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.
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.
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.
Uyar, U., & Kangallı Uyar, S. G. (2022). The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis. Ege Academic Review, 22(1), 59-74. https://doi.org/10.21121/eab.1064535
AMA
Uyar U, Kangallı Uyar SG. The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis. eab. Ocak 2022;22(1):59-74. doi:10.21121/eab.1064535
Chicago
Uyar, Umut, ve Sinem Güler Kangallı Uyar. “The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis”. Ege Academic Review 22, sy. 1 (Ocak 2022): 59-74. https://doi.org/10.21121/eab.1064535.
EndNote
Uyar U, Kangallı Uyar SG (01 Ocak 2022) The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis. Ege Academic Review 22 1 59–74.
IEEE
U. Uyar ve S. G. Kangallı Uyar, “The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis”, eab, c. 22, sy. 1, ss. 59–74, 2022, doi: 10.21121/eab.1064535.
ISNAD
Uyar, Umut - Kangallı Uyar, Sinem Güler. “The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis”. Ege Academic Review 22/1 (Ocak 2022), 59-74. https://doi.org/10.21121/eab.1064535.
JAMA
Uyar U, Kangallı Uyar SG. The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis. eab. 2022;22:59–74.
MLA
Uyar, Umut ve Sinem Güler Kangallı Uyar. “The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis”. Ege Academic Review, c. 22, sy. 1, 2022, ss. 59-74, doi:10.21121/eab.1064535.
Vancouver
Uyar U, Kangallı Uyar SG. The Impact Of Covid-19 Pandemic on Systematic Risk Of S&P 500 Sectors: A Wavelet Power Spectrum Analysis. eab. 2022;22(1):59-74.