A MARKOV SWITCHING APPROACH

1. Introduction

At the end of December 2019, Wuhan city in Hubei Province, China has out-broken unexplained viral pneumonia (Health Commission of Hubei Province, 2020), subsequently, the epidemic continued to spread throughout China and worldwide. At the end of January 2020, the World Health Organization (WHO) declared a public health emergency of international concern over the spread of COVID-19, which is the highest level of alarm under international law (WHO, 2022). The outbreak and spread of the global new crown pneumonia epidemic have brought heavy damage to the global capital market, and COVID-19 is regarded as a black swan event that has a huge impact on the world (Baker, et al., 2020; Su et al., 2022).

International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 [Vol. 4 No. 2 June 2022]

Journal website: http://myjms.mohe.gov.my/index.php/ijbec

THE IMPACT OF THE OIL RETURN ON S&P500 RETURN OVER THE PERIOD 2000 TO 2022: A MARKOV

SWITCHING APPROACH

Seuk Wai Phoong^1*, Seuk Yen Phoong² and Congmin Zhang³

1 Department of Management, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur, MALAYSIA

2 Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, MALAYSIA

3 Department of Accounting, Faculty of Economics and Management, Chongqing Metropolitan College of Science and Technology, Chongqing, CHINA

*Corresponding author: phoongsw@um.edu.my

Article Information:

Article history:

Received date : 17 March 2022 Revised date : 15 May 2022 Accepted date : 28 May 2022 Published date : 15 June 2022

To cite this document:

Phoong, S. W., Phoong, S. Y., &

Zhang, C. (2022).THE IMPACT OF THE OIL RETURN ON S&P500 RETURN OVER THE PERIOD 2000 TO 2022: A MARKOV SWITCHING APPROACH. International Journal of Business and Economy, 4(2), 24-29.

Abstract: There are two objectives in this paper. The first objective is to examine the association between oil return and U.S. return over the period January 2000 to January 2022 by using the Markov switching regression model. The second objective is to forecast the S&P500 return during the COVID-19 epidemic. The results show that there is an association between the WTI oil return and U.S. return, especially when the stock return is growing. Furthermore, this study also highlights COVID-19 triggered the dramatic changes in the stock return predictability over the period, January 2020 to January 2022.

Keywords: COVID-19; Markov Switching; Oil return;

S&P500; Static Forecast.

The magnitude of asset price volatility brought by the epidemic is comparable to the Great Crisis of the 2008 International Financial Crisis. In the context of global economic integration, the interconnectedness of various economies will be strengthened, and the way developed and developing countries deal with pandemic events will have a huge impact on the world economy. The epidemic has accelerated the division of the global economy, in order to resist the impact of the economic recession, countries have restricted international trade to protect their own interests, which may lead to the global prevalence of trade protectionism.

After the outbreak of the global epidemic, panic among stock investors broke out, and financial markets were under enormous pressure and experienced violent fluctuations reaching the level of the financial crisis. The figure below shows the world's major stock indexes fall during the global spread of COVID-19.

0.00 3,000.00 6,000.00 9,000.00 12,000.00 15,000.00 18,000.00 21,000.00 24,000.00 27,000.00 30,000.00 33,000.00

16-Dec 6-Jan 27-Jan 17-Feb 9-Mar 30-Mar 20-Apr 11-May 1-Jun

Dow Jones Industrial Average Nasdaq Composite

Hang Seng Index Nikkei 225

Figure 1: Selected Stock Indexes During the Global Spread of COVID-19 Resource: Choice Database

On March 9, 12, 16, and 18, 2020, U.S. stocks experienced circuit breakers four times in a row.

The Dow Jones Industrial Average fell more than 10,000 points, while the S&P 500 and Nasdaq fell 33% and 28%, respectively. There are many studies examining the relationship between stock price and oil price. Lee, Yang & Huang (2012) by applying the industry stock prices and oil prices of G7 countries from 1991 to 2009, found that there is a relationship between industry stock price changes and oil price changes. The stock price changes of major consumer goods and materials industries are most affected by oil price changes, followed by transportation, finance, energy, and medical care. Healthcare, Industrial, Utilities, Information Technology and Telecommunications industries. Narayan & Gupta's (2015) also concluded that the changes in oil prices affected the U.S. stock returns. Phoong et al. (2020) analyzed the impact of the changes in the oil price on GDP, and found that the structural change/ break affects the relationship of the oil price and GDP. This study is further analysis the correlations between the WTI crude oil price and Malaysia’s GDP. The result shows that there is a positive correlation between oil price and GDP.

Erten & Ocampo (2021) revealed that after the outbreak of the global new crown epidemic, the control of people's travel has led to a cliff-like decline in energy demand and a historic drop in world energy prices. According to Sharif, Aloui, & Yarovaya, (2020), a volatile drop in oil prices is a temporary risk that can be contained through the OPEC+ deal, but restrictions on people's travel will allow COVID-19 to further impact oil prices. Bildirici, Guler & Ucan (2020) also reported that the COVID-19 epidemic has caused recessions in all economies around the world, countries have introduced policies to prevent people from traveling, the one- by-one factory has been forced to close, which caused the rising unemployment rate, declined on the consumption power, and the global value chains have been disrupted, etc. The decline in production will be indirectly influenced the market demand for oil.

0.00 500.00 1,000.00 1,500.00 2,000.00 2,500.00 3,000.00 3,500.00 4,000.00

16-Dec 6-Jan 27-Jan 17-Feb 9-Mar 30-Mar 20-Apr 11-May 1-Jun

S&P 500 SSE Composite Index

This paper used a Markov switching regression model to measure the impact of the changes of the WTI oil return on S&P500 return, and then forecasting the changes of the price, in order to provide a reference for policymakers, and at the same time prepare for possible changes in energy prices, and ensure national energy security.

2. Data and Methodology

The data set used in this paper is the monthly closing price of S&P500 over the period January 2000 through January 2022. Furthermore, the WTI oil price (USD per barrel) is also included in this study to investigate the impact of oil price on stock price, thereafter forecasting the changes of the S&P500. The data is taken from yahoo finance and the currency is the US dollar.

The data set is taking returns by using the formula 𝑟 = 100 ∗ ln ( ^𝑃𝑡

𝑃_𝑡−1). The data is then analyzed by using two regimes switching regression analysis incorporates with the Markov properties in order to investigate the volatility changes in each regime with unobservable state variables. Markov switching regression model is used to measure the time series that transition over a set of finite states by following a Markov process.

The equation for the model is

𝑆&𝑃500_𝑡 = 𝜇_𝑘𝑊𝑇𝐼_𝑡+ 𝜏_𝑡

where the variables are in returns form, 𝜇_𝑘 = 𝜇₁ if k = 1 (regime 1), and 𝜇₂ if k = 2 (regime 2). The transition probabilities also can be calculated by [𝑃𝑟₁₁ 𝑃𝑟₁₂

𝑃𝑟₂₁ 𝑃𝑟₂₂], the 𝑃𝑟₁₁+ 𝑃𝑟₁₂= 1.

Static forecasting is also used in this study to predict the changes of the S&P500 return by using the actual value of the lagged S&P500 return. The static forecasting is choosing since it requires the real data for the variables. The root mean square error is also calculated in this paper to understand the standard deviations of the residuals (predict errors). It is crucial in econometrics to explain the concentration of the data around the best fit line.

3. Results and Discussion

The findings for a two states Markov switching regression model are summarized as follows:

Table 1: Markov Switching Regression Outputs

Regime 1 Regime 2

Variable Coefficient Variable Coefficient WTI -0.002799 (0.9058) WTI 0.177531 (0.0002)

C 1.640044 (0.0000) C -5.842302 (0.0000) Remark: () is the probability value,

Dependent variable: S&P 500 return

From the coefficient value in Table 1, the coefficient for WTI returns in regime 1 is -0.002799 (p-value: 0.9058) which indicates that it is a depression state and the relationship between WTI and S&P500 return is not significant (the p-value is greater than 0.05 level of significance).

The coefficient for WTI in regime 2 is 0.177531 (p-value = 0.0002 less than 0.05 significance level) indicating that regime 2 is an appreciation state and the WTI return has a significant impact on the changes of S&P500.

Besides that, the transition probabilities for 𝑝₁₁ = 0.888264, 𝑝₁₂ = 0.111736, 𝑝₂₁ = 0.637049, and 𝑝₂₂ = 0.362951 show that the changes of the price from depreciation state to appreciate/

growth state takes a longer time period compared to the transition from the growth state to the depreciation period.

The static forecasting method is used to predict the changes of the S&P500 return and the results are reported in Figure 2.

-15 -10 -5 0 5 10 15

I II III IV I II III IV I

2020 2021 2022

LOGSP500F Actuals

Forecast: LOGSP500F Actual: LOGSP500

Forecast sample: 2020M01 2022M01 Included observations: 25

Root Mean Squared Error 5.581159 Mean Absolute Error 4.401289 Mean Abs. Percent Error 109.6796 Theil Inequality Coef. 0.857611 Bias Proportion 0.018874 Variance Proportion 0.825379 Covariance Proportion 0.155747 Theil U2 Coefficient 1.044417 Symmetric MAPE 154.4530

Figure 2: Static Forecasts Output

The forecasting series for S&P500 returns are presented in Figure 2. The time period chosen is from January 2020 until January 2022. The reason for choosing this duration rather than the entire sample size is to investigate the changes in the stock returns during the COVID-19 epidemic. Based on the findings, the actual changes of the stock return are differing greatly from the predicted time series. This indicates that the COVID-19 epidemic caused a whipsaw movement in the stock return at the beginning of the COVID-19. These results can be supported by Bildirici, Guler & Ucan (2020) and Hong & Lee (2021) that COVID-19 affects the stock market performance, such as the price volatility and return predictability. Furthermore, a lower root mean square error indicates that the observed and simulated data are close to each other which means better accuracy. The root mean square error is 5.581159, and the mean absolute error is 4.401289. The difference between root mean square error is slightly larger than mean absolute error indicating that the variance in the individual data point is small.

4. Conclusion

This paper investigates the association between oil return and stock return before and during the COVID-19 epidemic by using a Markov switching regression model. Unlike other studies, we focus on the forecasts of the stock market performance during the COVI-19 epidemic (2020-2022). The results show that the WTI oil return affects the U.S. stock return, especially when the stock return is growing. Furthermore, we also find that there is a whipsaw movement at the beginning of the COVID-19 epidemic in the return predictability. The implication of this study is beneficial to the individual and institutional investors to understand the relationship between oil prices and the stock market, the price volatility, and return predictability, which is conducive to the operation of funds and improves personal income.

5. Acknowledgement

This research is funded by Fundamental Research Grant Scheme (FRGS), provided by Ministry of Higher Education. Grant number: FRGS/1/2019/STG06/UM/02/9.

References

Baker, S. R., Bloom, N., Davis, S. J., and Terry, S. J. (2020). Covid-induced economic uncertainty. Natl. Bureau Econ. Res., 26983. doi: 10.3386/w26983.

Bildirici, M., Guler Bayazit, N., & Ucan, Y. (2020). Analyzing crude oil prices under the impact of covid-19 by using lstargarchlstm. Energies, 13(11), 2980.

Erten, B., & Ocampo, J. A. (2021). The future of commodity prices and the pandemic-driven global recession: evidence from 150 years of data. World Development, 137, 105164.

Health Commission of Hubei Province. (2020). Press Conference on Prevention and Control of the Novel Coronavirus Pneumonia. https://wjw.hubei.gov.cn/bmdt/ztzl/

fkxxgzbdgrfyyq/ xxfb/202001/t20200124_2014641.shtml

Hong, H., Bian, Z. & Lee, C.C. (2021). COVID-19 and instability of stock market performance:

evidence from the U.S. Financial Innovation 7, 12 (2021). https://doi.org/10.1186/s40854- 021-00229-1

Lee, B. J., Yang, C. W., & Huang, B. N. (2012). Oil price movements and stock markets revisited: A case of sector stock price indexes in the G-7 countries. Energy Economics, 34(5), 1284-1300.

Morens, D. M., Folkers, G. K., & Fauci, A. S. (2009). What is a pandemic? The Journal of infectious diseases, 200(7), 1018-1021.

Mugaloglu, E., Polat, A. Y., Tekin, H., & Dogan, A. (2021). Oil price shocks during the COVID-19 pandemic: evidence from United Kingdom energy stocks. Energy Research Letters, 2(1), 24253.

Narayan, P. K., & Gupta, R. (2015). Has oil price predicted stock returns for over a century?

Energy Economics, 48, 18-23.

Our world in data (2022). Retrieved from https://ourworldindata.org/covid-cases

Phoong, S. Y. and Phoong, S. W. (2021). The Level of Knowledge, Awareness and Attitude of Tertiary Students toward COVID-19. Annals of the Romanian Society for Cell Biology, 25(5), 4175 4184.

Phoong, S.W., Phoong, S. Y. & Phoong, K.H. (2020). Analysis of Structural Changes in Financial Datasets using the Breakpoint Test and the Markov Switching Model. Symmetry 2020, 12, 401; doi:10.3390/sym12030401.

World Health Organization. (2022). WHO Director-General's opening remarks at the media briefing on COVID-19 - 1 February 2022. Retrieved from https://www.who.int/director- general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing- on-covid-19---1-february-2022

Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 101496.

Su, W., Guo, X., Ling, Y. & Fan, Y. (2022). China's SMEs Developed Characteristics and Countermeasures in the Post-epidemic Era. Frontiers in Psychology, 13, 842646.