| Reference: | 林思如，陳宗仁，王憲斌與魏石勇(2017)，「股市規模波動的價量關係—以台灣股票市場為例」，《中華管理評論國際學報》， 20(2)。
莊家彰與管中閔(2005)，「台灣與美國股市價量關係的分量迴歸分析」，《經濟論文》， 33(4)， 379-404。
劉映興與陳家彬(2002)，「台灣股票市場交易值、交易量與發行量加權股價指數關係之實證研究—光譜分析之應用」，《農業經濟半年刊》， 72，65-87。
Aluko, O.A., and P.O. Adeyeye (2020), “Imports and economic growth in Africa: testing for Granger causality in the frequency domain,” The Journal of International Trade & Economic Development, 29(7), 850-864.
Bahmani-Oskooee, M., Chang, T., and Ranjbar, O. (2016),“Asymmetric causality using frequency domain and time-frequency domain (wavelet) approaches,” Economic Modelling, 56, 66-78.
Bojanic, A.N. (2012), “The impact of financial development and trade on the economic growth of Bolivia,” Journal of Applied Economics, 15(1), 51-70.
Chen, S.W. (2008), “Untangling the nexus of stock price and trading volume: evidence from the Chinese stock market,” Economics Bulletin, 7(15), 1-16.
Croes, R., and Rivera, M.A. (2010), “Testing the empirical link between tourism and competitiveness: evidence from Puerto Rico,” Tourism Economics, 16(1), 217-234.
Croux, C., and Reusens, P. (2013), “Do stock prices contain predictive power for the future economic activity? A Granger causality analysis in the frequency domain,” Journal of Macroeconomics, 35, 93-103.
Dickey, D.A., and W.A. Fuller (1979), “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, 74, 427-431.
Goffe, W. (1994), “Wavelets in macroeconomics: an introduction,” Computational techniques for econometrics and economic analysis, 137-149.
Graham, M., and Nikkinen, J. (2011), “Co-movement of the Finnish and international stock markets: a wavelet analysis.,” The European Journal of Finance, 17:5-6, 409-425.
Granger, C.W.J. (1969), “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society, 37, 424-438.
Grinsted, A., Moore, J.C., and Jevrejeva, S. (2004), “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Process Geophysics, 11, 561-566.
Gronwald, M. (2009), “Reconsidering the macroeconomics of the oil price in Germany: testing for causality in the frequency domain,” Empirical Economics, 36, 441-453.
Gupta, S., Das, D., Hasim, H., and Tiwari, A.K. (2018), “The dynamic relationship between stock returns and trading volume revisited: a MODWTVAR approach,” Finance Research Letters, 27, 91-98.
Hudgins, L., Friehe, C., and Mayer, M. (1993), “Wavelet transforms and atmospheric turbulence,” Physical Review Letters, 71, 3279–3282.
Hui, E.C.M., and Yue S. (2006), “Housing price bubbles in Hong Kong, Beijing and Shanghai: a comparative study,” Journal of Real Estate Finance and Economics, 33, 299-327.
Jain, P.C., and Joh, G.-H. (1988), “The dependence between hourly prices and trading volume,” The Journal of Financial and Quantitative Analysis, 23(3), 269-283.
Jumbe, C.B.L. (2004), “Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi,” Energy Economics, 26, 61-68.
Kirikkaleli, D., and Güngör, H. (2021), “Comovement of commodity price indexes and energy price index: a wavelet coherence approach,” Financial Innovation, 7:15.
Lee, B.-S., and Rui, O.M. (2002), “The dynamic relationship between stock returns and trading volume: domestic and cross-country evidence,” Journal of Banking & Finance, 26, 51-78.
Li, X.L., Chang, T., Miller, S., Balcilar, M., and Gupta, R. (2015), “The comovement and causality between the U.S. housing and stock markets in the time and frequency domains,” International Review of Economics and Finance, 38, 220-233.
Loh, L. (2013), “Co-movement of Asia-Pacific with European and US stock market returns: a cross-time-frequency analysis,” Research in International Business and Finance, 29, 1-13.
Pal, D., and Mitra, S.K. (2017), “Time-frequency contained co-movement of crude oil and world food prices: a wavelet-based analysis,” Energy Economics, 62, 230-239.
Pinzón, K. (2018), “Dynamics between energy consumption and economic growth in Ecuador: a granger causality analysis,” Economic Analysis and Policy, 57, 88-101.
Rahman, M.M., and Kashem, M.A. (2017), “Carbon emissions, energy consumption and industrial growth in Bangladesh: empirical evidence from ARDL cointegration and Granger causality analysis,” Energy Policy, 110, 600-608.
Ramsey, J.B., and Zhang, Z. (1996), “The analysis of foreign exchange data using waveform dictionaries,” Journal of Empirical Finance, 4, 341-372.
Reboredo, J.C., and Rivera-Castro, M.A. (2014), “Wavelet based evidence of the impact of oil prices on stock returns,” International Review of Economics & Finance, 29, 145-176.
Rua, A., and Nunes, L.C. (2009), “International comovement of stock market returns: a wavelet analysis,” Journal of Empirical Finance, 12, 632-639.
Said E., and Dickey, D.A. (1984), “Testing for unit roots in autoregressive moving average models of unknown order,” Biometrika, 71, 599-607.
Tiwari, A. K., M. I. Mutascu, C. T. Albulescu, and P. Kyophilavong (2015), “Frequency domain causality analysis of stock market and economic activity in India,” International Review of Economics and Finance, 39, 224-238.
Toda, H. Y., and T. Yamamoto (1995), “Statistical inference in vector autoregressions with possibly integrated process,” Journal of Econometrics, 66(1-2), 225-250.
Yilanci, V., Ozgur, O., and Gorus, M.S. (2021), “Stock prices and economic activity nexus in OECD countries: new evidence from an asymmetric panel Granger causality test in the frequency domain,” Financial Innovation, 7:11. |
