A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Singh, Shveta
- An Empirical Analysis of Relation between Income, Consumption and Investment of Rural Haryana
Authors
1 Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar-125001, Haryana, IN
Source
Indian Journal of Economics and Development, Vol 4, No 10 (2016), Pagination: 1-7Abstract
Objectives: The present study aims to analyse the Relation between Income, Consumption and Investment of Rural Haryana.
Methods/Statistical analysis: This study is based on the primary data A questionnaire is prepared and the personal interviews method is used to collect the primary data from rural household. The sample selected involved 100 households. Stratified random sampling technique is used for sample selection. Haryana is divided into four divisions for administrative purpose that is Ambala, Rohtak, Gurgaon and Hisar. Data collected is analyzed by using regression analysis and MANOVA.
Findings: The regression outcome shows that there is a significant relationship between household income and household consumption expenditure and there is also a significant relationship between household income and household investment in rural Haryana. As the income of the household increases, simultaneously there is high increase in consumption expenditure as compared to investment. MANOVA results revealed that there is no significant relationship between income and consumption expenditure, but it is significant in case of total investment
Application/Improvements: In Haryana none of the study has been conducted to measure or analyse relationship between income, consumption and investment of rural household especially at micro level. Most of the studies on income, consumption and investment pattern of rural people are based on secondary data which sometimes does not prove to be adequate for the study. Most of the data available does not serve the needs of Haryana in a ground level prospective. So the current research paper seeks to analyse the relationship between Income, Consumption and Investment of rural Haryana.
Keywords
Income, Consumption, Investment, Regression Analysis, Rural Household, Relationship.References
- Sethia Savneet. India’s changing consumption pattern, GYANPRATHA-ACCMAN Journal of Management. 2013; 5(2), 1-12.
- Chaturvedi Meenakshi, Khare Shruti. Study of saving pattern and investment preferences of individual household in India. International Journal of Research in Commerce & Management. 2012; 3(5), 115-120,
- Bhardwaj Dr. Bhawana, Sharma Dr. Nisha, Sharma Dr. Dipanker. Income, Saving and investment pattern of employees of Bahra University, Solan. International Journal of Management & Business Study. 2013; 3(1), 137-141.
- H. Chudali, A. Choudhury, H. Ali Md. Socio- economic analysis of consumption patterns of nepalese people, Economic Affairs. 2011; 56(2), 213-218.
- Sethi Narayan, Pradhan Hemanta Kumar. The patterns of consumption expenditure in rural households of Western Odisha f India: An engel ratio analysis. OIDA International Journal of Sustainable Development. 2012; 05(04), 107-128.
- Rao Dr. Adusumalli Venkateswara, Saheb Dr. Bhanu Bhaba. Consumption expenditure pattern of rural households (A case study in Guntur district of Andhra Pradesh), International Journal of Multidisciplinary Educational Research. 2012; 1(1), 237-245.
- Oldiges Christian. Cereal consumption and per capita income in India, Economic & Political Weekly. 2012; 47(6), 63-71.
- Dachin Anca, MosoraLiviu-Cosmin. Influence factores of regional household income disparities in Romania. Journal of Social and Economic Statistics. 2012; 1(1) 78-93.
- Bairagi Prof Ujwala, Rastogi Prof Charu. An emperical Study of saving pattern and investment preferences of individual household with reference to pune city. ASM's International E- Journal of Ongoing Research in Management and IT. 2013; 1-11.
- Tiwari Aviral Kumar, Shahbaz Muhammad, Islm Faridul. Does financial development increase rural- urban income inequality? Co-integration analysis in the case of Income economy. International Journal of Social Economics. 2013; 40(2), 151-168.
- Naranpanawa Athula, Selvanathan Saroja and Bandara Jayatilleke. Empirical Income Distribution: The Case of Sri Lanka. International Journal of Social Economics. 2013; 40(1), 26-50.
- Akhil K. Antony and Prasad Syam. Food consumption and nutritional intake in rural India: Emerging trends and patterns. Indian Journal of Economics and Development. 2015; 3(12), 1-10.
- Roy Chandan, Mukherjee Sanchari Roy. An analytical study on determinants of income generation in rural sericulture sector of West Bengal. Indian Journal of Economics and Development, 2015; 3(2), 168-180.
- Modelling the Volatility of Banking Sectors of National Stock Exchange
Authors
1 Guru Jambheswar University of Science and Technology, Hisar, Haryana, IN
Source
Indian Journal of Economics and Development, Vol 7, No 3 (2019), Pagination: 1-9Abstract
Objective: To model the conditional volatility of banking sectors of National Stock Exchange, India and to capture its dynamics as volatility clustering, persistence and leverage effect.
Methodology: Volatility is analysed by applying EGARCH model on daily returns data of two sectors namely composite Bank sector (Bank) and PSU Bank sector (PSU).
Findings: It is found that both sectors are showing volatility clustering, significant persistence and leverage effect but PSU bank sector is more prone to negative news and its returns are more volatile, composite Bank sector is less prone to negative shocks due to inclusion of private banks. Volatility shocks take time to die out in both sectors. Volatility of both sectors is explosive in nature.
Applications: Finding is helpful in taking decisions regarding investment and reforms in banking to stabilize the volatility.
Keywords
PSU Bank, Bank and EGARCH Model.References
- T. Bollerslev. Generalized autoregressive conditional Heteroskedasticity. Journal of Econometrics. 1986; 31, 307–327.
- J. Caiado. Modelling and forecasting the volatility of the Portuguese stock index PSI-20. Estudos de Gestão. 2004; 9(1), 3-22.
- M. Karmakar. Modeling conditional volatility of the Indian stock markets. Vikalpa. 2005; 30(3), 21-38.
- L.R. Glosten, R. Jaganathan, D. Runkle. On the relation between the expected value and the volatility of the normal excess return on stocks. Journal of Finance. 1993; 48, 1779–1801.
- H. Goudarzi, C.S. Ramanarayanan. Modeling asymmetric volatility in the Indian stock market. International Journal of Business and Management. 2011; 6(3), 221-231.
- M. Mahmud, N. Mirza. Volatility dynamics in an emerging economy: case of Karachi stock exchange. Economic Research-EkonomskaIstraživanja. 2011; 24(4), 51-64.
- H. Ezzat. The application of GARCH methods in modeling volatility using sector Indices from the Egyptian exchange. 2013; 65-5.
- V. Prabakaran, D.L. Prabha. Analysing the volatility of NSE Indices – empirical study. Journal of Management and Science – JMS. 2012; 2(2), 33-41.
- A.K. Mittal, N. Goyal. Modeling the volatility of Indian stock market. International Journal of Research in IT & Management. 2012; 2(1), 1-23.
- S.Z.S. Abdalla, P. Winker. Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance. 2012; 4(8), 161-176.
- M. Islam. Estimating volatility of stock index returns by using symmetric garch models. Middle-East Journal of Scientific Research. 2013; 18(7), 991-999.
- K.O. Emenike, W.U. Ani. Volatility of the banking sector stock returns in Nigeria. Ruhuna Journal of Management and Finance. 2014; 1(1), 73-82.
- R. Birau, M. Siminica, J. Trivedi. Modeling and estimating long-term volatility of R.P.G.U stock markets. Environment and Financial Planning. 2014; 272-280.
- C. Shankar, K. Ramulu. Volatility and correlation of stock Indices on Indian Stock Market. International Journal of Research in Business Management. 2014; 2(4), 17-26.
- M. Anbukarasi, B. Nithya. Return and volatility analysis of the Indian sectoral Indices-with special reference to NSE. EPRA International Journal of Economic and Business Review. 2014; 2(8), 90-97.
- G. Sevil, M. Kamisli, S. Kamisli. Asymmetry and leverage effect of political risk on volatility: the case of BIST sub-sector. Journal of Applied Finance and Banking. 2015; 5(6), 37-50.
- A.S. Mhmoud, F.M. Dawalbait. Estimating and forecasting stock market volatility using garch models: empirical evidence from Saudi Arabia. International Journal of Engineering Research and Technology. 2015; 4(2), 464-471.
- Anshika. Micro-economic factors affecting stock returns: an empirical study of S&P BSE Bankex companies. Indian Journal of Economics and Development. 2017; 5(2), 1-6.
- R.F. Engle. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society. 1982; 50(4), 987-1007.
- D.B. Nelson. Conditional heteroskedasticity in asset returns: a new approach. Econometrica. 1991; 59(2), 347–370.