How fearful are Commodities and US stocks in response to Global fear? Persistence and Cointegration analyses

Yaya, OlaOluwa S. and Gil-Alana, Luis A. and Adekoya, Oluwasegun B. and Vo, Xuan Vinh (2021): How fearful are Commodities and US stocks in response to Global fear? Persistence and Cointegration analyses. Published in: , Vol. 74, No. 102273 (15 August 2021): pp. 1-15.

Abstract

This paper deals with the analysis of long-run relationships of fear indices for US stocks, commodities, and the energy sector with global fear indices for stocks and oil. Departing from the classical literature, fractional integration, and cointegration techniques are used to determine the degree of persistence in the long-run relationship of the indices. Our results are threefold. We first established a fractional cointegrating relationship between each of the global and oil fear indices and other fear indices. However, the long-run relationship tends to be weak for the technology stocks. In addition, the cointegrating framework reveals a nonstationary mean-reverting behaviour in the long-run relationship, implying that the effect of shocks from financial, economic, or other exogenous sources will be temporary though with long-lasting effects. These findings have crucial policy inferences for portfolio managers concerning investment decisions.

• Item Type: MPRA Paper
• Language: English
• Keywords: CBOE fear gauge; mean reversion; fractional integration; fractional cointegration; technology stocks
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Adekoya, O.B., Oliyide, J.A. and Oduyemi, G.O. (2020). How COVID-19 upturns the hedging potentials of gold against oil and stock markets risk: Nonlinear evidence through threshold regression and Markov regime switching models. Resour. Pol. 69 https://doi.org/10.1016/j.resourpol.2020.101926.

Andrada-Felix, J., Fernandez-Perez, A., Fernandez-Rodriguez, F. And Sosvilla-Rivero, S. (2021). Time connectedness of fear. Empirical Economics. DOI: 10.1007/s00181-021-02056-w

Barros, C. P. and Gil-Alana, L. A. (2009). Stock market returns and terrorist violence: Evidence from the Basque Country. Applied Economic Letters 6, 1575-1579.

Bash, A. and Alsaifi, K. (2019). Fear from uncertainty: An event study of Khashoggi and stock market returns. Journal of Behavioural and Experimental Finance 23, 54-58.

Badshah, I. (2018). Volatility spillover from the fear index to developed and emerging markets, forthcoming in Emerging Markets Finance and Trade, 54: 27-40.

Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives 28, 153-176.

Bouri, E., Jain, A., Biswal, P. C. and Roubaud, D. (2017). Cointegration and nonlinear causality amongst gold, oil and the Indian stock market: Evidence from implied volatility indices. Resources Policy 52, 201-206.

Bouri, E., Gupta, R., Hosseini, S. and Lau, C. K. M. (2018). Does global fear predict fear in BRICS stock markets? Evidence from a Bayesian Graphical Structural VAR model. Emerging Markets Review 34, 124-142.

Chen, H., Liu, L. and Li, X. (2018). The predictive content of CBOE crude oil volatility index. Physica A 492, 837-850.

Chen, X. and Chiang, T. C. (2020). Empirical investigation of changes in policy uncertainty on stock returns- Evidence from China’s market. Research in International Business and Finance 53, 101183.

Chiang, T. C. (2019). Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets. Finance Research Letters 29, 41-49.

Christou, C., Cunado, J., Gupta, R. and Hassapis, R. (2017). Economic policy uncertainty and stock market breturns in Pacific-Rim countries: Evidence based on a Bayesian panel VAR model. Journal of Multinational Financial Management 40, 92-102.

CNN (2020). Some companies are part of the coronavirus economy. The rest are in trouble. New report, May 12, 2020. https://www.cnn.com/2020/05/12/business/coronavirus-company-earnings/index.html

Diebold, F.X. and G. Rudebusch (1991), On the power of Dickey-Fuller tests against fractional alternatives 35, 155-160.

Dittman, I. (2000). Residuals based tests for fractional cointegration: A Monte Carlo study. Journal of Time Series Analysis 21, 6, 615-647.

Economou, F., Panagopoulos, Y. and Tsouma, E. (2018). Uncovering asymmetries in the relationship between fear and the stock market using a hidden-co-integration approach. Research in International Business and Finance 44, 459-470.

Fassa A.P. and Siriopoulos C. (2020). Implied volatility indices - A review. The Quarterly Review of Economics and Finance. https://doi.org/10.1016/j.qref.2020.07.004

Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis 4, 221-238.

Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. The Journal of Portfolio Management 31, 92-100.

Gul, T. G., Hussain, A. H., Bangash, S. B. and Khattak, S. W. K. (2010). Impact of terrorism on financial markets of Pakistan (2006-2008). MPRA Paper No. 41990.

Hassler, U. and J. Wolters, (1994), On the power of unit root tests against fractional alternatives, Economics Letters 45, 1-5.

Johnston, R. B. and Nedelescu, O. M. (2006). The impact of terrorism on financial markets. Journal of Financial Crime 13, 7-25.

Junttila, J., Pesonen, J. and Raatikainen, J. (2018). Commodity market based hedging against stock market risk in times of financial crisis: The case of crude oil and gold. Journal of International Financial Markets, Institutions and Money 56, 255-280.

Lee, D. and P. Schmidt (1996), On the power of the KPSS test of stationarity against fractionally integrated alternatives, Journal of Econometrics 73, 285-302.

Lucey, B. (2017),.Asymmetric linkages among the fear index andemerging market volatility indices. Emerging Markets Review 37, 17-31. doi:10.1016/j.ememar.2018.03.002

Maghyereh, A. I., Abdoh, H. and Awartani, B. (2019). Connectedness and hedging between gold and Islamic securities: A new evidence from time-frequency domain approaches. Pacific Basin Finance Journal 54, 13-28.

Magheyerh, A. I., Awartani, B. and Bouri, E. (2016). The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indices. Energy Economics 57, 78-93.

Marinucci, D. and Robinson, P. M. (2001). Semiparametric fractional cointegration analysis. Journal of Econometrics 105, 225-247.

Mensi, W., Hammoudeh, S., Reboredo, J. C. and Nguyen, D. K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach. Emerging Markets Review 19, 1-17.

Mensi, W., Tiwari, A., Bouri, E., Roubaud, D. and Al-Yahyaee, K. H. (2017). The dependence structure across oil, wheat and corn: A wavelet-based copula approach using implied volatility indexes. Energy Economics 66, 122-139.

Rehman, M. U., Shahzad, S. J. H., Uddin, G. S. and Hedstrom, A. (2018). Precious metal returns and oil shocks: A time varying connectedness approach. Resources Policy 58, 77-89.

Robinson, P.M. (1994). Efficient tests of nonstationary hypotheses. Journal of the American Statistical Association, 89, 1420-1437.

Robinson, P. M. (1995a). Log-periodogram regression of time series with long range dependence. Annals of Statistics 23, 1048-1072.

Robinson, P. M. (1995b). Gaussian semiparametric estimation of long range dependence. Annals of Statistics 23, 1630-1661.

Robinson, P.M. and Yajima, Y. (2002). Determination of cointegrating rank in fractional systems. Journal of Econometrics 106, 217-241.

Sarwar, G. (2012). Is VIX an investor fear guage in BRIC equity markets? Journal of Multinational Financial Management 22, 55-65.

Sarwar, G. and Khan, W. (2016). The effect of US stock market uncertainty on emerging market returns. Emerging Markets Finance and Trade 53, 1796-1811.

Sarwar, S., Tiwari, A. K. and Tingqiu, C. (2020). Volatility spillovers between oil market and Asian stock markets. (2020). Resources Policy, 66, 101608. https://doi.org/10.1016/j.resourpol.2020.101608

Smallwood, A. and S. Norrbin (2003), Long memory models and tests for cointegration: A synthesizing study, Computing in Economics and Finance, from Society for Computational Economics no. 32.

Tsai, I. (2014). Spillover of fear: Evidence from the stock markets of five developed countries. International Review of Financial Analysis 33, 281-288.

Waheed, R., Sarwar, S., Sarwar, S. and Khan, M. K. (2020). The impact of COVID‐19 on Karachi stock exchange: Quantile‐on‐quantile approach using secondary and predicted data. Journal of Public Affairs, 20(2), 1-6. https://doi.org/10.1002/pa.2290

Zhu, S., Liu, Q., Wang, Y., Wei, Y. and Wei, G. (2019). Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement? Physica A 536, 122567.