An Exploration into the Volatility of Equity Stocks in the Kuwait Economy Table of Contents Table of Contents 2 Key words 3 Aims & Background 3 Literature Review 4 Research Questions & Methods 5 References 7 Key words Kuwait Stock Exchange, Statistical measures, Volatility, Exchange Traded Funds, futures, capitalization rate of Kuwait, X-stream trading system, SMARTS surveillance system, Coefficient technique, long-run predictive variance, multi-period risk and return variance. Aims & Background The current study will be outlining whether stocks are least volatile in the Kuwait financial market than the other investment instruments. Main aim of the study is to measure dispersion involved in the equity investment options of Kuwait.
Statistical measures will be used to outline the level security or risk associated in the market index (Favero & Nucera, 2014). Volatility of the market options of the Kuwait Stock Exchange will be judged on the basis of Exchange Traded Funds and justification of futures and options in worldwide appearance. Moreover, the study will put a shed on the investment atmosphere of Kuwait and risks involved in it (Canarella, Miller & Pollard, 2010). It has been observed that capitalization rate of the Kuwait Stock Exchange market is highest among the Middle Eastern countries.
The equity market reported with the presence of almost two hundred companies, who are using their equity shares to raise funds. The stock exchange is having the market value of more than 28 billion KD or USD 100 billion (Arumugam, 2013). It offers high market capitalization rate in comparison to the GDP of the country. The ratio represents nearly 100% of the entire GDP of the Kuwait (Kse. com. kw, 2015). The system represents risk profile in highly informative manner.
They came into a “partnership” contract with NASDAQ OMX, which is the leading the stock exchange set up in the world. The organization is providing highly efficient trading analysis, exchange or transaction technologies, information regarding various public limited companies (Kse. com. kw, 2015). It has been observed that the KSE has started to use “X-stream trading system” after the tie up of NASDAQ deal (Kse. com. kw, 2015). The collaboration has also offered the equity investors in promoting the set up with “SMARTS surveillance system” (Kse. com. kw, 2015). The new system offered investors and other stakeholders with high transfer of knowledge and analytical experience.
NASDAQ OMX introduced huge number of financial experts who helped in gaining higher insights on the modern commercial markets of the Middle Eastern country. “The SMARTS surveillance system” started working from the year 2010 and X-stream used from 2012. Both the systems are helping the investors in meeting the fragile economic conditions (Kse. com. kw, 2015). Literature Review Volatility can be defined as the uncertainty or risk associated with investment made in equity stock (Favero & Nucera, 2014).
It refers to the level of risks allied with the change of investment volume, which will be measured with the help of the statistical technique like standard deviation and variance. Such numerical methods will calculate the difference between returns of the securities in distinct time or market index (Canarella, Miller & Pollard, 2010). Henceforth, it can be said that the equity, debenture, preference shares, money market instruments, mutual funds, real estate, derivatives and non marketable securities expressing high volatility represents higher level of risk (Brandt & Kang, 2002). In the financial investment market, option pricing formulas are gaining higher importance among stock exchanges.
Return from any underlying asset outlines high fluctuation which is calculated with the help of option expiration evaluation (Gregoriou, 2009). Volatility is calculated with the help of coefficient percentage and option-pricing formulas. Mainly, unpredictability of the investments is higher in the daily trading policies. Coefficient technique is used in outlining the changes in the level of risk (HansooYoo, 2013). Volatility of stock returns is calculated on annual basis so that the contrast of stock markets can be outlined. Instability of the stock markets is higher in the shorter prospects whereas, it is lower in the longer investment horizons.
Mean reversion is one of the highly used techniques for calculating return predictability in any type of market (HansooYoo, 2013). There are mainly five components that are used in judging the “long-run predictive variance” (Karunanayake, Valadkhani & Obrien, 2010). They are i. i.d. uncertainty, mean reversion, uncertainty regarding the expected future returns, insecurity of existing predictable returns and risk estimation (Favero & Nucera, 2014). In order to outline the unpredictability of the long-horizon discrepancy and parameter hesitation, different techniques are used.
Mainly conditional variance and components of long-horizon variance techniques are used (Kassimatis, 2011). Empirical frameworks of the outlining stock risk levels are one of the predictive systems components which use predictive variance. Major stock exchanges are using the techniques like priors and posteriors techniques in order to predict multi-period risk and return variance. On the contrary, strength of the equity markets are calculated with the help of substitute samples, model of uncertainty and time varying volatility (Schwartz, Byrne & Colaninno, 2011).
It has been observed that the target-date funds are gaining popularity as investment assets. Debenture, preference shares, money market instruments, mutual funds, real estate, derivatives and non marketable securities are the major options for the investors (Kassimatis, 2011). Mainly, volatility can be checked with the statistical methods like measure of dispersion, standard deviation, variance analysis and co-efficient and correlation methods (Pastor & Stambaugh, 2012). Research Questions & Methods H0: Stocks are least volatile in the Kuwait stock market. H1: Stocks are highly volatile in the Kuwait financial investment market. The above mentioned hypothesis will be used in the study to prove that the volatility of the equity share is lower than the other investment options like, debenture, preference shares, money market instruments, mutual funds, real estate, derivatives and non marketable securities.
The study will be outlining change of risk level which will be faced while investing in Kuwait Stock Exchange (Favero & Nucera, 2014). The hypothesis will be testing the level of risk instability focused on KSE instruments. In order to justify the marketplace, different methods will be used.
Mainly, the study will be outlining the volatility of stock of Kuwait in respect to the international standards (Schwartz, Byrne & Colaninno, 2011). While conducting the research, the researcher will be having options of three different philosophies like positivism, interpretive and realism. In the current study, positivism philosophy will be used to analyse volatility of stocks (Canarella, Miller & Pollard, 2010). Research approaches can be inductive or deductive. Deductive approach will be selected so that different financial risk related theories can be used and newly invented theories are used to investigate on the current topic.
On the other hand, the researcher can use the exploratory, explanatory and descriptive research designs. In case of the current study, descriptive design will be selected to analyze Kuwait based stock volatility (Brandt & Kang, 2002). Current topic requires information on the KSE risk volatility, which requires data collection from both primary and secondary sources. This study will be using the secondary data sources like the market snapshots, index chart and sartorial indices (Kse. com. kw, 2015). Such data collection techniques will help in contrasting the historical market data (Kse. com. kw, 2015). Quantitative data will be used to gain more insight on the Kuwait based financial and stock exchange market.
In addition, collected data will be analyzed by using “measure of dispersion, standard deviation, variance analysis and co-efficient and correlation methods”. Microsoft excel software will be used for calculation and data analysis (Schwartz, Byrne & Colaninno, 2011). Moreover, researcher needs to maintain the ethical issues like proper data application, benevolent involvement of respondents and maintaining anonymity among the data source. On the contrary, the research can face the complexity or limitations like authenticity, time issues and budget restrictions.
References Brandt, M. & Kang, Q. (2002). On the relationship between the conditional mean and volatility of stock returns. Cambridge, MA.: National Bureau of Economic Research. Canarella, G., Miller, S., & Pollard, S. (2010). NAFTA stock markets. New York: Nova Science Publishers. Arumugam, D. (2013). Stock Market Seasonality- Time Varying Volatility In The Emerging Indian Stock Market. IOSR Journal Of Business And Management, 9(6), 87-103. Favero, C. & Nucera, F. (2014). How Much Does the Stock Market Risk Decline with the Investment Horizon? A Cross-Country Comparison.
Economic Notes, 43(1), 1-19. Gregoriou, G. (2009). Stock market volatility. Boca Raton: CRC Press. HansooYoo, C. (2013). The Lead-Lag Relationship between Stock Market Volatility and Housing Market Volatility in the U. S. International business education review, 10(3), 65-86. Kse. com. kw, (2015). About KSE - Who We Are? Retrieved 23 June 2015, from http: //www. kse. com. kw/EN/AboutKSE/Pages/WhoWeAre. aspx Karunanayake, I., Valadkhani, A. & Obrien, M. (2010). Financial Crises and International Stock Market Volatility Transmission. Australian Economic Papers, 49(3), 209-221. Kassimatis, K. (2011). Risk Aversion with Local Risk Seeking and Stock Returns: Evidence from the UK Market.
Journal of Business Finance & Accounting, 38(5-6), 713-739. Pastor, L., & Stambaugh, R. (2012). Are Stocks Really Less Volatile in the Long Run? The Journal Of Finance, LXVII(2), 431-477. Schwartz, R., Byrne, J. & Colaninno, A. (2011). Volatility. New York: Springer.