Risk Perspectives for Commodities and Stock Markets in India
This study provides opportunities to decision−makers for hedging the fluctuations of one market. In this paper, an attempt has been made to examine the nature of risk in Indian commodity (Agri products) and stock markets. To measure the volatility of the selected markets, averages and standard deviations were used, and to study the direction between the prices of commodity and stock markets, Pearson’s coefficient of correlation was applied. It was observed that Indian commodity and stock markets were not similar on the basis of risk.
Key Words: Commodity market, Share market, Risk,
When an individual, an institution, a company and a group of any of these want to park their funds to earn yield in future, the volatility of market needs to be understood. If an investor wants to go with share market and commodity market, the volatility in overall markets would require thorough analysis before making an investment decision. This overall market volatility will help investors to understand the nature and quantity of overall market risks and this analysis helps to protect decision−makers’ funds from risks of speculative market operations. In the present research work, some efforts have been made by the researcher to understand the nature of risks and to provide some significant risk based indicatiors to safely play in commodity or stock markets.
OVERALL MARKET RISKS (IN COMMODITY AND STOCK MARKETS)
The overall market risk refers to the rate of gyration in index prices of that market or on the other hand, it is the probable deviation from the expected index prices.
REVIEW OF LITERATURE
In the past, various research works have been conducted on this topic and some of the renowned works are mentioned below:
Kumar, et. al., (2008) made an empirical study of volatility, risk premium and seasonality in risk−return relation of the Indian stock market and commodity market and commodity markets. Volatility clustering and asymmetric nature were examined for Indian stock and commodity markets. The risk−return relationship and seasonality in risk−return were also analyzed by the researcher with the help of index S&P CNX Nifty for a period of 18 years and with the help of commodity (Gold and Soybean) prices.
They found that, asymmetric property was showed by stock and commodity markets and risk−return−relationship was positive though insignificant for Nifty and Soybean whereas significant positive relationship was found in the case of Gold.
Roy, et al. (1995) focused on two key issues, namely: (a) what is the average level of volatility and whether it has increased in the current period; (b)whether the present trend of share price
movement is likely to impair the development process of our economy through the study ’Stock Market Volatility: Roots and Results’. In this study, they examined that, several volatility measures based on different price indices had been used to evaluate the stock price movements in historical perspective. In such, instance, the conclusion was essentially the same, i.e., stock market volatility had increased in that period if the changes in share prices had been in response to fundamental economic factors.
Ruddy, (1996) analyzed the volatility of securities traded on the National Stock Exchange ( NSE) and the Bombay Stock Exchange (BSE). In this study, researcher employed stock market trading data relating to about 3,000 securities traded on Bombay Stock Exchange (BSE) and over 1,000 securities traded on NSE. Volatility of individual securities was also analyzed in this study and it was found that the securities traded on BSE had more volatility than the securities traded on NSE. The researcher also showed through this study that Indian Capital Markets were highly volatile.
Hunjra, et.al. (2011), made a study to determine the risk and return relationship on the basis of univariate modeling approach. To attain this objective the data were analyzed regarding gold price, cotton price and sugar price along with KSE 100 index (for the time period of ten years) by the researchers. They found that, asymmetric and seasonal effects were present in commodity market and stock market but asymmetric properties and seasonal effects were most dominant in the stock market prices comparative to other commodities.
OBJECTIVES OF THE STUDY
The objectives of the present study are:
1. To study the nature of risk within markets (Commodity and Stock markets).
2. To study the relationship between selected indices under the study.
STUDY AREA AND UNITS
The study is concerned with the indices selected from the Indian commodity and share segment. One index (Dhaanya) was selected from the National Commodity and Derivatives Exchange Ltd (NCDEX) and another index (Sensex−30) was taken from Bombay Stock Exchange (BSE). Both markets (NCDEX and BSE) were matched for their technology, transparency, independent board of directors and professional management. Unit Profile: Unit wise profile of the selected units is given below: Sensex−30: Sensex is the pulse of the Indian stock markets. It was launched in 1986 by Bombay Stock Exchange to represent the whole markets´ trend. It represents the trend in whole market on the basis of gyration in prices of its’ 30 constituents and for the purpose of index calculation Free−float Market Capitalization methodology is used. Dhaanya: Dhaanya is an agricultural commodities index computed by National Commodity and Derivatives Exchange Ltd. The index values are calculated using the prices of 10 agricultural commodities traded on the NCDEX platform. The components of Dhaanya are selected from diverse sub sectors of the Indian agri industry and account for nearly 70% of trading on the NCDEX platform.
A total of two indices (Dhaanya and Sensex−30.) were selected by accidental sampling. Sampling design is given in the Table1.
PROCEDURE OF DATA COLLECTION
Analysis of every research work is based on relevant data and it can be collected by two ways: by way of primary data collection and by way of secondary data collection. In our study, secondary data about two units have been collected from different sources, these were: (A) News papers:− (i) The Economic Times, and (ii) The Times of India (B) Websites:− (i) Website of Bombay Stock Exchange of India −http://www.bseindia.com (ii) Website of National commodity and Derivatives Exchange −http://www.ncdex.com Data collected from all the sources were matched in the manner of consistency and finally the data (closing prices related to indices) collected from the website of BSE and website of NCDEX were taken for the analysis purposes.
Every research work is always limited by shortage of time and resources. Therefore, under the study, prices of selected units from April, 2011 to March, 2012 were analyzed by the researcher with the help of mean, standard deviation and Pearson’s correlation.
To meet the objectives of the study, raw data were treated with different kinds of analysis. For carrying out the analysis the different types of statistical tools were used like, to know the volatility within the markets, mean and standard deviation were used.
To study the nature of risk within the markets the volatility was measured in the closing prices of Sensex−30 and Dhaanya indices with the help of averages and standard deviations. The data are of monthly nature. Month to month volatility was arrived at by assessing the standard deviations. It was found that volatility was increasing or decreasing in the reverse trend of the prices of Sensex−30 and it was uncertain of nature when prices of Sensex− 30 were in stable position (as indicated in Table− 2 and Chart−1 and 2). Volatility in prices of Dhaanya index was uncertain of nature during the period of April to November and after that, it was going up in same direction as index prices (as indicated in Table−2 and Chart−1 and 2).
To examine the association between the movements of Senex−30 and Dhaanya indices the daily closing prices of monthly nature were analyzed with the help of Pearson’s coefficient of correlation and then it was found that the prices of both indices were negatively correlated in many months. High level of negative association was found in the month of December only, moderate negative correlation was found in the months of April, May, July, September and March and low level of negative correlation was found in the months of August and November. High level of positive correlation was found in the prices of both indices in the months of October and January and moderate positive correlation was found in the months of June and February.
1) The risk trend in the value of Sensex−30 was reverse to price movements. 2) The risk in the value of Dhaanya index was of gyrated nature. 3) Relationship between both the indices was found negatively correlated in many of the months.
With the above study it can now be concluded that Indian commodity markets and stock markets are dissimilar on the basis of nature of risk. This study also infers that, wiser investment decisions can be taken by investors or they can select the more appropriate market through understanding of the overall risk within the market. This study also leads the decision− makers to play a strategic game against the risks between two and more than two markets.
1. Kumar, Brajesh and Singh, Priyanka, "Volatility Modeling, Seasonality and Risk− Return Relationship in Garch−in−Mean Framework: The Case of Indian Stock and Commodity Markets", The 5th Conference of Asia−Pacific Association of Derivatives Paper, June−2008, Available at SSRN: htpp://ssrn.com/abstract=1140264 2. Hunjra, Ahmad Imran, Azam, Muhammad, Niazi, Ghulam Shabbir Khan, Butt, Babar Zaheer, Rehman, Kashif Ur and Azam, Rauf I., "Risk and Return Relationship in Stock market and Commodity Prices: A Comprehensive Study of Pakistani markets", World Applied Sciences Journal, Vol. 13, No. 3, 2011, pp.470−481. 3. Roy, Malay K and Karmakar, Madhusudan (1995), "Stock Market Volatility: Roots and Results", Vikalpa, Vol. 20, No. 1, January −March 1995, p.p. 37−48. 4. Reddy, Yarram Subha (1996), "Volatility of Securities Traded on the National Stock Exchange and Bombay Stock Exchange: A Comparison", Decision, Vol. 23, No. 1−4, Jan−Dec 1996, p.p. 1−24.
Chart−1: Month wise Mean Prices of Indices
Chart−2: Month wise Volatility of Indices