A Model for Calculating the Effectiveness of Writing and Hedging SBI Derivatives
DOI:
https://doi.org/10.54392/irjmt2333Keywords:
ANOVA, Derivatives, Option-chain, Regression, Residual normalityAbstract
Options in the stock market are a form of risk management that can help protect investors from various potential liabilities. Increased demand for derivatives is reflected in higher trading volumes every day. Over time, it has been easier for regular investors to get their hands on derivatives. The major Indian exchanges trade a wide range of financial goods, including stock derivatives. This article explains how to trade F&O on the National Stock Exchange (NSE) in India. To write options, the NSE typically employs call-and-put options. It may be able to design ways to achieve this goal by studying the State Bank of India (SBI) options chain for the first quarter of fiscal years 22 and 23. Based on the current stock price, the suggested computational approach writes call (CE) and put (PE) options for the upcoming month's settlement date. CE and PE were written at prices twenty rupees higher and lower than the stock options strike price, respectively. Furthermore, the pricing for both products has been reduced to zero rupees. According to our research, selling options to firms with minimal volatility is a good idea.
References
P. Mukherjee, A. Mondal, S. Dey, A. Layek, S. Neogi, M. Gope, S. Gupta, Monitoring, Recognition and Attendance Automation in Online Class: Combination of Image Processing, Cryptography in IoT Security. In: D. Giri, J.K. Mandal, K. Sakurai, D. De, (eds) Proceedings of International Conference on Network Security and Blockchain Technology. ICNSBT 2021. Lecture Notes in Networks and Systems, Springer, Singapore, 481 (2022) 18–27. https://doi.org/10.1007/978-981-19-3182-6_2
S. Gupta, Chan-vese segmentation of SEM ferrite-pearlite microstructure and prediction of grain boundary, International Journal of Innovative Technology and Exploring Engineering, 8(10) (2019) 1495–1498. http://doi.org/10.35940/ijitee.A1024.0881019
B. Mondal, S. Gupta, Execution Survey and State of the Art of Different ML-Based Ensemble Classifiers Approach Contextual Analysis of Spam Remark Location. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021, Springer Nature, Singapore, (2022) 311-323. https://doi.org/10.1007/978-981-19-1142-2_24
H. Li, H. An, J. Huang, X. Huang, S. Mou, Y. Shi, The evolutionary stability of shareholders’ co-holding behavior for China’s listed energy companies based on associated maximal connected sub-graphs of derivative holding-based networks, Applied energy, 162 (2016) 1601-1607. https://doi.org/10.1016/j.apenergy.2015.04.049
M. Arfaoui, A. Ben Rejeb, Oil, gold, US dollar and stock market interdependencies: a global analytical insight. European Journal of Management and Business Economics, 26(3) (2017) 278-293. https://doi.org/10.1108/EJMBE-10-2017-016
A.K. Singh, J. Patra, M. Chakraborty, S. Gupta, Prediction of Indian government stakeholder oil stock prices using hyper parameterized LSTM models, 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India, (2022) 1-6. https://doi.org/10.1109/ICICCSP53532.2022.9862425
R. Soujanya, P. Akshith Goud, A. Bhandwalkar, and G. Anil Kumar, Evaluating future stock value asset using machine learning, Materials Today: Proceedings, 33(7) (2020) 4808–4813. https://doi.org/10.1016/j.matpr.2020.08.385
A.K. Pradhan, I. Mittal, A.K. Tiwari, Optimizing the market-risk of major cryptocurrencies using CVaR measure and copula simulation, Macroeconomics and Finance in Emerging Market Economies, 14(3) (2021) 291–307. https://doi.org/10.1080/17520843.2021.1909828
S. Gupta, A. Banerjee, J. Sarkar, M. Kundu, S.K. Sinha, N.R. Bandyopadhyay, S. Ganguly, Modelling the steel microstructure knowledge for in-silico recognition of phases using machine learning, Materials Chemistry and Physics, 252 (2020) 123286. https://doi.org/10.1016/j.matchemphys.2020.123286
D.S. Gupta, Vaishali, R. Tahlan, N.S. Joshi, R. Agarwal, Stock Market (NIFTY) Forecasting using Machine Learning Analysis on Option Chain, International Journal of Recent Technology and Engineering, 9(5) (2021) 80–83. http://www.doi.org/10.35940/ijrte.E5155.019521
I. Sengupta, C. Koner, N.K. Bhattacherjee, S. Gupta, Automated Student Merit Prediction using Machine Learning, 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, (2022) 556-560 https://doi.org/10.1109/AIC55036.2022.9848976
P. Palanivel, P. Koshy Mathews, Prediction and optimization of process parameter of friction stir welded AA5083? H111 aluminum alloy using response surface methodology, Journal of Central South University, 19(1−8) (2012) 1–8. https://doi.org/10.1007/s11771-012-0964-y
R. Birău, J. Trivedi, Estimating Long-term Volatility on National Stock Exchange of India, Procedia Economics and Finance, 32(15) (2015) 574–579. https://doi.org/10.1016/S2212-5671(15)01434-3
C. Basarir, M.F. Bayramoglu, Global macroeconomic determinants of the domestic commodity derivatives, Contributions to Economics, (2018) 331–349. https://doi.org/10.1007/978-3-319-78494-6_16
B. Mondal, D. Chakraborty, N. K. Bhattacherjee, P. Mukherjee, S. Neogi, S. Gupta, Review for Meta-Heuristic Optimization Propels Machine Learning Computations Execution on Spam Comment Area Under Digital Security Aegis Region, in Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems, P. Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Ed. Springer Nature, (2022) 343–361. https://doi.org/10.1007/978-3-030-99079-4_13
S. Gupta, J. Sarkar, M. Kundu, N.R. Bandyopadhyay, S. Ganguly, Automatic recognition of SEM microstructure and phases of steel using LBP and random decision forest operator, Measurement, 151(2020) 107224. https://doi.org/10.1016/j.measurement.2019.107224
S. Aboura, J. Chevallier, R. Jammazi, A.K. Tiwari, The place of gold in the cross-market dependencies, Studies in Nonlinear Dynamics & Econometrics, 20(5) (2016) 567–586. https://doi.org/10.1515/snde-2015-0017
A. Ahmad, M. Sulaiman, A. J. Aljohani, A. Alhindi, H. Alrabaiah, Design of an efficient algorithm for solution of Bratu differential equations, Ain Shams Engineering Journal, 12(2) (2021) 2211–2225. https://doi.org/10.1016/j.asej.2020.11.007
M.M. Akhtar, A.S. Zamani, S. Khan, A.S.A. Shatat, S. Dilshad, F. Samdani, Stock market prediction based on statistical data using machine learning algorithms, Journal of King Saud University – Science, 34(4) (2022) 101940. https://doi.org/10.1016/j.jksus.2022.101940
J.F. Garcia-Quijano, G. Deckmyn, E. Moons, S. Proost, R. Ceulemans, B. Muys, An integrated decision support framework for the prediction and evaluation of efficiency, environmental impact and total social cost of domestic and international forestry projects for greenhouse gas mitigation: Description and case studies, Forest Ecology and Management, 207(1-2) (2005) 245–262. https://doi.org/10.1016/j.foreco.2004.10.030
S.J. Abul, Factors influencing Individual Investor Behaviour: Evidence from the Kuwait Stock Exchange, Asian Social Science, 15(3) (2019) 27-39. https://doi.org/10.5539/ass.v15n3p27
D. Girardi, S. Bowles, Institution shocks and economic outcomes: Allende’s election, Pinochet’s coup and the Santiago stock market, Journal of Development Economics, 134 (2018) 16–27. https://doi.org/10.1016/j.jdeveco.2018.04.005
T. Foucault, S. Moinas, E. Theissen, Does anonymity matter in electronic limit order markets?, Review of Financial Studies, 20(5) (2007) 1707–1747. https://doi.org/10.1093/rfs/hhm027
C. Beaman, A. Barkworth, T.D. Akande, S. Hakak, M.K. Khan, Ransomware: Recent advances, analysis, challenges and future research directions, Computers & Security, 111 (2021) 102490. https://doi.org/10.1016/j.cose.2021.102490
M. Ahmadi, N. Bashiri Behmiri, M. Manera, How is volatility in commodity markets linked to oil price shocks?, Energy Economics, 59 (2016) 11–23. https://doi.org/10.1016/j.eneco.2016.07.006
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