Influence of stock market variables on correlations among S&P sectors
Název práce v češtině: | Vliv proměnných akciového trhu na korelace mezi sektory S&P |
---|---|
Název v anglickém jazyce: | Influence of stock market variables on correlations among S&P sectors |
Klíčová slova: | korelace, DCC, S&P, výnos dluhopisu, ropa, VIX |
Klíčová slova anglicky: | correlation, DCC, S&P, bond yield, crude oil, VIX |
Akademický rok vypsání: | 2020/2021 |
Typ práce: | diplomová práce |
Jazyk práce: | angličtina |
Ústav: | Institut ekonomických studií (23-IES) |
Vedoucí / školitel: | PhDr. František Čech, Ph.D. |
Řešitel: | skrytý![]() |
Datum přihlášení: | 25.05.2021 |
Datum zadání: | 25.05.2021 |
Datum a čas obhajoby: | 15.06.2022 09:00 |
Místo konání obhajoby: | Opletalova - Opletalova 26, O105, Opletalova - místn. č. 105 |
Datum odevzdání elektronické podoby: | 01.05.2022 |
Datum proběhlé obhajoby: | 15.06.2022 |
Oponenti: | doc. PhDr. Jozef Baruník, Ph.D. |
Kontrola URKUND: | ![]() |
Seznam odborné literatury |
Ang, A. & Bekaert, G. (1999). International asset allocation with time-varying correlations, NBER Working Paper
7056. Arouri, M. et al. (2011). Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management. Journal of International Money and Finance, 30, (7), 1387-1405. Broadstock, D. C. & Filis, G. (2014). Oil price shocks and stock market returns: New evidence from the United States and China. Journal of International Financial Markets, Institutions and Money, 33(C), 417-433. Engle, R. F. & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlation multivariate garch. Working paper no. 8554, National Bureau of Economic Research. Engle, R. F. (2000). Dynamic conditional correlation - a simple class of multivariate garch models. Discussion Paper, 2000-09. University of California, San Diego, May 2000. Engle, R. F. (2002). Dynamic conditional correlation. Journal of Business and Economic statistics, 20(3), 339– 350. Gjika, D. & Horváth, R. (2013). Stock market comovements in Central Europe: Evidence from the asymmetric DCC model. Economic Modelling, 33, 55–64. Mensi, W. et al. (2016). Global financial crisis and spillover effects among the U.S. and BRICS stock markets. International Review of Economics & Finance, 42, 257–276. Syllignakis, M.N. & Kouretas, G.P. (2011). Dynamic correlation analysis of financial contagion: evidence from the Central and Eastern European markets. International Review of Economics and Finance 20 (4), 717–732. Wang, P. & Moore, T. (2008). Stock market integration for the transition economies: time-varying conditional correlation approach. The Manchester School 76, 116–133. |
Předběžná náplň práce v anglickém jazyce |
Motivation:
Determining the correlations between financial instruments has been the cornerstone of portfolio creation. Researchers over the past decades have focused on various ways of estimating the standard deviations (volatility) as well as correlations among different financial assets. Vast majority of studies analyzing the correlations investigated the co-movements of just aggregate stock indices from different countries. For example, Syllignakis, M.N. & Kouretas, G.P. (2011) examined the interconnection between stock markets in Germany, the US and Russia, and seven emerging countries from Central and Eastern Europe. Applying weekly data from 1997 to 2009 in the DCC-GARCH model, the authors show that returns on the US and German stock markets significantly influenced the returns on the CEE markets. On the other hand, the correlation between the Russian stock market and the CEE markets did not turn out to be statistically significant. Next, Mensi, W. et al. (2016) examined the dynamic correlations between the stock markets in the US and BRICS countries (Brazil, Russia, India, China and South Africa). Using the data between September 1997 and October 2013, the results of the DCC-FIAPARCH model suggest that the US stock market returns were significant for forecasting returns on stock markets in all BRICS countries. Moreover, the correlations between the US and all BRICS countries except for Russia strengthened during and after the 2008-2009 financial crisis. However, not much work has been devoted to the interrelation between individual sector or industry indices within one country. The main goal of this thesis is to examine the time-varying dependencies among S&P 500 sectors. All eleven sectors, represented by sector indices, will be considered in the analysis, whereas six sectors will be studied in more detail. These six sectors are selected such that both cyclical and non-cyclical sectors are represented. Further, although the precise distinction between value vs. growth sectors is not possible, some of the chosen sectors tend to be tilted to value, or growth, respectively. Hence, the dynamics of dependencies among cyclical, non-cyclical, rather-value, and rather-growth sectors will be observed and analyzed. This research will aim to answer the question of how the analyzed sectors co-move when the overall market, represented by the S&P 500 Index, goes up or down. The general notion is that stocks are correlated more in times of market corrections or crises (Ang, A. & Bekaert, G. (1999)). However, this thesis is supposed to show the difference in reactions of individual correlation pairs to the returns of the S&P 500 Index. Next, a possible link between the US bond market (represented by the 10-year Treasury Note) and the dynamics of analyzed correlations will be examined. Arouri, M. et al. (2011) as well as Broadstock, D. C. & Filis, G. (2014) showed that sector stock indices might react to the oil market evolution differently. Hence, in my thesis I will also investigate how oil price changes affect the correlations among examined S&P 500 sectors. Methodology: I will work with the daily data on eleven S&P 500 sector indices, the 10-year Treasury Note, and oil prices. The data is available on the web page of S&P Global, The Wall Street Journal or Investing.com. To include two considerable market downtrends – the ones in 2007-2009 and 2020 – time period between January 2007 and February 2022 will be examined. Focusing on six sectors will lead to the detailed analysis of fifteen time-varying correlations. The ultimate selection of sectors is Consumer Staples, Energy, Financials, Health Care, Information Technology, and Utilities. Consumer Staples, and Utilities represent non-cyclical sectors, whereas the remaining four sectors are considered cyclical. Concerning the distinction between value and growth sectors, Financials (value) and Information Technology (growth) are regarded as major counterparts. Next, Health Care is tilted toward the growth factor, while the other three are rather value sectors. (S&P Global web page) For each sector index, the logarithmic daily returns will be calculated. An appropriate form of ARMA-GARCH model will be estimated, which will then become a basis for a multivariate GARCH model (e.g. Dynamic Conditional Correlation introduced by Engle, R. F. (2000)). Time-varying correlations suggested by this model will be further analyzed. First, the descriptive statistics of dependencies between individual sectors will offer a general overview and comparison of the examined pairs. Next, regression models will examine the relationship between the correlation pairs and the S&P 500 Index as well as the 10-year Treasury Note or the oil price changes. These regression models will be applied for three different lengths of returns: 1 day, 1 week, 1 month to see for which returns there are significant relationships between the independent variable (S&P 500 Index, 10-year U.S. Treasury Note, oil price changes) and the dependent variable (correlations among analyzed S&P 500 sectors). Expected contribution: This thesis should bring a deeper understanding of the dynamics of the interdependencies among S&P 500 sectors. Compared to studies that focused on correlations of aggregate stock indices, such as Gjika, D. & Horváth, R. (2013) or Mensi, W. et al. (2016), my thesis will offer the sectoral perspective on the US stock market. As both cyclical and non-cyclical sectors will be studied in more detail, this paper is supposed to explain how correlations between these two types of sectors evolve. Furthermore, it should explore the co-movement between sectors that are rather-value and rather-growth oriented. Overall, the goal of this work is to deliver a better perception of the stock market – how the cyclical and noncyclical sectors are related, in which situations value and growth indices are less (more) correlated, how the U.S bond market or oil price changes influence individual correlation pairs, etc. Therefore, it should help investors with construction of a diversified portfolio based on the specific phase, in which the stock market currently stands. Moreover, the year 2020 was a year full of records and extremes as far as the stock market and events related to it are concerned: oil price turned negative for the first time, the S&P 500 Index dropped by 34% between February and March (23 trading days), then a 68% increase in the index until the end of the year followed, and many other extremes. Maybe, this year has changed the style of investing. Therefore, it might be beneficial to show the results of the analysis covering the years 2020 as well as 2021. Perhaps, the correlations in and after 2020 will considerably differ from their values 10 years ago, for example. That all shall be shown by this thesis. Hypotheses: 1. Hypothesis #1: Correlations among S&P 500 sectors increase in times of a market decline. 2. Hypothesis #2: US bond market significantly influences the dynamics of the correlations among S&P 500 sectors. 3. Hypothesis #3: There exists a significant relationship between oil price changes and correlations among analyzed S&P 500 sectors. Outline: 1. Introduction: I will introduce the topic and show why it is important to do further research in this area. 2. Literature review: An overview of previous studies and their conclusions will be provided in this part. 3. Data: In this section, I will describe the data I will work with. Also, the procedure of sectors selection will be discussed. 4. Methodology: This part will be devoted to the methodology I will follow in my research. 5. Results interpretation: In this section, I will present the results of my analysis, and show the differences among the examined correlation pairs. 6. Conclusion: In this part, I will summarize the main results and goals achieved by this thesis. |