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Bala, Anju
- राष्ट्रीय स्वातन्त्र्य आन्दोलन और हिन्दी साहित्य
Abstract Views :309 |
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Authors
Affiliations
1 Village Mandhi Piranu, Mandhi Hariya, Charkhi Dadri. Bhiwani, Haryana, IN
1 Village Mandhi Piranu, Mandhi Hariya, Charkhi Dadri. Bhiwani, Haryana, IN
Source
International Journal of Literary Studies, Vol 2, No 3 (2012), Pagination: 207-208Abstract
No Abstract
- हिन्दी कथा साहित्य में दलित विमर्श
Abstract Views :321 |
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Authors
Affiliations
1 Village Mandhi Piranu, Mandhi Hariya, Charkhi Dadri. Bhiwani, Haryana, IN
1 Village Mandhi Piranu, Mandhi Hariya, Charkhi Dadri. Bhiwani, Haryana, IN
Source
International Journal of Literary Studies, Vol 2, No 3 (2012), Pagination: 211-212Abstract
No Abstract- Prevent Kinnow Produce from Post Harvest Disorders and Diseases
Abstract Views :247 |
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Authors
Affiliations
1 Department of Botany, Punjab Agricultural University, LUDHIANA (PUNJAB), IN
2 Regional Research Station (P.A.U.), Abohar (PUNJAB), IN
1 Department of Botany, Punjab Agricultural University, LUDHIANA (PUNJAB), IN
2 Regional Research Station (P.A.U.), Abohar (PUNJAB), IN
Source
Rashtriya Krishi (English), Vol 9, No 1 (2014), Pagination: 45-46Abstract
Abstract not Given.Keywords
No Keywords given- Molecular Characterization of Bipolaris sorokiniana Populations from Winter Cereals
Abstract Views :300 |
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Authors
Anju Bala
1,
Yogesh Vikal
2
Affiliations
1 Regional Research Station (P.A.U.), Abohar (Punjab), IN
2 School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana (Punjab), IN
1 Regional Research Station (P.A.U.), Abohar (Punjab), IN
2 School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana (Punjab), IN
Source
International Journal of Plant Protection, Vol 8, No 2 (2015), Pagination: 331-337Abstract
Isolates of B. sorokiniana were obtained from infected leaf samples of bread wheat, durum wheat, barley, triticale and rye leaves collected randomly from Gurdaspur, Ludhiana and Ferozepur areas of Punjab. Based on qualitative colony parameters, 30 isolates were selected for further studies. Molecular characterization of the isolates was done using 30 RAPD primers and Polymorphic Information Content values for these ranged from 0.51 to 0.98 with an average of 0.72. A total of 197 alleles were amplified out of which 184 were polymorphic and 13 were monomorphic. The number of alleles amplified varied from 3-13 and size of amplified fragments varied from 3.5 Kb to 200bp.The similarity index values ranged from 0.48 to 0.78 indicating wide range of genetic diversity among isolates. On cluster analysis of the molecular data, the isolates were grouped into two major clusters at 50 per cent level of similarity, whereas at 60 per cent similarity coefficient, the isolates were grouped into 10 clusters. Five complete clusters were formed by 25 isolates whereas 5 isolates formed independent lineages. RAPD profiles, however, did not correlate polymorphism with the geographic source or host source of the isolates.Keywords
Bipolaris sorokiniana, RAPD Markers, Molecular Characterization, Cereals.References
- Burdon, J.J. and Silk, J. (1997). Sources and patterns of diversity in plant pathogenic fungi. Phytopathology, 87: 664-669.
- Christensen, J.J. and Davies, F.R. (1937). Nature of variation in Helminthosporium sativum. Mycologia, 29: 85-99.
- Doyle, J.J and Doyle, J.L. (1990). Isolation of plant DNA from fresh tissue. Focus, 12: 13-15.
- Fetch, T.G. and Steffenson, B.J. (1994). Identification of Cochliobolus sativum isolates expressing differential virulence on two row barley genotypes from North Dakota. Can. J. Pl. Path., 16: 202-206.
- Ghazvini, H. and Tekauz, A. (2007).Virulence diversity in the population of Bipolaris sorokiniana. Plant Dis., 91: 814-821.
- Jahani, M., Aggarwal, R., Srivastava, K.D. and Renu (2008). Genetic differentiation of Bipolaris spp. based on Random Amplified Polymorhic DNA markers Indian Phytopathology, 61: 449-455.
- Jaiswal, S.K., Sweta., Prasad, L.C., Sharma, S., Kumar, S., Prasad, R., Pandey, S.P., Chand, R. and Joshi, A.K. (2007). Identification of molecular marker and aggressiveness for different groups of Bipolaris sorokiniana causing spot blotch disease in Wheat (Triticum aestivum L.).Current Microbiol., 55: 135-141.
- Kaur, S and Nanda, G. S. (2001). Leaf blight epidemic of wheat in 1995-96. J. Res. Punjab Agri. Univ., Ludhiana, 38: 218-223.
- Kumar, J., Schafer, P., Huckelhoven, R., Langen, G., Baltruschat, H., Stein, E., Nagaranjan, S. and Kogel, K. (2002). Bipolaris sorokiniana, a cereal pathogen of global concern: cytological and molecular approaches towards better control. Molec. Pl. Path., 3: 185-195.
- Malvickan, D.K. and Grau, C.R. (2001). Characteristics and frequency of Aphanomyces euteiches races1 and 2 associated with alfalfa in the mid- western United States. Plant Dis., 85: 740-744.
- Mc Dermott, J.M. and Mc Donald, B. A. (1993). Gene flow in plant pathosystems. Ann. Rev. Phytopathol., 31:335-373.
- Muller, M.V.G., Germani, J.C. and Sueli, T.V.D.S. (2005). The use of RAPD to characterize Bipolaris sorokiniana isolates. Genet. Mol. Res., 4: 642-652.
- Nelson, R. R. and Kline, D.M. (1962). Interspecific variation in pathogenicity in the genus Helminthosporium to Graminaceous species. Phytopathology, 52: 1045-1049.
- Oliveira, A.M.R., Matsumura, A.T.S., Prstes, A.M. and Vander, S.T. (2002). Intraspecific variability of Bipolaris sorokiniana isolates determined by random amplified polymorphic DNA (RAPD) Genet. Molec. Res., 1: 350-358.
- Pandey, S., Sharma, S., Chand, R., Shahi, P. and Joshi, A. (2008). Clonal variability and its relevance in generation of new pathotypes in the spot blotch pathogen Bipolaris sorokiniana. Current microbial., 56: 33-41.
- Rao, V. P., Thakur, R.P. and Mathur, K. (1998). Morphological and pathogenic diversity among grain sorghum isolates of Colletotrichum graminicola in India. Indian Phytopath., 51: 164-174.
- Tekauz, A. (2002). Spot blotch (Cochliobolus sativus) infection responses in selected North American barley cultivars at the seedling and adult plant stages. In: Proceedings of 2nd Int. Workshop Barley Leaf Blights ICARDA, Aleppo, Syria, pp 428- 435.
- Tinline, R.D. (1960). Pathogenic and cultural variation in Cochliobolus sativus. Trans. Br. Mycol. Soc., 43: 696-697.
- Vakalovnakis, D.J. and Fragkiadakis, G.A.(1999). Genetic diversity in Fusarium oxysporum isolates from cucumber differentiation by pathogenicity, vegetative compatibility, and the RAPD fingerprinting. Phytopathol., 89: 161-168.
- Wood, L.S. (1962). Relation of variation inHelminthosplorium sativum to seedling blight of small grains. Phytopathol., 52: 493-497.
- Zadoks, J. C., Chang, T.T., and Konzak, C.F. (1974). A decimal code for the growth stages of cereals.Weed Res., 14: 415-421.
- Zhong, S. and Steffenson, B.J. (2001). Virulence and molecular diversity in Cochliobolus sativus. Phytopathol., 91: 469-476.
- Design and Analysis of 32 Bit Regular and Improved Square Root Carry Select Adder
Abstract Views :159 |
PDF Views:2
Authors
Anju Bala
1,
Sunita Rani
1
Affiliations
1 Department of Electronics and Communication Engineering, Punjabi University, Patiala, IN
1 Department of Electronics and Communication Engineering, Punjabi University, Patiala, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 20 (2016), Pagination: 76-82Abstract
In modern VLSI technology transistors size is shrinking day by day for increasing speed and to reduce chip size, performance degradation is one of the major issues. As the technology scale down leakage power dissipation increases exponentially. In this paper a comparison among different parameters of square ischolar_main carry select adders has been presented. These two 32 bit square ischolar_main carry select adders are designed at 32nm technology. Performance of these sqrt carry select adders are evaluated and analysed in terms of delay, average power dissipation, power delay product and transistor count. Simulations are performed at 1.1v, with transistor length at 32nm. Their analysis reveals that improved 32 bit sqrt carry select adder has lesser delay, PDP as well as transistor count as compared to regular 32 bit sqrt carry select adder.Keywords
Square Root CSLA (SQRT CSLA), Binary to Excess Converter (BEC), RCA, ADDER, REGULAR.Full Text
- Estimation and Validation of Actual Evapotranspiration for Wheat Crop Using SEBAL Model over Hisar District, Haryana, India
Abstract Views :288 |
PDF Views:99
Authors
Affiliations
1 Department of Civil Engineering, K. R. Mangalam University, Gurgaon 122 001, IN
2 The Energy and Resources Institute, New Delhi 110 003, IN
3 Department of Civil and Environmental Engineering, The Northcap University, Gurgaon 122 017, IN
4 Centre for Remote Sensing and Geo-Informatics, Sathyabama University, Chennai 600 119, IN
1 Department of Civil Engineering, K. R. Mangalam University, Gurgaon 122 001, IN
2 The Energy and Resources Institute, New Delhi 110 003, IN
3 Department of Civil and Environmental Engineering, The Northcap University, Gurgaon 122 017, IN
4 Centre for Remote Sensing and Geo-Informatics, Sathyabama University, Chennai 600 119, IN
Source
Current Science, Vol 113, No 01 (2017), Pagination: 134-141Abstract
Evapotranspiration (ET) is one of the complex, but essential components of the hydrologic cycle. Advances in remote sensing (RS) and geographical information systems (GIS) have enabled us to estimate ET spatially. In the present study, both, RS and GIS tools have been utilized to estimate the actual crop ET by surface energy balance algorithm for land (SEBAL) model using high spatial resolution satellite image Landsat7 ETM+ for Hisar district, Haryana in north India. Previously calibrated and validated SEBAL model with lysimeter data within the same agroclimatic zone were used in the study. Derived actual ET from lysimeter data validated SEBAL method was again validated using Penman-Montieth (PM) method for the study area located in the same agro-climatic zone. Based on the primary and secondary data analysis, it can be inferred that SEBAL ET is the best spatial ET estimation model for Hisar district or regions having similar agro-climatic conditions. Validation of SEBAL ET with ground-observed lysimeter data showed high coefficient of correlation (R2 = 0.91). Validation using the PM method also showed high coefficient of correlation (R2 = 0.835). Other statistical parameters (RMSE = 0.583, NRMSE = 0.236) also showed good agreement between actual SEBAL ETc and PM ETc (crop evapotranspiration). It was also found that any prior knowledge about the crops, their types and cropping seasons is not required for the estimation of actual ET by SEBAL model.Keywords
Energy Balance Algorithm, Evapotranspiration, Ground Truthing, Remote Sensing, Wheat.Full Text
References
- Gupta, S. P., India vision 2020. Online document of the Planning Commission, Government of India; http://planningcommission.nic.in/reports/genrep/pl_vsn2020.pdf
- Bastiaansen, W. G. M., SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. Hydrol., 2000, 229, 87–100.
- Roerink, G. J., Su, Z. and Menenti, M., S-SEBI: a simple remote sensing algorithm to estimate thesurface energy balance. Phys. Chem. Earth, Part B, 2000, 25,147–157.
- Kustas, W. P. and Norman, J. M., A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations. Water Resour. Res., 1997, 33, 1495–1508.
- Su, Z., The surface energy balance system (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci., 2002, 6, 85–100.
- Bastiaansen, W. G. M., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J. and Vander, W. T., Asurface energy balance algorithm for land (SEBAL): Part 2 Validation. J. Hydrol., 1998, 212–213, 198–212.
- Bala, A., Rawat, K. S., Misra, A. K. and Srivastava, A., Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India. Geocarto Int., 2015; doi:10.1080/10106049.2015.1076062.
- Allen, R. G., Pereira, L. S., Raes, D. and Smith, M., Crop ET-guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome, 1998.
- Peacock, C. E. and Hess, T. M., Estimating evapotranspiration from a reed bed using the Bowen ratio energy balance method. Hydrol. Process., 2004, 18, 247–260.
- Lockwood, J. G., Is potential evapotranspiration and its relationship with actual evapotranspiration sensitive to elevated atmospheric CO2 levels? Climatic Change, 1999, 41, 193–212.
- Yu, G. R., Wen, X. F., Sun, X. M., Tanner, B. D. and Lee, X. H., Overview of China flux and evaluation of its eddy covariance measurement. Agric. For. Meteorol., 2006, 137, 125–137.
- Karimi, P. and Bastiaanssen, W. G. M., Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data. Hydrol. Earth Syst. Sci. Discuss., 2014, 11, 1–51.
- Tasumi, M., Trezza, R., Allen, R. G. and Wright, J. L., Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid US. Irrig. Drain. Syst., 2005, 19, 355–376.
- Liou, Y. A. and Kar, S. K., Evapotranspiration estimation with remote sensing and various surface energy balance algorithms. a review. Energies, 2014, 7, 2821–2849.
- Saroj Sharma, M. P. and Prawasi, R., Geospatial approach cropping system analysis: a case study of Hisar district in Haryana. Int. J. Comput. Technol. Appl., 2014, 5, 457–461.
- Bastiaanssen, W. G. M. and Roebeling, R. A., Analysis of land surface exchange processes in two agricultural regions in Spain using Thematic Mapper Simulator data. In Joint IAHS/IAMAP Symposium, Yokohama, Japan, IAHS, 11–23 July 1993, vol. 212, pp. 407–416.
- Testing the Long Memory Feature in Indian Equity Market
Abstract Views :216 |
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Authors
Anju Bala
1,
Kapil Gupta
2
Affiliations
1 Department of Management, I.K.Gujral Punjab Technical University, Jalandhar, Punjab, IN
2 epartment of Management, I.K.Gujral Punjab Technical University, Jalandhar, Punjab, IN
1 Department of Management, I.K.Gujral Punjab Technical University, Jalandhar, Punjab, IN
2 epartment of Management, I.K.Gujral Punjab Technical University, Jalandhar, Punjab, IN
Source
Optimization: Journal of Research in Management, Vol 10, No 2 (2018), Pagination: 24-33Abstract
The Present paper examined the long memory behavior in Indian equity market. This paper uses the data from January 2000 to March 2018 of Sensex, Nifty-50 and VIX. By using the Rescaled range analysis as proposed by Lo (1951) ‘Hurst Exponent’, this indicates that there is significant long memory in Sensex and Nifty-50 returns series. However, volatility does not show any persistence but exhibit clustering. The study conclude that there is not persistence behavior with respect to long memory effect on Nifty-50 returns subject to occurrence of structural breaks(demonization).The study concludes with managerial relevance and issued for futures research. Findings would be beneficial for the investors, practitioners, academics and policy makers etc. To the best of our knowledge, there is dearth of literature on the subject in Indian equity market. Therefore the present study is an attempt to plug this gap.Keywords
Long Memory, Hurst Exponent, Volatility Clustering, Market Efficiency, Structural Breaks.References
- • Ajina, A., Lakhal, F., and Sougné, D. (2015). Institutional investors, information asymmetry and stock market liquidity in France. International Journal of Managerial Finance, 11(1), 44-59.
- • Akram, N. (2014). The Effect of Liquidity on Stock Returns: Evidence from Pakistan. IOSR Journal of Business and Management (IOSRJBM) 16 (2), PP 66-69.
- • Barkoulas, J. T., and Baum, C. F. (1996). Longterm dependence in stock returns. Economics Letters, 53(3), 253-259.
- • Bali, T. G., Peng, L., Shen, Y., and Tang, Y. (2014). Liquidity shocks and stock market reactions. Review of Financial Studies, 27(5), 1434-1485.
- • Balcilar, M., Ozdemir, Z. A., and Cakan, E. (2015). Structural Breaks, Long Memory, or Unit Roots in Stock Prices: Evidence from Emerging Markets. International Econometric Review (IER), 7(1), 13-33.
- • Barkoulas, J. T., Baum, C. F., and Travlos, N. (2000). Long memory in the Greek stock market. Applied Financial Economics, 10(2), 177-184.
- • Baillie, R. T., and Morana, C. (2009). Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics and Control, 33(8), 1577-1592.
- • Backus, D., and Zin, S. E. (1993). Long-memory inflation uncertainty: Evidence from the term structure of interest rates. Working paper No.133, National Bureau of Economic Research.
- • Baker, M., and Stein, J. C. (2004). Market liquidity as a sentiment indicator. Journal of Financial Markets, 7(3), 271-299.
- • Blume, M. E., and Keim, D. B. (2012). Institutional investors and stock market liquidity: trends and relationships. This paper has been downloaded from Social Science Research Network (SSRN) on May 19,2015.
- • Bhattacharya, S. N., and Bhattacharya, M. (2012). Long memory in stock returns: A study of emerging markets. Iranian Journal of Management Studies, 5(5), 67-88.
- • Badhani, K. N. (2012). Does Nifty have a Long Memory? Semi-parametric Estimation of Fractional Integration in Returns and Volatility. Decision (0304-0941), 39(3).
- • Badhani, K. N. (2008). Long memory in stock returns and volatility in India: A nonparametric analysis. ICFAI Journal of Applied Finance, 14(12), 34-53.
- • Baum, C. F., Barkoulas, J. T., and Caglayan, M. (1999). Long memory or structural breaks: can either explain nonstationary real exchange rates under the current float? Journal of International Financial Markets, Institutions and Money, 9(4), 359-376.
- • Barkoulas, J. T., and Baum, C. F. (1997). Long memory and forecasting in Euroyen deposit rates. Financial Engineering and the Japanese Markets, 4(3), 189-201.
- • Booth, G. G., and Tse, Y. (1995). Long memory in interest rate futures markets: A fractional cointegration analysis. Journal of Futures Markets, 15(5), 573-584.
- • Baillie, R. T. (1996). Long memory processes and fractional integration in econometrics. Journal of econometrics, 73(1), 5-59.
- • Chung, H., Lin, W. T., and Wu, S. (2000). An analysis of long memory in volatility for Asian stock markets. Review of pacific basin financial markets and policies,3(03), 309-330.
- • Chordia, T., Roll, R., and Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249268.
- • Chen, J. H., and Diaz, J. F. (2013). Long memory and shifts in the returns of green and non-green Exchange-Traded Funds (ETFs). International Journal of Humanities and Social Science Invention, 2(10), 29-32.
- • Cont, R. (2005). Long range dependence in financial markets. In Fractals in Engineering (pp. 159-179). Springer London.
- • Cajueiro, D. O., and Tabak, B. M. (2004). Evidence of long range dependence in Asian equity markets: the role of liquidity and market restrictions. Physica A: Statistical Mechanics and its Applications, 342(3), 656-664.
- • Christodoulou-Volos, C., and Siokis, F. M. (2006). Long range dependence in stock market returns. Applied Financial Economics, 16(18), 1331-1338.
- • Caporale, G. M., and Gil-Alana, L. A. (2008). Long Memory and Structural Breaks in the Spanish Stock Market Index. Open Operational Research Journal, 2, 13-17.
- • Cheung, Y. W., and Lai, K. S. (1995). A search for long memory in international stock market returns. Journal of International Money and Finance, 14(4), 597-615.
- • Chen, J. H., & Huang, Y. F. Long Memory and Structural Breaks in Modelling the Volatility Dynamics of VIX-ETFs.
- • Cajueiro, D. O., and Tabak, B. M. (2005). Possible causes of long-range dependence in the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 345(3), 635-645.
- • Cevik, P., and Emec, H. (2013). Long memory properties in return and volatility: An Application of the Impact of Arab Spring in Turkey Financial Market. Current Research Journal of Social Sciences, 5(2), 60-66.
- • Dicle, M. F., Beyhan, A., & Yao, L. J. (2010). Market efficiency and international diversification: Evidence from India. International Review of Economics & Finance, 19(2), 313-339.
- • Datar, M. K. (2000, December). Stock Market Liquidity: Measurement and Implications. In Fourth capital Market Conference, december.
- • Eleswarapu, V. R., and Krishnamurti, C. (1994). Liquidity, stock returns and ownership structure: An empirical study of the Bombay Stock Exchange.unpolished document GowCheng Huang, Kartono Liano, Ming-Shiun Pan,(May 2002),” Do Stock Splits Signal future Profitability.
- • Fabio Caccioli and Matteo Marsili (2010). Information Efficiency and Financial Stability. Economics: The Open-Access, Open-Assessment E-Journal, 4 (2010-20): 1
- • Figlewski, S. (1978). Market” efficiency” in a market with heterogeneous information. The Journal of Political Economy, 581-597.
- • Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work*. The journal of Finance, 25(2), 383-417
- • Fama, E. F. (1991). Efficient capital markets: II. The journal of finance, 46(5), 1575-1617.
- • Gayathri, M., Murugesan, S., & Gayathri, J. (2012). Persistence and long range dependence in Indian stock market returns. International Journal of management and business studies, 2(4), 72-77.
- • Granger, C. W., and Hyung, N. (2004). Occasional structural breaks and long memory with an application to the S and P 500 absolute stock returns. Journal of empirical finance, 11(3), 399-421.
- • Gurgul, H., and Wojtowicz, T. (2006). Long memory on the German stock exchange. Czech Journal of Economics and Finance (Finance a uver), 56(09-10), 447-468.
- • Greene, M. T., and Fielitz, B. D. (1977). Longterm dependence in common stock returns. Journal of Financial Economics, 4(3), 339349.
- • Ho, T. S., Schwartz, R. A., and Whitcomb, D. K. (1985). The trading decision and market clearing under transaction price uncertainty. Journal of Finance, 21-42.
- • Hassler, U., and Wolters, J. (1995). Long memory in inflation rates: International evidence. Journal of Business & Economic Statistics, 13(1), 37-45.
- • Hiremath, G. S., and Kamaiah, B. (2011). Testing Long Memory in Stock Returns of Emerging Markets: Some Further Evidence. Economics, Management, and Financial Markets, 6(3), 136-147.
- • Hiremath, G. S., and Kamaiah, B. (2010). Long memory in stock market volatility: Indian evidences. Artha Vijnana, 52(4), 332-345.
- • Heidarpoor, F., Rouhi, A., and Mashaei, K. F. (2012). Relationship between stock liquidity and stock price changes rate. African Journal of Business Management, 6(37), 10178-10184.
- • Huang, B. N., and Yang, C. W. (1999). An examination of long-term memory using the intraday stock returns. Technical Report 99– 03, Clarion University of Pennsylvania, Clarion.
- • Jennings, R. H., and Barry, C. B. (1983). Information dissemination and portfolio choice. Journal of Financial and Quantitative Analysis, 18(01), 1-19.
- • Joshipura, M. (2008). Price and Liquidity Effects of Stock Split: empirical evidence from Indian stock market. NSE Research initiative.
- • Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(01), 109-126.
- • Kumar, A. (2004). Long memory in stock trading volume: Evidence from Indian stock market. This paper has been downloaded from Social Science Research Network (SSRN) on May 19, 2015.
- • Kumar, D. (2014). Long-range dependence in Indian stock market: a study of Indian sectoral indices. International Journal of Emerging Markets, 9(4), 505-519.
- • Kasman, A., Kasman, S., and Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review, 10(2), 122-139.
- • Kasman, A., and Torun, E. (2012). Long memory in the Turkish stock market return and volatility. Central bank review, 7(2), 1327.
- • Kasman, S., Turgutlu, E., and Ayhan, A. D. (2009). Long memory in stock returns: evidence from the major emerging Central European stock markets. Applied Economics Letters, 16(17), 1763-1768.
- • Kang, S. H., and Yoon, S. M. (2007). Long memory properties in return and volatility: Evidence from the Korean stock market. Physica A: Statistical Mechanics and its Applications, 385(2), 591-600.
- • Lesmond, D. A. (2005). Liquidity of emerging markets. Journal of Financial Economics, 77(2), 411-452.
- • Lo, A (1991), Long Term Memory in Stock Market Prices. Econometrica,Vol.59, No.5, pp.1279-1313.
- • Limam, I. (2003). Is long memory a property of thin stock markets? International Evidence using Arab countries. Review of Middle East Economics and Finance, 1(3), 251-266.
- • Muranaga, J. (1999). Dynamics of market liquidity of Japanese stocks: An analysis of tick-by-tick data of the Tokyo Stock Exchange (Vol. 99). Institute for Monetary and Economic Studies, Bank of Japan.
- • Mukherjee, I., Sen, C., and Sarkar, A. (2011). Long memory in stock returns: Insights from the Indian market. The International Journal of Applied Economics and Finance, 5(1), 6274.
- • Nyborg, K. G., and Wang, Z. (2013). Stock liquidity and corporate cash holdings.Swiss Finance Institute Research Paper, (13-36).
- • Narasimhan, M. S., and Kalra, S. (2014). The Impact of Derivative Trading on the Liquidity of Stocks. VIKALPA, 39(3), 51.
- • Nath, G. C., & Dalvi, M. (2004). Day of the week effect and market efficiency-evidence from Indian equity market using high frequency data of national stock exchange. This paper has been downloaded from Social Science Research Network on June 2nd,2015.
- • Nath, G. C., and Reddy, Y. V. (2002, December). Long memory in rupee-dollar exchange rate–an empirical study. In Capital Market Conference.
- • Onour, I. A. (2010). North Africa stock markets: analysis of long memory and persistence of shocks. International Journal of Monetary Economics and Finance, 3(2), 101-111.
- • Prasanna, K., and Bansal, B. (2014). Foreign Institutional Investments and Liquidity of Stock Markets: Evidence from India. International Journal of Economics and Finance, 6(6), p103.
- • Pastor, L., and Stambaugh, R. F. (2001). Liquidity risk and expected stock returns (No. w8462). National Bureau of Economic Research.
- • Raj, Mahendra, and Damini Kumari. “Day-oftheweek and other market anomalies in the Indian stock market.” International Journal of Emerging Markets 1.3 (2006): 235-246.
- • Pati, P. C., & Rajib, P. (2011). Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures. Applied Economics Letters, 18(6), 567-574.
- • Souza, S. R., Tabak, B. M., and Cajueiro, D. O. (2008). Long-range dependence in exchange rates: the case of the European monetary system. International Journal of Theoretical and Applied Finance, 11(02), 199223.
- • Souza, S. R., Tabak, B. M., and Cajueiro, D. O. (2008). Long memory testing for Fed Funds Futures’ contracts. Chaos, Solitons & Fractals, 37(1), 180-186.
- • Suresha, B. (2014). Ownership Structure and Market Liquidity–Sectorial Evidence From India. Research Journal of Finance and Accounting, 5(19), 127-133.
- • Sadique, S., and Silvapulle, P. (2001). Long term memory in stock market returns: international evidence. International Journal of Finance & Economics,6(1), 59-67.
- • Soofi, A. S., Wang, S., and Zhang, Y. (2006). Testing for long memory in the Asian foreign exchange rates. Journal of Systems Science and Complexity,19(2), 182-190.
- • Sensoy, A. (2013). Time-varying long range dependence in market returns of FEAS members. Chaos, Solitons and Fractals, 53, 39-45.
- • Taqqu, M. S., Teverovsky, V., and Willinger, W. (1995). Estimators for long-range dependence: an empirical study. Fractals, 3(04), 785-798.
- • Turkyilmaz, S., and Balibey, M. (2014). Long Memory Behavior in the Returns of Pakistan Stock Market: ARFIMA-FIGARCH Models. International Journal of Economics and Financial Issues, 4(2), 400-41
- • Tayal, R., and Thomas, S. (2012). Measuring and explaining the asymmetry of liquidity. This paper has been downloaded from Social Science Research Network (SSRN) on May19, 2015.
- • Verma, A. (2008). Long memory of the Indian stock market. The IUP Journal of Financial Economics, 6(3), 74-83.
- • Willinger, W., Taqqu, M. S., and Teverovsky, V. (1999). Stock market prices and long-range dependence. Finance and stochastics, 3(1), 1-13.
- Long Term Memory:Evidence from Major Sectoral Indices of India
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Authors
Anju Bala
1,
Kapil Gupta
2
Affiliations
1 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, IN
2 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, IN
1 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, IN
2 Department of Management, I.K. Gujral Punjab Technical University, Jalandhar, Punjab, IN
Source
International Journal of Business Analytics and Intelligence, Vol 7, No 1 (2019), Pagination: 24-35Abstract
This paper tests the existence of long term memory with reference to structural changes/breaks in Indian Stock Market. Furthermore, the present paper applied Hurst Exponent in Rescaled Range Analysis as suggested by Hurst (1951) and Lo (1991) and structural breaks detected by using Multiple Break Test (Balcilar et al., 2015) by using daily returns of sectoral indices from January 2010 to May 2018. Empirical evidence shows the predictable structure in all sectoral indices (2010-2018) except Nifty Private Bank with H value 0.4972. The findings imply that existence of long memory would be useful for the investors, practitioners, academicians, and policymakers.Keywords
Emerging Market, Long Term Memory, Hurst Exponent, Structural Breaks, Market Efficiency.References
- Barkoulas, J. T., & Baum, C. F. (1996). Long-term dependence in stock returns. Economics Letters, 53(3), 253–259.
- Bali, T. G., Peng, L., Shen, Y., & Tang, Y. (2014). Liquidity shocks and stock market reactions. Review of Financial Studies, 27(5), 1434–1485.
- Balcilar, M., Ozdemir, Z. A., & Cakan, E. (2015). Structural breaks, long memory, or unit ischolar_mains in stock prices: Evidence from emerging markets. International Econometric Review (IER), 7(1), 13–33.
- Barkoulas, J. T., Baum, C. F., & Travlos, N. (2000). Long memory in the Greek stock market. Applied Financial Economics, 10(2), 177–184.
- Baillie, R. T., & Morana, C. (2009). Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach. Journal of Economic Dynamics and Control, 33(8), 1577–1592.
- Backus, D., & Zin, S. E. (1993). Long-memory inflation uncertainty: Evidence from the term structure of interest rates. Working paper No.133, National Bureau of Economic Research.
- Bhattacharya, S. N., & Bhattacharya, M. (2012). Long memory in stock returns: A study of emerging markets. Iranian Journal of Management Studies, 5(5), 67–88.
- Badhani, K. N. (2012). Does Nifty have a Long Memory? Semi-parametric Estimation of Fractional Integration in Returns and Volatility. Decision, 39(3), 86-100.
- Badhani, K. N. (2008). Long memory in stock returns and volatility in India: A nonparametric analysis. ICFAI Journal of Applied Finance, 14(12), 34–53.
- Baum, C. F., Barkoulas, J. T., & Caglayan, M. (1999). Long memory or structural breaks: can either explain nonstationary real exchange rates under the current float? Journal of International Financial Markets, Institutions and Money, 9(4), 359–376.
- Barkoulas, J. T., & Baum, C. F. (1997). Long memory and forecasting in Euroyen deposit rates. Financial Engineering and the Japanese Markets, 4(3), 189–201.
- Booth, G. G., & Tse, Y. (1995). Long memory in interest rate futures markets: A fractional cointegration analysis. Journal of Futures Markets, 15(5), 573–584.
- Baillie, R. T. (1996). Long memory processes and fractional integration in econometrics. Journal of econometrics, 73(1), 5–59.
- Bala, & Gupta (2018) Testing the long memory feature in Indian equity market. Journal of Research in Management,10(2), 24–33.
- Chung, H., Lin, W. T., & Wu, S. (2000). An analysis of long memory in volatility for Asian stock markets. Review of Pacific Basin Financial Markets and Policies, 3(3), 309–330.
- Chen, J. H., & Diaz, J. F. (2013). Long memory and shifts in the returns of green and non-green ExchangeTraded Funds (ETFs). International Journal of Humanities and Social Science Invention, 2(10), 29–32.
- Cont, R. (2005). Long range dependence in financial markets. In Fractals in Engineering (pp. 159–179). London: Springer.
- Christodoulou-Volos, C., and Siokis, F. M. (2006). Long range dependence in stock market returns. Applied Financial Economics, 16(18), 1331–1338.
- Caporale, G. M., & Gil-Alana, L. A. (2008). Long memory and structural breaks in the Spanish stock market index. Open Operational Research Journal, 2, 13–17.
- Cheung, Y. W., & Lai, K. S. (1995). A search for long memory in international stock market returns. Journal of International Money and Finance, 14(4), 597–615.
- Chen, J. H., & Huang, Y. F. (2014). Long memory and structural breaks in modelling the volatility dynamics of VIX-ETFs. International Journal of Business, Economics and Laws, 4(1), 54-63.
- Cajueiro, D. O., & Tabak, B. M. (2005). Possible causes of long-range dependence in the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 345(3), 635–645.
- Cevik, P., & Emec, H. (2013). Long memory properties in return and volatility: An application of the impact of arab spring in turkey financial market. Current Research Journal of Social Sciences, 5(2), 60–66.
- Dicle, M. F., Beyhan, A., & Yao, L. J. (2010). Market efficiency and international diversification: Evidence from India. International Review of Economics & Finance, 19(2), 313–339.
- Caccioli, F., & Marsili, M. (2010). Information efficiency and financial stability. Economics: The Open-Access, Open-Assessment E-Journal, 4(2010–20), 1-20.
- Figlewski, S. (1978). Market “efficiency” in a market with heterogeneous information. The Journal of Political Economy, 581–597.
- Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383–417.
- Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617.
- Gayathri, M., Murugesan, S., & Gayathri, J. (2012). Persistence and long range dependence in Indian stock market returns. International Journal of Management and Business Studies, 2(4), 72–77.
- Granger, C. W., & Hyung, N. (2004). Occasional structural breaks and long memory with an application to the S and P 500 absolute stock returns. Journal of Empirical Finance, 11(3), 399–421.
- Gurgul, H., & Wojtowicz, T. (2006). Long memory on the German stock exchange. Czech Journal of Economics and Finance (Finance a uver), 56(9–10), 447–468.
- Greene, M. T., & Fielitz, B. D. (1977). Long-term dependence in common stock returns. Journal of Financial Economics, 4(3), 339–349.
- Ho, T. S., Schwartz, R. A., & Whitcomb, D. K. (1985). The trading decision and market clearing under transaction price uncertainty. Journal of Finance, 40(1) 21–42.
- Hassler, U., & Wolters, J. (1995). Long memory in inflation rates: International evidence. Journal of Business & Economic Statistics, 13(1), 37–45.
- Hurst, H. E. (1951) The long-term storage capacity of reservoir. Transactions of the American Society of Civil Engineers 116, Paper 2447, Published in 1950 as Proceedings-Separate No. 11.
- Hiremath, G. S., & Kamaiah, B. (2011). Testing long memory in stock returns of emerging markets: Some further evidence. Economics, Management, and Financial Markets, 6(3), 136–147.
- Hiremath, G. S., & Kamaiah, B. (2010). Long memory in stock market volatility: Indian evidences. Artha Vijnana, 52(4), 332–345.
- Heidarpoor, F., Rouhi, A., & Mashaei, K. F. (2012). Relationship between stock liquidity and stock price changes rate. African Journal of Business Management, 6(37), 10178–10184.
- Huang, B. N., & Yang, C. W. (1999). An examination of long-term memory using the intraday stock returns. Technical Report 99–03. Clarion University of Pennsylvania, Clarion.
- Jennings, R. H., & Barry, C. B. (1983). Information dissemination and portfolio choice. Journal of Financial and Quantitative Analysis, 18(1), 1–19.
- Joshipura, M. (2008). Price and liquidity effects of stock split: Empirical evidence from Indian stock market. NSE Research initiative.
- Karpoff, J. M. (1987). The relation between price changes and trading volume: A survey. Journal of Financial and quantitative Analysis, 22(1), 109–126.
- Kumar, A. (2004). Long memory in stock trading volume: Evidence from Indian stock market. This paper has been downloaded from Social Science Research Network (SSRN) on May 19, 2015.
- Kumar, D. (2014). Long-range dependence in Indian stock market: A study of Indian sectoral indices. International Journal of Emerging Markets, 9(4), 505–519.
- Kasman, A., Kasman, S., & Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review, 10(2), 122–139.
- Kasman, A., & Torun, E. (2012). Long memory in the Turkish stock market return and volatility. Central Bank Review, 7(2), 13–27.
- Kasman, S., Turgutlu, E., & Ayhan, A. D. (2009). Long memory in stock returns: Evidence from the major emerging Central European stock markets. Applied Economics Letters, 16(17), 1763–1768.
- Kang, S. H., & Yoon, S. M. (2007). Long memory properties in return and volatility: Evidence from the Korean stock market. Physica A: Statistical Mechanics and its Applications, 385(2), 591–600.
- Lesmond, D. A. (2005). Liquidity of emerging markets. Journal of Financial Economics, 77(2), 411–452.
- Lo, A. (1991). Long term memory in stock market prices. Econometrica, 59(5), 1279–1313.
- Limam, I. (2003). Is long memory a property of thin stock markets? International Evidence using Arab countries. Review of Middle East Economics and Finance, 1(3), 251–266.
- Mukherjee, I., Sen, C., & Sarkar, A. (2011). Long memory in stock returns: Insights from the Indian market. The International Journal of Applied Economics and Finance, 5(1), 62–74.
- Nath, G. C., & Dalvi, M. (2004). Day of the week effect and market efficiency-evidence from Indian equity market using high frequency data of national stock exchange. This paper has been Downloaded from Social Science Research Network on June 2nd, 2015.
- Nath, G. C., & Reddy, Y. V. (2002, December). Long memory in rupee-dollar exchange rate: An empirical study. In Capital Market Conference.
- Onour, I. A. (2010). North Africa stock markets: Analysis of long memory and persistence of shocks. International Journal of Monetary Economics and Finance, 3(2), 101–111.
- Prasanna, K., & Bansal, B. (2014). Foreign institutional investments and liquidity of stock markets: Evidence from India. International Journal of Economics and Finance, 6(6), 103–118.
- Raj, M., & Kumari, D. (2006). Day-of-the-week and other market anomalies in the Indian stock market. International Journal of Emerging Markets, 1(3), 235–246.
- Pati, P. C., & Rajib, P. (2011). Intraday return dynamics and volatility spillovers between NSE S&P CNX Nifty stock index and stock index futures. Applied Economics Letters, 18(6), 567–574.
- Souza, S. R., Tabak, B. M., & Cajueiro, D. O. (2008). Long-range dependence in exchange rates: the case of the European monetary system. International Journal of Theoretical and Applied Finance, 11(2), 199–223.
- Souza, S. R., Tabak, B. M., & Cajueiro, D. O. (2008). Long memory testing for Fed Funds Futures’ contracts. Chaos, Solitons & Fractals, 37(1), 180–186.
- Suresha, B. (2014). Ownership structure and market Liquidity-Sectorial Evidence from India. Research Journal of Finance and Accounting, 5(19), 127–133.
- Sadique, S., & Silvapulle, P. (2001). Long-term memory in stock market returns: International evidence. International Journal of Finance & Economics, 6(1), 59–67.
- Soofi, A. S., Wang, S., and Zhang, Y. (2006). Testing for long memory in the Asian foreign exchange rates. Journal of Systems Science and Complexity, 19(2), 182–190.
- Sensoy, A. (2013). Time-varying long range dependence in market returns of FEAS members. Chaos, Solitons and Fractals, 53, 39–45.
- Taqqu, M. S., Teverovsky, V., & Willinger, W. (1995). Estimators for long-range dependence: An empirical study. Fractals, 3(4), 785–798.
- Turkyilmaz, S., & Balibey, M. (2014). Long memory behavior in the returns of Pakistan stock market: ARFIMA-FIGARCH models. International Journal of Economics and Financial Issues, 4(2), 400–441.
- Tayal, R., & Thomas, S. (2012). Measuring and explaining the asymmetry of liquidity. This paper has been downloaded from Social Science Research Network (SSRN) on May19, 2015.
- Verma, A. (2008). Long memory of the Indian stock market. The IUP Journal of Financial Economics, 6(3), 74–83.
- Willinger, W., Taqqu, M. S., & Teverovsky, V. (1999). Stock market prices and long-range dependence. Finance and stochastics, 3(1), 1–13.