim S (2006a) The hedge ratio and the empirical relationship between the stock and futures markets: a new approach using wavelet analysis. J Bus 79:799–820. doi: 10.1086/499138 CrossRefGoogle Scholar
- In F, Kim S (2006b) Multiscale hedge ratio between the Australian stock and futures markets: evidence from wavelet analysis. J Multinatl Financ Manag 16:411–423. doi: 10.1016/j.mulfin.2005.09.002 CrossRefGoogle Scholar
- Johansen S (1988) Statistical analysis of cointegration vectors. J Econ Dyn Control 12:231–254. doi: 10.1016/0165-1889(88)90041-3 CrossRefGoogle Scholar
- Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive methods. Econometrica 59:1551–1580. doi: 10.2307/2938278 CrossRefGoogle Scholar
- Kim S, In F (2003) The relationship between financial variables and real economic activity: evidence from spectral and wavelet analysis. Stud Nonlinear Dyn Econom 7:0–16. doi: 10.2202/1558-3708.1183 Google Scholar
- Kim S, In F (2005a) The relationship between stock returns and inflation: new evidence from wavelet analysis. J Empir Financ 12:435–444. doi: 10.1016/j.jempfin.2004.04.008 CrossRefGoogle Scholar
- Kim S, In F (2005b) Multihorizon sharpe ratio. J Portf Manag 31:105–111. doi: 10.3905/jpm.2005.470583
- Kim S, In F (2007) On the relationship between changes in stock prices and bond yield in the G7 countries: wavelet analysis. J Int Financ Mark Inst Money 17:167–179. doi: 10.1016/j.intfin.2005.10.004 CrossRefGoogle Scholar
- Kwiatkowski D, Phillips P, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J Econom 54:159–178. doi: 10.1016/0304-4076(92)90104-Y CrossRefGoogle Scholar
- Lai KS, Lai M (1991) A cointegration test for market efficiency. J Futures Mark 11:567–575. doi: 10.1002/fut.3990110505 CrossRefGoogle Scholar
- Lee HS (2004) International transmission of stock market movements: a wavelet analysis. Appl Econ Lett 11:197–201. doi: 10.1080/1350485042000203850 CrossRefGoogle Scholar
- Lin S, Stevenson M (2001) Wavelet analysis of the cost-of-carry model. Stud Nonlinear Dyn Econom 5:87–102. doi: 10.2202/1558-3708.1073 CrossRefGoogle Scholar
- Martens M, Kofman P, Vorst TCF (1998) A threshold error-correction model for intraday futures and index returns. J Appl Econom 13:245–263. doi: 10.1002/(SICI)1099-1255(199805/06)13:3<245::AID-JAE480>3.0.CO;2-E CrossRefGoogle Scholar
- Ng L, Wu F (2007) The trading behavior of institutions and individuals in Chinese equity markets. J Bank Financ 31:2695–2710. doi: 10.1016/j.jbankfin.2006.10.029 CrossRefGoogle Scholar
- Pan Z, Wang X (1998) A stochastic nonlinear regression estimator using wavelets. Comput Econ 11:89–102CrossRefGoogle Scholar
- Percival DB, Walden AT (2000) Wavelet methods for time series analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
- Phillips P, Perron P (1988) Testing for a unit root in time series regression. Biometrica 75:335–346. doi: 10.1093/biomet/75.2.335 CrossRefGoogle Scholar
- Ramsey JB (1999) The contribution of wavelets to the analysis of economic and financial data. Philos Trans R Soc Lond A Math Phys Eng Sci 357:2593–2606. doi: 10.1098/rsta.1999.0450 CrossRefGoogle Scholar
- Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlinear Dyn Econom 6:1–27. doi: 10.2202/1558-3708.1090 Google Scholar
- Ramsey JB, Lampart C (1998a) Decomposition of economic relationships by timescale using wavelets. Macroecon Dyn 2:49–71Google Scholar
- Ramsey JB, Lampart C (1998b) The decomposition of economic relationships by time scale using wavelets: expenditure and income. Stud Nonlinear Dyn Econom 3:23–42. doi: 10.2202/1558-3708.1039 Google Scholar
- Ramsey JB, Zhang Z (1997) The analysis of foreign exchange data using waveform dictionaries. J Empirl Financ 4:341–372. doi: 10.1016/S0927-5398(96)00013-8 CrossRefGoogle Scholar
- Schwarz TV, Szakmary AC (1994) Price discovery in petroleum markets: arbitrage, cointegration, and the time interval of analysis. J Futures Mark 14:147–167. doi: 10.1002/fut.3990140204 CrossRefGoogle Scholar
- Theissen E (2002) Price discovery in floor and screen trading systems. J Empir Financ 9:455–474. doi: 10.1016/S0927-5398(02)00005-1 CrossRefGoogle Scholar
- Walden AT (2001) Wavelet analysis of discrete time series. In: Casacuberta C, Miró-Roig RM, Verdera J, Xambó-Descamps S (eds) European congress of mathematics. Birkhäuser Basel. doi: 10.1007/978-3-0348-8266-8_56
- Yang J, Yang Z, Zhou Y (2012) Intraday price discovery and volatility transmission in stock index and stock index futures markets: evidence from China. J Futures Mark 32:99–121. doi: 10.1002/fut.20514 CrossRefGoogle Scholar

## Copyright information

© Springer-Verlag Berlin Heidelberg 2017