The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis

Yıl 2022, Cilt: 22 Sayı: 1, 59 - 74, 30.01.2022

Umut Uyar Sinem Güler Kangallı Uyar

https://doi.org/10.21121/eab.1064535

Öz

Investors reacted with panic and fear to the Coronavirus (COVID-19) pandemic and they created financial fluctuations. The aim of this study is to examine the volatility levels of S&P500 sector portfolios’ systematic risks in terms of different investment horizons. We employed the wavelet approaches that allow for analyzing the behavior of time series both
jointly at the time and frequency spaces. Thus, we observed the variation of financial beta coefficients, and the volatility levels of systematic risks over different investment horizons by sectors. Daily returns of 386 stocks from eleven sectors and S&P500 index was used for the period of January 2005 and July 2020. The findings of the study show that the systematic risks of sectors vary over different investment horizons. This means that the sensitivity of sectors to the daily movements of the market change at various time scales. Moreover, the volatility levels of systematic risks of each sector change over different investment horizons during the pandemic period. The results show that investors in the S&P 500 ignore the COVID-19 at the beginning, however, they reacted with panic during the pandemic period. In this respect, the findings
provide supporting evidence on behalf of the Prospect Theory.

Anahtar Kelimeler

COVID-19 pandemic, Wavelet multi-scaling, Wavelet power spectrum analysis, Systematic risk, S&P500

Kaynakça

• Aguiar-Conraria, L. and Soares, M. J. (2011), “Oil and the macroeconomy: using wavelets to analyze old issues”, Empirical Economics, Vol. 40 No. 3, pp. 645-655.
• Aloui, C. and Hkiri, B. (2014), “Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis”, Economic Modelling, Vol. 36, pp. 421-431.
• Altarturi, B. H., Alshammri, A. A., Hussin, T. M. T. T. and Saiti, B. (2016), “Oil price and exchange rates: A wavelet analysis for organisation of oil exporting countries members”, International Journal of Energy Economics and Policy, Vol. 6 No. 3, pp. 421-430.
• Aygoren, H. (2008) “Istanbul Menkul Kiymetler Borsasinin fractal analizi”, Dokuz Eylul Universitesi Iktisadi Idari Bilimler Fakultesi Dergisi, Vol. 23 No. 1, pp. 125-134.
• Bernard, V. L., Botosan, C. A. and Phillips, G. D. (1994), “Challenges to the Efficient Market Hypothesis: Limits to the Applicability of Fraud-on-the-Market Theory”, Nebraska Law Review, Vol. 73 No. 4, pp. 781-811.
• Black, F. (1986), “Noise”, The Journal of Finance, Vol. 41 No. 3, pp. 528-543.
• Brailsford, T. J. and Faff, R. W. (1997), “Testing the conditional CAPM and the effect of intervaling: a note”, Pacific-Basin Finance Journal, Vol. 5 No.5, pp. 527-537.
• Capobianco, E. (2004), “Multiscale analysis of stock index return volatility”, Computational Economics, Vol. 23 No.3, pp. 219- 237.
• Cohen, K. J. (1986), The microstructure of securities markets, Prentice Hall, Sydney.
• Connor, J. and Rossiter, R. (2005), “Wavelet transforms and commodity prices” Studies in Nonlinear Dynamics and Econometrics, Vol. 9 No.1, pp. 1-22.
• Cornish, C. R., Bretherton, C. S. and Percival, D. B. (2006), “Maximal overlap wavelet statistical analysis with application to atmospheric turbulence” Boundary-Layer Meteorology, Vol. 119 No. 2, pp. 339-374.
• Crowley, P. M. (2007), “A guide to wavelets for economists” Journal of Economic Surveys, Vol. 21 No.2, pp. 207-267.
• Crowley, P. M. and Lee, J. (2005), “Decomposing the co-movement of the business cycle: a time-frequency analysis of growth cycles in the euro area”, working paper, Bank of Finland Research Discussion Paper No:12, Finland.
• Fama, E. F. (1970), “Efficient capital markets: A review of theory and empirical work” The Journal of Finance, Vol. 25 No. 2, pp. 383-417.
• Gallegati, M., Gallegati, M., Ramsey, J. B. and Semmler, W. (2006), “The decomposition of the ınflation–unemployment relationship by time scale using wavelets” Contributions to Economic Analysis, Vol. 277, pp. 93-111.
• Gallegati, M., Gallegati, M., Ramsey, J. B. and Semmler, W. (2014), “Does productivity affect unemployment? A time-frequency analysis for the US”, Wavelet Applications in Economics and Finance, Springer, Cham, pp. 23-46.
• Gençay, R., Gradojevic, N., Selçuk∥, F. and Whitcher, B. (2010), “Asymmetry of information flow between volatilities across time scales”, Quantitative Finance, Vol. 10 No. 8, pp. 895-915.
• Gençay, R., Selçuk, F., and Whitcher, B. (2001), “Scaling properties of foreign exchange volatility”, Physica A: Statistical Mechanics and its Applications, Vol. 289 No. 1-2, pp. 249-266.
• Gençay, R., Selçuk, F., and Whitcher, B. (2003), “Systematic risk and timescales” Quantitative Finance, Vol. 3 No.2, pp. 108-116.
• Gençay, R., Selçuk, F. and Whitcher, B. (2005), “Multiscale systematic risk”, Journal of International Money and Finance, Vol. 24 No.1, pp. 55-70.
• Gençay, R., Selçuk, F. and Whitcher, B. J. (2002), An introduction to wavelets and other filtering methods in finance and economics, Academic Press, San Diego, CA.
• Glen, P. J. (2005), “Efficient Capital Market Hypothesis, Chaos Theory, and the Insider Filing Requirements of the Securities Exchange Act of 1934: The Predictive Power of Form 4 Filings”, Fordham Journal of Corporate and Financial Law, Vol. 11 No.1, pp. 85-114.
• Griggs Jr, F. S. (2002), “No stone unturned-forecasting revisited” AACE International Transactions, Vol. RI91-RI94.
• Grinsted, A., Moore, J. C. and Jevrejeva, S. (2004), “Application of the cross wavelet transform and wavelet coherence to geophysical time series”, Nonlinear Processes in Geophysics, Vol. 11, pp. 561-566.
• Grossman, S. J. and Stiglitz, J. E. (1980), “On the impossibility of informationally efficient markets”, The American Economic Review, Vol. 70 No. 3, pp. 393-408.
• Habimana, O. (2019), “Wavelet multiresolution analysis of the liquidity effect and monetary neutrality”, Computational Economics, Vol. 53 No. 1, pp. 85-110.
• Handa, P., Kothari, S. P. and Wasley, C. (1989), “The relation between the return interval and betas: Implications for the size effect”, Journal of Financial Economics, Vol. 23 No. 1, pp. 79-100.
• Handa, P., Kothari, S. P. and Wasley, C. (1993), “Sensitivity of multivariate tests of the capital asset‐pricing model to the return measurement interval”, The Journal of Finance, Vol. 48 No. 4, pp. 1543-1551.
• In, F. and Kim, S. (2013), An introduction to wavelet theory in finance: a wavelet multiscale approach, World Scientific Publishing, Singapure.
• Jarrett, J. E. and Kyper, E. (2006), “Capital market efficiency and the predictability of daily returns”, Applied Economics, Vol. 38 No. 6, pp. 631-636.
• Jensen, M. C. (1978), “Some anomalous evidence regarding market efficiency”, Journal of Financial Economics, Vol. 6 No. 2/3, pp. 95-101.
• Jiang, C., Chang, T. and Li, X. L. (2015), “Money growth and inflation in China: new evidence from a wavelet analysis”, International Review of Economics and Finance, Vol. 35, pp. 249-261
• Kahneman D. and Tversky A. (1979), “Prospect theory: An analysis of decision under risk”, Econometrica, Vol. 47, pp. 263–291.
• Kahneman, D. and Riepe, M. W. (1998), “Aspects of investor psychology”, Journal of Portfolio Management, Vol. 24 No. 4, pp. 52-65.
• Karp, A. and Van Vuuren, G. (2019), “Investment implications of the fractal market hypothesis”, Annals of Financial Economics, Vol. 14 No. 01, pp. 1-27.
• Kim, S. and In, F. H. (2003), “The relationship between financial variables and real economic activity: evidence from spectral and wavelet analyses”, Studies in Nonlinear Dynamics and Econometrics, Vol. 7 No. 4, pp. 1-16.
• Levhari, D. and Levy, H. (1977), “The capital asset pricing model and the investment horizon”, The Review of Economics and Statistics, Vol. 59 No. 1, pp. 92-104.
• Mallat, S. G. (1989), “A theory for multiresolution signal decomposition: the wavelet representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11 No. 7, pp. 674-693.
• Masset, P. (2015), “Analysis of Financial Time Series Using Wavelet Methods”, Handbook of Financial Econometrics and Statistics, Springer, USA, pp. 539-573.
• Percival, D. B. and Mofjeld, H. O. (1997), “Analysis of subtidal coastal sea level fluctuations using wavelets”, Journal of the American Statistical Association, Vol. 92 No. 439, pp. 868-880.
• Percival, D. and Walden, A. (2000) Wavelet methods for time series analysis. Cambridge University Press, Cambridge, UK.
• Ramsey, J. B. (1999), “The contribution of wavelets to the analysis of economic and financial data”, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, Vol. 357 No. 1760, pp. 2593-2606.
• Ramsey, J. B. (2002), “Wavelets in economics and finance: past and future”, Studies in Nonlinear Dynamics and Econometrics, Vol. 6 No. 3, pp. 1-27.
• Ramsey, J. B. and Lampart, C. (1998), “Decomposition of economic relationships by timescale using wavelets”, Macroeconomic Dynamics, Vol. 2 No. 1, pp. 49-71.
• Robinson, K. K. (2013), “Technical Analysis: Does Recent Market Data Substantiate the Efficient Market Hypothesis?” Available at SSRN 3093251.
• Rua, A. and Nunes, L. C. (2009), “International comovement of stock market returns: A wavelet analysis”, Journal of Empirical Finance, Vol. 16 No. 4, pp. 632-639.
• Saiti, B., Bacha, O. I. and Masih, M. (2016), “Testing the conventional and Islamic financial market contagion: evidence from wavelet analysis”, Emerging Markets Finance and Trade, Vol. 52 No. 8, pp. 1832-1849.
• Schleicher, C. (2002), “An introduction to wavelets for economists”, working paper, Bank of Canada, Canada.
• Sharpe, W. F., Alexander, G. J. and Bailey, J. V. (1999), Investments, Prentice-Hall, USA.
• Shik Lee, H. (2004), “International transmission of stock market movements: a wavelet analysis”, Applied Economics Letters, Vol. 11 No. 3, pp. 197-201.
• Shiller, R. J., Fischer, S. and Friedman, B. M. (1984), “Stock prices and social dynamics”, Brookings Papers on Economic Activity, Vol. 1984 No. 2, pp. 457-510.
• Shleifer, A. (2000), Inefficient Markets: An Introduction to Behavioral Finance, 1st ed., Oxford University Press, USA.
• Xu, F., Lai, Y. and Shu, X. B. (2018), “Chaos in integer order and fractional order financial systems and their synchronization”, Chaos, Solitons and Fractals, Vol. 117, pp. 125-136.