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Haridass, M
- A Stochastic Model Approach to Study the Demeanor of Financial Markets and Impact of COVID-19 Pandemic on Spot and Forward Rates
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Authors
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1 Professor, Department of Mathematics, PSG College of Technology, Coimbatore, Tamil Nadu, India., IN
1 Professor, Department of Mathematics, PSG College of Technology, Coimbatore, Tamil Nadu, India., IN
Source
International Journal of Financial Management, Vol 11, No 1 (2021), Pagination: 41-51Abstract
This study explores the possibility of applying Discrete Time Markov Chain to predict the behaviour of financial markets, to obtain their steady state probabilities, and to apply Markov chains to predict the business cycle. The basic terminologies of Markov chain, as well as a brief introduction about mathematical finance are presented. The classifications of future markets and various types of business risks have also been analysed. The spot and forward rates were observed through a real data considered from the State Bank of India, before and after the impact of the novel Corona Virus (COVID-19), and analysed. Markov models will provide the best solution to deal the current situation. By using Markov models, one can approximately predict the future behaviour of the economic situations of financial markets. This study implies that every investor should make careful examination before investing in the financial market to minimise the risk, considering the impact of the contagious disease COVID-19.Keywords
Markov Chain, Steady State, Financial Markets, Business Cycle, Spot Rates, Forward Rates, COVID-19
References
- Baruah, M., & Parikh, A. K. (2018). Impact of risk tolerance and demographic factors on financial investment decision. International Journal of Financial Management, 8(1), 36-48.
- Boru, T., & Kuhil, A. M. (2018). Empirical evidences on structure-conduct-performance relationship in banking sector: A literature review. International Journal of Financial Management, 8(2), 11-25.
- Cui, Z., Lars Kirkby, J., & Nguyen, D. (2018). A general framework time-changed Markov processes and applications. European Journal of Operational Research, 273(2), 785-800. doi.org/10.1016/j.
- ejor.2018.08.033
- D’Amico, G., & Petroni, F. (2014). Multivariate highfrequency financial data via semi-Markov processes. Markov Processes and Related Fields, 20(3), 415-434.
- Dacco, R., & Satchell, S. (2001). Forward and spot exchange rates in a bivariate TAR framework. The European Journal of Finance, 7(2), 131-143.
- Geng, X., & Li, L. (2009). Markov process functionals in finance and insurance. Applied Mathematics - A Journal of Chinese Universities, 24(1), 21-26. doi.org/10.1007/s11766-009-1913-x
- Ghahramani, S. (2005). Fundamentals of probability with stochastic processes (3rd ed.). New Jersey: Pearson Prentice Hall.
- Grimshaw, S. D., & Alexander, W. P. (2011). Markov chain models for delinquency: Transition matrix estimation and forecasting. Applied Stochastic Models in Business and Industry, 27(3), 267-279.
- doi.org/10.1002/asmb.827
- Hagan, P. S., & Woodward, D. E. (1999). Markov interest rate models. Applied Mathematical Finance, 6(4), 233-260. doi.org/10.1080/13504869950079275
- Haridass, M., & Harshini, H. (2020). Analysis of annuity and impact of COVID 19 on mortgage in mathematical finance difference equations approach.International Journal of Financial Management, 10(2&3), 1-13.
- Hoek, J., & Elliott, R. J. (2012). Asset pricing using finite state Markov chain stochastic discount functions, Journal of Stochastic Analysis and Applications, 30(5), 865-894. oi.org/10.1080/07362994.2012.704852
- Kaur, J. (2019). Financial distress and bank performance: A study of select Indian banks. International Journal of Financial Management, 9(3), 26-35.
- Kevin, J. H. (2015). Introduction to financial mathematics.New York: Chapman and Hall/CRC Press.
- Kijima, M. (2013). Stochastic processes with applications to finance (2nd ed.). New York: Chapman and Hall/ CRC Press. doi.org/10.1201/b14785
- Ross, S. (2019). Introduction to probability models (12th ed.). Academic Press.
- Trivedi, K. S., Vaidyanathan, K., & Selvamuthu, D. (2015). Markov chain models and applications. In M. S. Obaidat, F. Zarai, and P. Nicopolitidis (Ed.).Modeling and Simulation of Computer Networks and Systems Methodologies and Applications (pp. 393-421). Elsevier, Inc. Morgan Kaufmann.
- Verlekar, R. P., & Kama, M. (2019). An application and comparison of bankruptcy models in the Indian banking sector. International Journal of Financial Management, 9(4), 42-53.
- Zafar, S. M. T., Maqbool, A., & Fortyal, A. (2011). A study of impact of leverage on the profitability of Indian banking industry. International Journal of Financial Management, 1(2), 85-99.
- Zhang, G., & Li, L. (2018). Analysis of Markov chain approximation for option pricing and hedging: Grid design and convergence behaviour. Operations Research, 67(2), 407-427. doi.org/10.1287/
- opre.2018.1791