const -0,397 ** -0,204 -0,468 ** -0,300 ** -0,090 -0,287 -0,398 ** -0,286 c1 -0,316 ** -0,153 -0,367 *** -0,226 ** -0,052 -0,195 -0,307 ** -0,212 c2 -0,265 ** -0,126 -0,302 ** -0,191 * -0,088 -0,219 -0,266 ** -0,196 c3 0,232 ** 0,121 0,275 ** 0,177 ** 0,052 0,168 0,234 ** 0,169 c4 -0,001 -0,001 -0,001 -0,000 0,000 0,000 -0,002 ** -0,002 ** c5 -0,000 -0,001 -0,001 -0,001 0,002 * -0,000 -0,001 -0,001 c6 -0,020 -0,040 ** -0,043 ** -0,037 ** -0,034 -0,073 *** -0,026 -0,044 ** c7 -0,043 * -0,046 ** -0,072 ** -0,048 * 0,010 0,002 -0,047 * -0,029
Note. Composed by the author, 2014.
UK is the only country which sectors’ returns cannot be measured using VIXlocal and
VIXglobal. Only MSCI UK_CD and UK_FN sectors might be distinguished. The returns of
UK_CD could be determined if and only if VIXlocal was positive at t-1. If VIXlocal increased
by 1% at time t-1, the returns of UK_CD would rise by 0,023% at time t (Appendix 31). On the other hand, the returns of UK_FN could be determined if and only if VIXlocal went up by
any measure at t-1 causing the outcome to decline by 0,002% at time t.
Empirical Research Overview and Recommendations to Investors
After empirical research which aim was to analyze the relevance of CBOE VIXglobal
and VIXlocal implied volatility indices to stock market returns in emerging and developed
countries it was found that:
1. Correlation between VIXglobal and VIXlocal is moderate or weak (except for Brazil).
For this reason both VIXglobal and VIXlocal should be used to determine stock market
returns in all countries under consideration while forming the portfolio.
2. Intensified situation in the US is immediately reflected in two European markets and the day later in Japan and Australia showing that stock market returns in developed countries and VIXglobal diverge. It basically agrees with Gemmil et al. (1997) who
stated that IV changes in one market at t-1 are transmitted to the other market at time t, and the study of Chulia et al. (2009) that reported the volatility transmission from US to EZ. In the meantime, the stock index returns in the emerging markets maintain weak or very weak relationship with VIXglobal at time t and t-1 as US percentage of
total portfolio investment assets in those countries is relatively small (except for Brazil which market’s liquidity is relatively low) . VIXlocal at time t better reflects the
investors’ sentiments in home markets while VIXlocal at t-1 does not.
3. The contemporaneous negative correlation between VIXglobal and returns of MSCI
stock market sectors is weak in both emerging and developed countries, contrary to the results of main stock market indices where developed countries indicated moderate negative relationship with VIXglobal. Meanwhile, the correlation between
VIXlocal and returns of MSCI stock market sectors at time t better reflects the
investors’ sentiments in home markets. On the other hand, both lagged VIXglobal and
lagged VIXlocal show very weak correlation in sector indices of emerging and
developed countries.
4. As a result, if foreign investors are risk averse they would tend to diversify portfolios by including stocks that have low correlation with VIXglobal and thus would prefer
emerging market equities. BRIC countries are further reducing their dependence on developed countries as they established BRICS Bank and formed BRICS exchange alliance. Although the internal risk of emerging markets is still high, after the situation analysis conducted in the first part of this work it is seen that those
economies improve and thus getting more attractive to investors. On the other hand, investors whose portfolios include stocks that have moderate or strong negative correlation with VIXglobal could curb the risk by investing in VIX index, tradable
asset, derived from S&P500. If volatility goes down, short VIXglobal. If volatility goes
up,long VIXglobal.
5. According to research results both VIXglobal and VIXlocal are useful implied volatility
measures that are relevant to determine stock market returns in three emerging markets (IN, RU, CN) and three developed countries (JP, AU, DE). The returns of Brazilian and UK stock market main indices proved to be undeterminable by VIXglobal
and VIXlocal (with Brazilian MSCI indices being an exception). Mobarek et al. (2014)
also supported the existence of weak form of EMH in Brazil while Shiller (2013) argued EMH are partially true as they fit well to the modern stock market analysis where information flows and trade execution is faster than ever (i.e. UK). Importantly, the regression analysis showed Chinese market being as a safe haven investment for both main stock market index and MSCI sector indices (IND, CS, CD, TC, UT, FN, EN) presumably because of its strict regulation on capital control. The investment in this market and its sectors helps to mitigate the risk exposure during the market turmoil. Nonetheless, Chinese VIXlocal has the lowest standard deviation and thus
swings the least among the countries under consideration indicating uneventful and stable Chinese market with daily MSCI sector average returns of 0,04%.
Limitations and Implications for Further Research
Data range discussed in the thesis is taken from the beginning of 2009 to the end of September of 2014 in order to avoid financial crises of 2007-2008. Therefore, as the period of the recent economic downturn is not the main subject of this work, further research could be done regarding this topic.
Some indices were studied under the shorter period of time due to the lack of data: Firstly, Brazilian main and sectors indices were considered from 2011to the end of September of 2014 (Table 8, p. 39); secondly, MSCI RS_CD sector index – from 2009 to 11 05 2009,
MSCI RS_HC sector index – from 2009 to 26 05 2010; and MSCI CN_HC sector index – from 29 05 2009 to the end of September of 2014.
The thesis also concentrates on the specific sector indices composed by MSCI. Their composition availability and weighted structure is limited and thus conclusions might be less accurate regarding stock market sector analysis. As a result, the further researchers might compose by themselves the sector specific indices of countries under considerations rather than relying on MSCI indices.
The work pays attention only to the stock markets as they are more volatile than other securities. Thus the sentiment indices are reflected better in stock markets. However, for the further research other financial assets such as bonds might be considered.
In this thesis only VIXglobal and VIXlocal implied volatility indices and their relevance
to the stock markets were discussed even though there are many other sentiment indicators such as Zew Economic sentiment, Consumer confidence Indicator (CCI) and others. As a result, the research could be expanded considering more sentiment indicators. Moreover, there could be added more independent variables such as GDP, inflation, markets’ liquidity level or volatility of exchange rates. Notably, the major events discussed in the situation analysis were not excluded and thus could affect the results. It would be recommended to perform this analysis taking into consideration the time horizon before, during or after the major market shocks in order to determine the stock market returns by implied volatility indices. Furthermore, only the countries of one region (i.e. Asia) could be taken into
consideration and compared with each other willing to estimate if the sentiment indicator is equally relevant to all region.
Nonetheless, linear regression model was used as it is performed by many other analysts. However, further researchers might consider more developed non-linear models that better reflect financial data.
Conclusions
1. The first part of this thesis concentrated on the implied volatility levels and current economical situation in four emerging markets (BRIC) and four developed markets (DE, UK, JP, AU), the cross market investment environment landscape, and the major market events that are obviously reflected as shocks in stock market indices during 2009-2014. Finally, the MSCI stock market sectors of the chosen countries were described.
1.1. The markets become more and more interdependent and the transmission of volatility increases. Currently the fear level in the markets intensified as there were more VIX futures contracts traded on October 15th than over the entire first 3 years the VIX futures were in existence. As a result, the recent volatility level in the market is high. 1.2. The analysis revealed that five major events were significantly reflected into the stock
market indices and sentiment indices in all countries under consideration during 2009- 2014. Firstly, the Dodd Frank Wall Street Reform and Consumer Protection Act were released in 2010. Then, the downfall of 2011 in Asia, Europe, Middle East and the US stock markets due to the fears that European sovereign debt crisis could be transmitted to Italy and Spain. At the same time S&P downgraded the America’s credit rating from AAA to AA+. Fourthly, the stock markets were shuck by earthquake and tsunami in Japan on March 2011. Finally, in early March of 2014 first round of sanctions were issued against Russia.
1.3. The importance of emerging markets is significantly increasing as they have become integrated part of the global equity portfolio allocation. According to MSCI Emerging Markets Index, those countries composed of only 1% of world market cap in 1988 while currently accounts for around 11% of world market cap. Although these countries produce higher yields, they also face higher risk arising from the lack of liquidity, more volatile exchange rate movements, heavy government interventions
and other factors that make investments less reliable. Contrary to emerging markets developed countries (DE, UK, JP, AU) present more stable and more attractive investment environment. Their relatively higher economic growths, stricter regulation policies and better ranks in terms of economic freedom indicators show the strength of those countries.
1.4. Investors become more interested in geographical portfolio diversification that allows investors to have a larger basket of foreign securities and better return-to-volatility ratio. The US is the top one economy by size of portfolio investment liabilities according to the IMF (2013). Therefore, any turbulence in the US might be reflected in the rest of the world.
2. The correlation analysis showed that the state of VIXglobal as a global volatility indicator
might be questionable and thus both VIXglobal and VIXlocal should be considered to
determine the returns of underlying stock market indices alongside their sectors. 2.1. VIXglobal and returns of British and German stock market indices diverge at time t.
2.2. Presumably due to the time lag VIXglobal at t-1 (rather than VIXglobal at time t) diverges
from returns of Australian and Japanese stock market indices at time t.
2.3. The returns of stock indices in the emerging markets maintain weak or very weak relationship with VIXglobal at time t and t-1 (except for Brazil which market’s liquidity
is relatively low). VIXlocal at time t better reflects the investors’ sentiments in home
markets while VIXlocal at t-1 does not.
2.4. The contemporaneous negative correlation between VIXglobal and returns of MSCI
stock market sectors is weak in both emerging and developed countries. Meanwhile, the correlation between VIXlocal and returns of MSCI stock market sectors at time t
lagged VIXglobal and lagged VIXlocal show very weak correlation in sector indices of
emerging and developed countries.
3. Except for major stock market indices of UK and Brazil, the stock market returns do not follow random walk and might be predicted by implied volatility indices. The regression analysis indicates that the changes in both VIXglobal and VIXlocal are significant for
determination of stock market returns in both emerging and developed countries. As VIXglobal or VIXlocal increases the returns of stock index decrease except for China where
an increase in VIXglobal and VIXlocal lead the stock market returns also to increase. Thus
Chinese market as well as its sectors might be considered as a safe haven investment relative to VIXglobal and VIXlocal analysis.
4. As far as sector volatility goes, a major problem is lack of sector-specific implied volatility indicators. Nevertheless, VIXglobal does not perform uniformly well across all
sectors and the results are not as conclusive as the country-based analysis. In addition, MSCI indices were taken into consideration that further makes the results of analysis less reliable as their real composition is not freely available. However, the majority of stock market sector returns might be determined by implied volatility indicators VIXglobal and
References
Adkins, L.C. (2014). Using gretl for principles of econometrics. Retrieved from: http://www.learneconometrics.com/gretl/using_gretl_for_POE4.pdf Allen, F., Brealey, R., & Myers, S. (2011). Principles of corporate finance. New York:
McGraw-Hill/Irwin.
Baca, S. P. , Garbe, B. L., & Weiss, R., A. (2000). The rise of sector effects in major equity markets. Financial Analyst Journal, 34-40.
Bailey, W., Kumar, A., & Ng, D. (2011). Behavioral biases of mutual fund investors. Journal of Financial Economics, 102 (1), 1–27.
Bernard, V., & Thomas, J. (1990). Evidence that stock prices do not fully reflect the implications of current earnings for future earnings. Journal of Accounting and Economics, 13, 305–340.
Bernard, V., Thomas, J. (1989). Port-earnings-announcement drift: Delayed prices response or risk premium? Journal of Accounting Research, 27, 1-36.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Boscaljon, B., Filbeck, G., & Zhao, X. (2011). Market timing using the VIX for style rotation. Financial Services Review, 20, 35-44.
Cavaglia, S., Brightman, C., & Aked, M. (2000). The increasing importance of industry factors. Financial Analyst Journal, 41-54.
CBOE. (2014). All abou VIX. Retrieved from:
http://cfe.cboe.com/education/vixprimer/about.aspx
Chulia, H., Climent, F. Soriano, P., & Torro, H. (2009). Volatility transmission patterns and terrorist attacks. Quantative Finance, 9(5), 607-619.
Cootner, P. (1962). Stock prices: Random vs. systematic changes. Industrial Management Review, 3, 24–45.
Connors, L. (2002). Timing your S&P trades with the Vix. Futures: News, Analysis & Strategies for Futures, Options & Derivatives Traders, 46-47.
Copeland, M. (1999). Market timing: Style and size rotation using the VIX. Financial Analysts Journal, 73-82.
Drummen M., & Zimmermann, H. (1992). The structure of European stock returns. Financial Analyst Journal, 15-25.
Dungey, M., Fry, R., & Martin, V. L. (2003). Equity transmission mechanism from Asia to Australia: Interdependence or contagation? Australian Journal of Management, 28(2), 157-180.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance. Econometrica, 50, 987–1007.
Fama, E., F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 383-417.
Fama, E. (1963). Mandelbrot and the stable Paretian hypothesis. Journal of Business, 36, 420–429.
Fama, E., & Blume, M. (1966). Filter rules and stock market trading profits. Journal of Business, 39, 226–241.
Fassas, A.P., & Siriopoulos, C. (2012). The efficiency of the VIX futures market: A panel data approach. The Journal of Alternative Investments, 55-65.
Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: A new measure. Journal of Futures Markets, 15, 265–302.
Forbes, K., Rigobon, R. (2002). No contagation, only interdependence : Measuring stock market comovements. Journal of Finance, 57(5), 2223-2261.
Gemmill, G., & Kamiyama, N. (1997). International transmission of option volatility and skewness: when you’re smiling, does the whole world smile? City University Business School, London, 95-117.
Gervais, S., & Odean, T. (2001). Learning to be overconfident. Review of Financial Studies, 14(1), 1–27.
Goldman Sachs. (2014). The buzz: We think volatility is too high. Options reseach. Global Investment Reseach by the Goldman Sachs Group, Inc., 1-19.
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70, 393–408.
Gujarati, D. N. (2004). Basic econometrics. Fourth edition. Retrieved from:
http://egei.vse.cz/english/wp-content/uploads/2012/08/Basic-Econometrics.pdf Heritage Foundation. (2014). 2014 index of economic freedom. Retrieved from:
http://www.heritage.org/index/
Hu, L. (2006). Dependence patterns across financial markets: A mix cupola approach. Applied Financial Economics, 16(10), 717-729.
International Monetary Fund. (2014). Coordinated investment portfolio survey. Retrieved from: http://cpis.imf.org/
Jagric, T., Podobnik, B., & Kolanovic, M. (2005). Does the efficient market hypothesis hold? Evidence from six transition economies. Eastern European Economics, 43 (4), 79– 103.
Karolyi, G. A., & Stulz, R. M. (1996). Why do markets move together? An investigation of U.S.-Japan stock return comovements. The Journal of Finance, 51 (3), 951-986. Keim, D. (1983). Size-related anomalies and stock return seasonality: Further empirical
King, M., A., & Wadhwani, S. (1990). Tranasmission of volatility between stock markets. Review of Financial Studies, 3(1), 5-33.
Laidroo, L., & Grigaliūnienė, Z. (2012). Testing for asymmetries in price reactions to quarterly earnings announcements on Tallinn, Riga and Vilnius stock exchanges during 2000–2009. Baltic Journal of Economics, 12 (1), 61–86.
Leroy, S. (1973). Risk aversion and the martingale property of stock returns. International Economic Review, 14, 436–446.
Lo, A., & MacKinlay, C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41–66.
Loh, L., Martellini, L., & Stoyanov, S. (2013). Assessing volatility indicators: The benefit of local equity volatility indices. Bankers, Markets & Investors, 124, 50-59.
Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. Journal of Finance, 56(2), 649-676.
Loretan, M., & English, W. (2000). Evaluating changes in correlations during periods of high market volatility. Global Investor, 135, 65.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Mcewan, R. (2004). Getting your S&P fix with the Vix. Futures: News, Analysis &
Strategies for Futures, Options & Derivatives Traders, 46-48.
Mobarek, A., & Fiorante, A. (2014). The prospects of BRIC countries: Testing weak-form market efficiency. Research in International Business and Finance, 30, 217-232. Moran, M.T., & Dash, S. (2007). VIX Futures and options: Pricing and using volatility
products to manage downside risk and improve efficiency in equity portfolios. The Journal of Trading, 96-105.
Moraux, F., Navatte, P., & Villa, C. (1999). The predictive power of the French market volatility index: A multi horizons study. European Finance Review, 2, 303–320.
Moskow Exchange. (2014). BRICS exchanges alliance. Retrieved from: moex.com/s506
Natarajan, V. K., Singh, A. R. R., & Priya, N. C. (2013). Examining mean-volatility spilovers across national stock markets. Journal of Economics, Finance and Administrative Science, 19, 55-62.
Ozsoylev, H. N. (2008). Amplification and asymmetry in crashes and frenzies. Annals of Finance, 4, 157-181.
Peng, Y., & Ng,W. L. (2012). Analysing financial contagion and asymmetric market dependence with volatility indices via copulas. Spinger, 8, 49–74.
Pinheiro, M. (2005). Information asymmetries and a multiplier effect on price correlation and trading. Annals of Finance, 1, 395-421.
Poon, H., Rockinger, M., & Tawn, J. (2000). Extreme value dependence in financial markets: Diagnostics, models, and financial implications. The Review of Financial Studies,17 (2), 581–610.
Rigobbon, R. (2003). Identification through heteroskedasticity. Review of Economics and Statistics, 85 (4), 777-792.
Samuelson, P. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6, 41–9.
Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? American Economic Review, 71, 421–36.
Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83–104.
Shiller, R. J. (26 October 2013). Sharing Nobel honors, and agreeing to disagree. The New York Times. Retrieved from: http://www.nytimes.com/2013/10/27/business/sharing- nobel-honors-and-agreeing-to-disagree.html
Simon, D. P. (2003). The Nasdaq volatility index during and after the bubble. Journal of Derivatives, 9–24.
Soriano, P., & Climent, F. (2006). Region vs. industry effects and volatility transmission. Financial Analyst Journal, 62 (6), 52-64.
Skiadopoulos, G. (2004).The Greek implied volatility index: Construction and properties. Applied Financial Economics, 14, 1187–1196.
Wagner, N., & Szimayer, A. (2004). Local and spillover shocks in implied market volatility: Evidence for the US and Germany. Research in International Business and Finance, 18, 237–251.