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Indian Journal of Finance

ISSN: 0973-8711 Frequency: Monthly Peer Review: Double-blind Published since: 2007 Language: English
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Original Article

Open Access Original Article

The Ripple Effect : Influence of Exchange Rate Volatility on Indian Sectoral Indices

Amiya Kumar Mohapatra1,  Debasis Mohanty2,  Varda Sardana3,  Amit Shrivastava4

1 Professor and Dean (Research) Jaipuria Institute of Management, Dakachaya, Indore – 453 771, Madhya Pradesh

2 Assistant Professor , School of Commerce Management and Research, ITM University, Raipur - 493 661, Chhattisgarh

3 Assistant Professor, Jaipuria Institute of Management, A - 32A, Opposite IBM, Sector 62, Noida - 201 309, Uttar Pradesh

4 Professor, Jaipuria Institute of Management, Dakachaya, Indore - 453 771, Madhya Pradesh

Volume 18
Issue 2
Pages 8–24
Year 2024
Received: July 14, 2023 Accepted: Jan. 5, 2024 Published: Feb. 1, 2024
Abstract

Purpose: The study aimed to analyze the spillover effect of foreign exchange rate volatility on the sectoral indices of India. The paper included five exchange rates (USD-INR, EURO-INR, GBP-INR, CNY-INR, and JPY-INR) and seven (automobile, banking, energy, FMCG, infrastructure, information & technology, and pharmaceutical) sectoral indices in this study.

Methodology: The study used the generalized autoregressive conditional heteroscedasticity (GARCH) (1,1) model for modeling volatility. T-GARCH model was used to estimate the leverage effect; and DCC-GARCH was used for analyzing the spillover effect of exchange rates on select sectoral indices.

Findings: The study found that leverage effect existed in all the series as the sum of α and λ of T-GARCH is less than 1, and the model was found acceptable. In the long-run, there was a spillover effect from all the exchange rates to all the sectoral indices. A short-run USD-INR spillover was observed on five sector indices, excluding banks and infrastructure. For every sectoral index, the DCC α of EURO-INR was determined to be negligible. Therefore, there was no transmission of short-term volatility from the EURO-INR to sectoral indices. Indexes of the FMCG, infrastructure, and IT sectors were not affected by the short-term volatility of the GBP-INR. DCC α and DCC β of CNY-INR were found to be significant for all the indices, which revealed transmission of information in the short-run as well as in the long-run. The three indices for which DCC α of JPY-INR was found insignificant are the bank, FMCG, and pharma sectors.

Implications: The results of this study shall aid policymakers and regulators in formulating investor-friendly rules and regulations to maintain a steady stock market. Prospective stakeholders shall also benefit from this research by using it to make reliable and well-informed investment decisions and portfolios. This study could be used as a reference document in other research and academic discussions as well.

Originality: Foreign exchange rates and sectoral indices, the focus of this study, have received relatively little attention in the literature despite the fact that research on stock markets has gained a lot of attention recently. This research will be a trailblazer in examining how fluctuations in foreign exchange rates impact India’s sectoral indexes.

Keywords Foreign Exchange Rate Volatility Sectoral Indices GARCH India. JEL Classifications Code
How to Cite

Amiya Kumar Mohapatra, Debasis Mohanty, Varda Sardana, Amit Shrivastava (2024). The Ripple Effect : Influence of Exchange Rate Volatility on Indian Sectoral Indices. Indian Journal of Finance, 18(2), 8–24. https://doi.org/10.17010/ijf/2024/v18i2/173519

References
  1. Aggarwal, S., & Khurana, S. (2018). Empirical examination of stock market volatility: An international comparison. Indian Journal of Finance, 12(1), 47–61. https://doi.org/10.17010/ijf/2018/v12i1/120741
  2. Aksöz Yilmaz, H., & Güzel, F. (2021). How do the exchange rates affect the sector indices? A dynamic panel data analysis for Borsa Istanbul. İstanbul İktisat Dergisi, 71(2), 414–434. https://doi.org/10.26650/ISTJECON2021-970320
  3. Black, F. (1976). The pricing of commodity contracts. Journal of Financial Economics, 3(1–2), 167–179. https://doi.org/10.1016/0304-405X(76)90024-6
  4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
  5. Brooks, C., & Rew, A. G. (2002). Testing for a unit root in a process exhibiting a structural break in the presence of GARCH errors. Computational Economics, 20(3), 157–176. https://doi.org/10.1023/A:1020945428824
  6. Chaudhary, R., Bakhshi, P., & Gupta, H. (2020). Volatility in international stock markets: An empirical study during COVID-19. Journal of Risk and Financial Management, 13(9), 208. https://doi.org/10.3390/jrfm13090208
  7. Chauque, D. F., & Rayappan, P. A. (2018). The impact of macroeconomic variables on stock market performance: A case of Malaysia. Edelweiss Applied Science and Technology, 2(1), 100–104. https://doi.org/10.33805/2576.8484.122
  8. Das, J. P., & Kumar, S. (2023). Impact of corporate hedging practices on firm's value: An empirical evidence from Indian MNCs. Risk Management, 25(2), Article 10. https://doi.org/10.1057/s41283-023-00115-3
  9. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348
  10. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
  11. Engle, R. F., Lilien, D. M., & Robins, R. P. (1987). Estimating time varying risk premia in the term structure: The Arch-M model. Econometrica, 55(2), 391–407. https://doi.org/10.2307/1913242
  12. Engle, R. F., Focardi, S. M., & Fabozzi, F. J. (2008). ARCH/GARCH models in applied financial econometrics. In, F. J. Fabozzi (ed.). Handbook of finance (pp. 689–701). Wiley. https://onlinelibrary.wiley.com/doi/10.1002/9780470404324.hof003060/abstract
  13. He, P., Sun, Y., Zhang, Y., & Li, T. (2020). COVID–19's impact on stock prices across different sectors—An event study based on the Chinese stock market. Emerging Markets Finance and Trade, 56(10), 2198–2212. https://doi.org/10.1080/1540496X.2020.1785865
  14. Kapoor, P., & Goel, S. (1990). Is mutual fund a prefered avenue for investments. ELK Asia Pacific Journals–Special Issue. https://dokumen.tips/documents/elk-asia-pacific-journals-special-issue-isbn-978-93-of-respondents-towards.html?page=1
  15. Khanna, S., & Kumar, A. (2020). GARCH and TGARCH approach to information linkages. Indian Journal of Finance, 14(8-9), 35–51. https://doi.org/10.17010/ijf/2020/v14i8-9/154947
  16. Matta, R., Kochhar, K., Mohapatra, A. K., & Mohanty, D. (2022). Board characteristics and risk disclosure quality by integrated reporters: Evidence from Indian Banks. Prabandhan: Indian Journal of Management, 15(5), 27–42. https://doi.org/10.17010/pijom/2022/v15i5/169579
  17. Mohanty, D., Mohapatra, A. K., Tripathy, S., & Matta, R. (2023). Nexus between foreign exchange rate and stock market: Evidence from India. Investment Management and Financial Innovations, 20(3), 79–90. https://doi.org/10.21511/IMFI.20(3).2023.07
  18. Oskooe, S. A. (2010). Emerging stock market performance and economic growth. American Journal of Applied Sciences, 7(2), 265–269. https://doi.org/10.3844/ajassp.2010.265.269
  19. Pandey, A., & Mohapatra, A. K. (2017). Validation of fama French model in Indian capital market. International Journal of Economic Research, 14(2), 255–272. https://serialsjournals.com/abstract/71459_21.pdf
  20. Pandey, A., Sehgal, S., Mohapatra, A. K., & Samanta, P. K. (2021). Equity market anomalies in major European economies. Investment Management and Financial Innovations, 18(2), 245–260. https://doi.org/10.21511/imfi.18(2).2021.20
  21. Perumandla, S., & Kurisetti, P. (2018). Time-varying correlations, causality, and volatility linkages of Indian commodity and equity markets: Evidence from DCC - GARCH. Indian Journal of Finance, 12(9), 21–40. https://doi.org/10.17010/ijf/2018/v12i9/131558
  22. Rai, K., & Garg, B. (2022). Dynamic correlations and volatility spillovers between stock price and exchange rate in BRIICS economies: Evidence from the COVID-19 outbreak period. Applied Economics Letters, 29(8), 738–745. https://doi.org/10.1080/13504851.2021.1884835
  23. Rastogi, S. (2014). The financial crisis of 2008 and stock market volatility - Analysis and impact on emerging economies pre and post crisis. Afro-Asian Journal of Finance and Accounting, 4(4), 443–459. https://doi.org/10.1504/AAJFA.2014.067017
  24. Reddy, R. V., Nayak, R., Nagendra, S., & Ashwith. (2019). Impact of macro - economic factors on Indian stock market- A research of BSE sectoral indices. International Journal of Recent Technology and Engineering, 8(2S7), 597–602. https://doi.org/10.35940/ijrte.B1110.0782S719
  25. Rezitis, A. N., & Stavropoulos, K. S. (2011). Price volatility and rational expectations in a sectoral framework commodity model: A multivariate GARCH approach. Agricultural Economics, 42(3), 419–435. https://doi.org/10.1111/j.1574-0862.2010.00521.x
  26. Sikarwar, E., & Gupta, R. (2019). Economic exposure to exchange rate risk and financial hedging: Influence of ownership as a governance mechanism. Journal of Economic Studies, 46(4), 965–984. https://doi.org/10.1108/JES-10-2017-0286
  27. Singh, A. K., Shrivastav, R. K., & Mohapatra, A. K. (2022). Dynamic linkages and integration among five emerging BRICS markets: Pre- and Post-BRICS period analysis. Annals of Financial Economics, 17(3), Article 2250018. https://doi.org/10.1142/S201049522250018X
  28. Singhal, S., Choudhary, S., & Biswal, P. C. (2019). Return and volatility linkages among international crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico. Resources Policy, 60, 255–261. https://doi.org/10.1016/j.resourpol.2019.01.004
  29. Sugiharti, L., Esquivias, M. A., & Setyorani, B. (2020). The impact of exchange rate volatility on Indonesia's top exports to the five main export markets. Heliyon, 6(1), E03141. https://doi.org/10.1016/j.heliyon.2019.e03141
  30. Tang, B. (2015). Exchange rate exposure of Chinese firms at the industry and firm level. Review of Development Economics, 19(3), 592–607. https://doi.org/10.1111/rode.12162
  31. Vikram, I., Hotwan, A., & Mohanty, D. (2022). Comparison of international stock market Volatility: An empirical analysis during economic crisis 2008 and COVID-19. ECS Transactions, 107(1), 18593. https://doi.org/10.1149/10701.18593ecst
  32. Yadav, M. P., Sharma, S., & Bhardwaj, I. (2023). Volatility spillover between Chinese stock market and selected emerging economies: A dynamic conditional correlation and portfolio optimization perspective. Asia-Pacific Financial Markets, 30(2), 427–444. https://doi.org/10.1007/s10690-022-09381-9
  33. Yadav, S. (2016). Integration of exchange rate and stock market: Evidence from the Indian stock market. Indian Journal of Finance, 10(10), 56–63. https://doi.org/10.17010/ijf/2016/v10i10/103015
  34. Zarei, A., Ariff, M., & Bhatti, M. I. (2019). The impact of exchange rates on stock market returns: New evidence from seven free-floating currencies. The European Journal of Finance, 25(14), 1277–1288. https://doi.org/10.1080/1351847X.2019.1589550
  35. Zheng, J., Li, Z., Ghardallou, W., & Wei, X. (2023). Natural resources and economic performance: Understanding the volatilities caused by financial, political and economic risk in the context of China. Resources Policy, 84, 103697. https://doi.org/10.1016/j.resourpol.2023.103697
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