Date of submission: March 20, 2023; date of acceptance: August 17, 2023. * Contact information (corresponding Author): adishbansal8@gmail.com, Depart- ment of Management and Hospitality, I.K. Gujral Punjab Technical University, Main Campus, Kapurthala, Pin Code-144603, India, phone: +91 904 156 1827; ORCID ID: https://orcid.org/0000-0002-8737-3162. ** Contact information: kapilfutures@gmail.com, Department of Management and Hospitality, I.K. Gujral Punjab Technical University, Main Campus, Kapurthala, Pin Code-144603, India, phone: +91 47 809 8074; ORCID ID: https://orcid.org/0000-0003- 3817-1772. Copernican Journal of Finance & Accounting e-ISSN 2300-3065 p-ISSN 2300-12402023, volume 12, issue 3 Kumar, A., & Gupta, K. (2023). Financialization and Dynamics of Currency Futures Market during COVID-19: Evidence from India. Copernican Journal of Finance & Accounting, 12(3), 43–64. http://dx.doi.org/10.12775/CJFA.2023.015 adisH Kumar* I.K. Gujral Punjab Technical University Kapil gupta** I.K. Gujral Punjab Technical University financialization and dynamics of currency futures marKet during covid-19: evidence from india Keywords: financialization, exchange rates, style investment, financial crisis, COV ID- 19, structural breaks. J E L Classification: C1, C5, G13, G14, G17. Abstract: This study examines inter-relationship and impact of COVID-19 on Indian currency and equity futures markets during the period of financial crisis. In such peri- od, investors look for alternative asset classes to hedge against risk as observed during Global Financial Crisis. This study examines whether same phenomenon was observed after COVID-19 in India considering currency futures as an alternate asset class. For this purpose daily exchange rate of Indian Rupee with British Pound Sterling, Japanese Yen, Euro and United States Dollar and for equity futures, near-month NIFTY 50 futures Adish Kumar, Kapil Gupta 4444 contracts are used. After examining stationarity of data, Co-integration test, Grang- er causality and Bi-variate correlation is applied. ARCH and DCC-GARCH model is em- ployed to allow for heteroscedasticity and time variation in correlation. It is observed that YEN, JPY and USD display significantly negative correlation with Nifty futures. Currency futures is causing Nifty futures during COVID-19 period and leads Nifty fu- tures by one day. However, it is other way around during pre-COVID-19 period. Long- run co-integration is not evident. ARCH effect is present in both time series and except for insignificant short-run shock persistence during COVID-19 period, there exists time varying correlation between currency returns and Nifty.  Introduction Introduction The present study examines connectedness and inter-relationship between currency and equity futures markets in India. In recent period, increased inter- est in portfolio diversification across asset classes has stimulated investors to look for assets that have potential to provide protection against shocks due to market stress (Zghal & Ghorbel, 2022). Therefore, cross-asset integration has become significant concern due to increased association between currency and equity futures markets (Aravind, 2017; Mittal, Sehgal & Mittal, 2019). According to portfolio rebalancing approach, an increase in the stock price attracts domestic investor to withdraw funds from foreign exchange and invest it in stock market, which leads to depreciation of exchange rate. However, when stock prices decline, stock market exhibits selling pressure as investors may try to avoid further losses, which in turn, leads to withdrawal of funds from stock market and investment in foreign currency. As a result, exchange rate may rise (Aravind, 2017). Therefore, negative shock in one market can prove to be a positive shock in other market as any change in price of asset in one mar- ket can lead investors to change their position in other market so that hedging ratio remains same (Maitra & Dawar, 2019). This spillover of shock from one market to other is explained by the theory of style investing, which is a process of classification of large number of assets classes like equity, debt, commodities, currency etc. into different categories (styles) based on some common characteristics and allocating funds among these styles, rather than individual securities (Barberis & Shleifer, 2003). Styles force prices to deviate from their fundamental values as they generate common factors in the assets within styles, which may be completely different otherwise and asset starts co-moving with other assets within style (Adams & Gluck, 2015). finanCialization and dynamiCs of CurrEnCy futurEs markEt… 4545 This phenomenon can be understood with figure 1. In broad area of invest- ment management, investors can focus on one particular asset or may go for portfolio diversification. If they choose to diversify through portfolio, they can include a mix of asset classes such as equity, commodity, currency etc. in their portfolio, which becomes their investment style. Now, if any kind of association in the form of correlation or spillover occurs within a style, it leads to financial- ization of asset class. Figure 1. Understanding Style Investing diversification. If they choose to diversify through portfolio, they can include a mix of asset classes such as equity, commodity, currency etc. in their portfolio, which becomes their investment style. Now, if any kind of association in the form of correlation or spillover occurs within a style, it leads to financialization of asset class. Figure 1. Understanding Style Investing Source: compiled by authors from various arguments in the review of literature. Currency is often included as an asset class in the professionally managed portfolios like hedge funds and mutual funds, in which they invest for the purpose of portfolio rebalancing, which led to its integration with other financial markets (Kutty, 2010). This process of integration is known as financialization where dominance of finance industry increases and its role expands in overall economy (Casey, 2011). This kind of market behavior is more evident after the period of financial crisis (Solnik, 1987; Shen, Tang, Xing & Ng, 2020). The spillover from one market to other were non- existent before Global Financial Crisis (GFC), however, it was observed afterwards (Kumar & Gupta, 2023). Büyüksahin and Robe (2014) also observed that after GFC period, cross linkages between commodity market and equity market increased. Similar evidences are available in different studies conducted in different financial markets (Kang, Maitra, Dash & Brooks, 2019; Chatziantoniou, Filippidis, Filis & Gabauer, 2021). In addition, Asian financial crisis 1997 also made significant impact on Asia-Pacific real estate markets. Before crisis period, these markets were not integrated, however, it became integrated afterwards (Gerlach, Wilson & Zurbruegg, 2006). Therefore, financial crisis can increase connectedness in different S o u r c e : compiled by authors from various arguments in the review of literature. Currency is often included as an asset class in the professionally managed port- folios like hedge funds and mutual funds, in which they invest for the purpose of portfolio rebalancing, which led to its integration with other financial mar- kets (Kutty, 2010). This process of integration is known as financialization where dominance of finance industry increases and its role expands in overall economy (Casey, 2011). This kind of market behavior is more evident after the period of financial crisis (Solnik, 1987; Shen, Tang, Xing & Ng, 2020). The spillover from one mar- ket to other were non-existent before Global Financial Crisis (GFC), however, it was observed afterwards (Kumar & Gupta, 2023). Büyüksahin and Robe (2014) also observed that after GFC period, cross linkages between commodity mar- ket and equity market increased. Similar evidences are available in different studies conducted in different financial markets (Kang, Maitra, Dash & Brooks, 2019; Chatziantoniou, Filippidis, Filis & Gabauer, 2021). In addition, Asian fi- nancial crisis 1997 also made significant impact on Asia-Pacific real estate mar- Adish Kumar, Kapil Gupta 4646 kets. Before crisis period, these markets were not integrated, however, it be- came integrated afterwards (Gerlach, Wilson & Zurbruegg, 2006). Therefore, financial crisis can increase connectedness in different markets due to chain of reactions from investors during and after crisis period (Kang & Lee, 2019). Similarly, the negative impact of COVID-19 on financial markets around the world is evident (Mirza, Naqvi, Rahat & Rizvi, 2020). The policies adopted to control the spread of disease contributed to supply shock. To safeguard staff from catching infection, offices and factories were shut down entirely (Liu, Manzoor, Wang, Zhang, & Manzoor, 2020). It hampered the economic activi- ty around the world badly. Therefore, equity markets fell badly (Joshi, 2022) and this health crisis translated into financial and economic crisis also (Sahoo, 2021). Negative sentiment of investors proved to be the best explanation for significant fall of equity markets around the world (Liu et al., 2020). In the backdrop of the theory of financial crisis and its effect on differ- ent markets and market behavior of investors, this study examines inter-re- lationship and connectedness across two asset classes namely Nifty futures and Currency futures in India. Following are noticeable research gaps. Firstly, to the best of author’s knowledge, there is no study that examines inter-rela- tionship between these two markets during COVID-19. Secondly, there is lim- ited and yet unsettled debate in literature1 on relationship between these two markets in India. In literature2, there is divergent evidence on influence of currency market on equity market. Adjasi, Harvey and Agyapong (2008) observed inverse rela- tionship between exchange rate volatility and equity market returns in Ghana stock exchange. However, Kutty (2010) found no relationship between the two markets during long-run in Mexico stock exchange. Furthermore, very weak relationship was observed between equity market and exchange rate in Johan- nesburg Stock Exchange (Mlambo, Maredza & Sibanda, 2013). In the Indian context, Agrawal, Srivastav and Srivastava, (2010) observed inverse relationship between pair of INR/USD currency and Nifty returns. However, Yadav (2016) found this correlation insignificant and observed uni- directional causality was also observed from equity market to currency mar- 1 Please see Frankel and Rodriguez (1975), Yadav (2016), Aravind (2017) and Mai- tra and Dawar (2019). 2 Please see Adjasi et al., (2008), Agrawal et al., (2010), Kutty (2010) and Mlambo et al., (2013). finanCialization and dynamiCs of CurrEnCy futurEs markEt… 4747 ket. However, Aravind (2017) found no causality between these two markets. In addition, Maitra and Dawar (2019) observed that only USD affects Nifty and Sensex while both indices affect exchange rate. Derivatives were first launched in India in equity segment at National Stock Exchange (NSE) with introduction of Index futures contracts in June, 2000. In addition, trading in currency futures was launched at NSE in August, 20083. Since then, there is tremendous growth in volume of trading of both instru- ments. Index futures and equity futures are consistently in top 10 countries among Global derivative exchanges since 2011 with exception of 2018 while currency futures are in top 3 countries since 2014 in terms of number of con- tracts traded4. Furthermore, it is noticeable that Indian equity market is substantially participated by Foreign Portfolio Investors (FPI). In financial year 2020-21, equity market recorded a net investment of ₹ 274 thousand crore from FPIs (Annual Report of Securities and Exchange Board of India, 2020-21. However, COVID-19 negatively affected Indian equity market. Two benchmark indices i.e. Nifty and Sensex fall by 23.8% and 26% respectively during financial year 2019-20 (Annual Report of Securities and Exchange Board of India, 2019-20). Furthermore, during initial phase of COVID-19, net foreign portfolio invest- ment in equity segment became negative and it remained volatile afterwards (figure 2). Therefore, it is evident from the above figure that foreign portfolio invest- ment fall significantly and remained volatile during COVID-19 period in In- dia. However, there is dearth of studies in such an important market and the evidence is contradicting (Agrawal et al., (2010), Yadav (2016) and Aravind (2017)). Furthermore, to the best of author’s knowledge, no study is available in literature that studies COVID-19 as structural break. Therefore, present study attempts to plug this research gap and contribute to the scarce literature. 3 Please see Indian Securities Market, A Review 2008-09 report, page no. 158 and 174, available at https://www1.nseindia.com/research/dynaContent/ismr.htm. 4 Please see IOMA Derivative Market Survey Report, 2020, page no. 17 and 30, avail- able at https://www.world-exchanges.org/our-work/articles/derivatives-report-2020. Adish Kumar, Kapil Gupta 4848 Figure 2. Foreign Portfolio Investment monthly data for the year 2020 Derivatives were first launched in India in equity segment at National Stock Exchange (NSE) with introduction of Index futures contracts in June, 2000. In addition, trading in currency futures was launched at NSE in August, 20083. Since then, there is tremendous growth in volume of trading of both instruments. Index futures and equity futures are consistently in top 10 countries among Global derivative exchanges since 2011 with exception of 2018 while currency futures are in top 3 countries since 2014 in terms of number of contracts traded4. Furthermore, it is noticeable that Indian equity market is substantially participated by Foreign Portfolio Investors (FPI). In financial year 2020-21, equity market recorded a net investment of ₹ 274 thousand crore from FPIs (Annual Report of Securities and Exchange Board of India, 2020-21. However, COVID-19 negatively affected Indian equity market. Two benchmark indices i.e. Nifty and Sensex fall by 23.8% and 26% respectively during financial year 2019-20 (Annual Report of Securities and Exchange Board of India, 2019-20). Furthermore, during initial phase of COVID-19, net foreign portfolio investment in equity segment became negative and it remained volatile afterwards (figure 2). Figure 2. Foreign Portfolio Investment monthly data for the year 2020 3Please see Indian Securities Market, A Review 2008-09 report, page no. 158 and 174, available at https://www1.nseindia.com/research/dynaContent/ismr.htm. 4Please see IOMA Derivative Market Survey Report, 2020, page no. 17 and 30, available at https://www.world- exchanges.org/our-work/articles/derivatives-report-2020. S o u r c e : based on Foreign Portfolio Investment monthly data extracted from website of Nation- al Securities Depository Limited (NSDL) for year 2020. The remaining paper is structured as follows: section 2 presents detail of data, section 3 depicts methodology used to conduct this study and is followed by sec- tion 4, which focuses on results and discussions of study. Section 5 concludes the study. The Research Methodology And The Course Of The Research ProcessThe Research Methodology And The Course Of The Research Process Data DescriptionsData Descriptions Indian Rupee is paired with 4 major currencies namely YEN, EUR, GBP and USD5. The daily exchange rate data for YEN (¥), EUR (€), GBP (£) and USD ($) to Indian Rupee (₹) and for Nifty Futures near month contracts has been down- loaded from the official website of NSE. Data is downloaded for the period as mentioned in table 1. 5 Please see Annual Report of Securities and Exchange Board of India, 2020-21, page no. 102 available at https://www.sebi.gov.in/sebiweb/home/HomeAction.do?doList- ing=yes&sid=4&ssid=80&smid=101. finanCialization and dynamiCs of CurrEnCy futurEs markEt… 4949 Table 1. Description of Sample Period For Equity For Currency Full Sample Period Impact of COVID-19 Pre-COVID-19 During COVID-19 Nifty 50 Index Futures YEN (For 100 ¥ to 1 ₹) Feb 2010 to Nov 2021 Feb 2010 to Dec 2019 Jan 2020 to Nov 2021 EUR (For 1 € to 1 ₹) Feb 2010 to Nov 2021 Feb 2010 to Dec 2019 Jan 2020 to Nov 2021 GBP (For 1 £ to 1 ₹) Feb 2010 to Nov 2021 Feb 2010 to Dec 2019 Jan 2020 to Nov 2021 USD (For 1 $ to 1 ₹) Sept 2008 to Nov 2021 Sept 2008 to Dec 2019 Jan 2020 to Nov 2021 S o u r c e : compiled by author. Data for currency pairs has been taken from the date of inception of trad- ing in respective currencies to November, 2021. Analysis is done for full peri- od as well as after dividing the data into structural breaks for pre and during COVID-19 periods. Materials and MethodsMaterials and Methods To examine the presence of unit root in currency and Nifty futures contracts, Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests are used. First difference log returns of both time series are found to be stationery. The re- sults are reported not reported to save space, however same are available on demand. Moreover, to check for the co-movement between these two markets, Johansen’s Co-integration test is used. Furthermore, to check for direction of spillover between these markets, Granger Causality test is applied. In addition, Bi-variate correlation is also examined to check degree of association between these markets. Vector Auto Regression (VAR) is also applied to check lead-day relationship between two markets. In addition, Auto Regressive Conditional Heteroscedasticity (ARCH) test is applied to check for the Heteroscedasticity in return series of both currencies and NIFTY. DCC GARCH model has been used also to account for the time varying correlation between the two series. This model has been employed because if the time series under study is heteroske- dastic in nature and it needs to be tested through a model, which allows varia- tion due to conditional factors (Singhal & Ghosh, 2016). Moreover, when more and more variables are added to this system, the correlation results do not dif- fer and the model remains accurate (Engle, 2002). Adish Kumar, Kapil Gupta 5050 The Covariance matrix in DCC-GARCH model proposed by Engle (2002) is explained as follows: Ht = Dt Rt Dt Where: Ht = Conditional Covariance Matrix, Dt = kxk Diagonal matrix of time varying standard deviation from univariate GARCH model with (σ2 i,t)1/2 on ith diagonal, Rt = Time varying correlation matrix. To fit this model, Rt has to be positive and the sum of estimated parameters should be less than or equal to 1. On the basis of given methodology, two param- eters i.e θ1 and θ2 are estimated. Theta 1 parameter is associated with short-run persistence and theta 2 with long-run persistence of shock on dynamic condi- tional correlation. The Outcome Of The Research Process And ConclusionsThe Outcome Of The Research Process And Conclusions Descriptive StatisticsDescriptive Statistics The results of descriptive statistics are reported in Table 2(a) and 2(b). Mean returns for all currencies and Nifty are approximately zero, which indicates that market exhibits mean reversion behavior. Moreover, returns for all sub- periods for currencies (except Yen during COVID-19 period) are positive, which shows depreciation of INR against various currencies during the sample peri- od. Nifty returns for all sub – periods are positive, which confirms continuous growth of Indian equity market6. In addition, co-efficient of skewness and kurtosis are significant for both as- set classes during all sub-periods, which confirms that returns are not normal- ly distributed and are leptokurtic, which is confirmed by Jarque-Bera statistics that all return series are significant at 5% level of significance. 6 Please see Indian Securities Market, A Review 2019 report, page no. 18, available at https://www1.nseindia.com/research/dynaContent/ismr.htm. finanCialization and dynamiCs of CurrEnCy futurEs markEt… 5151 Table 2(a). Descriptive Statistics (Currency Futures Contracts) Full Sample Period Pre-COVID – 19 During COVID – 19 Yen Euro GBP USD Yen Euro GBP USD Yen Euro GBP USD Observa- tions 2934 2934 2934 3277 2459 2459 2459 2802 475 475 475 475 Mean 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 0.00 Median 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.00 0.00 0.00 0.00 Std. Dev. 0.01 0.01 0.01 0.01 0.04 0.03 0.01 0.01 0.01 0.00 0.01 0.00 Skewness 0.29 0.02 -0.49 0.28 0.29 0.15 -0.57 0.23 0.17 0.73 -1.71 0.9 Kurtosis 7.06 6.48 12.40 7.76 6.57 6.33 13.38 7.49 11.40 5.64 20.58 8.67 Jarque- -Bera Test 2055.2* 1499.3* 10926.3* 3146.3* 1342.8* 1143* 11180.9* 2386.8* 1400.2* 81.2* 6346.6* 701.1* *Significant at 1% level of significance S o u r c e : based on author’s calculations. Table 2(b). Descriptive Statistics (Nifty Futures Contracts) Against Full Sample Period Pre-COVID – 19 During COVID – 19 Yen, Euro and GBP USD Yen, Euro and GBP USD Yen, Euro, GBP and USD Observations 2934 3277 2459 2802 475 Mean 0.00 0.00 0.00 0.00 0.00 Median 0.00 0.00 0.00 0.00 0.00 Std. Dev. 0.01 0.01 0.00 0.01 0.01 Skewness -0.89 -0.30 -0.09 0.15 -1.71 Kurtosis 17.44 20.12 5.01 19.11 20.58 Jarque-Bera Test 25876.3* 40105.2* 417.1* 30299.6* 6346.6* *Significant at 1% level of significance S o u r c e : based on author’s calculations. Adish Kumar, Kapil Gupta 5252 Bi-variate CorrelationBi-variate Correlation The results (Table 3) show that except for GBP, all currencies exhibit negative correlation during all sub-periods and it is statistically significant at 5% lev- el of significance (Agrawal et al., 2010). This may be due to the argument ex- plained earlier that positive news in one market can be negative in other market (Maitra and Dawar, 2019). These results are consistent with Aravind (2017), which states that when currency and equity are viewed as asset classes, there should be inverse relationship between two markets for the reason that inves- tor employs funds in asset class that flourishes by withdrawing it from under- performing asset. Table 3. Bi-variate Correlation Event YEN and Nifty 50 Futures EUR and Nifty 50 Futures GBP and Nifty 50 Futures USD and Nifty 50 Futures Coefficient Coefficient Coefficient Coefficient Full Sample Period -0.38* -0.18* 0.11* -0.45* Pre-COVID-19 -0.41* -0.19* -0.17* -0.45* During COVID-19 -0.36* -0.17* 0.09* -0.44* *Significant at 1% level of significance S o u r c e : based on author’s calculations. Granger Causality TestGranger Causality Test The results (Table 4) display that for all currencies except for GBP, currency fu- tures is causing Nifty futures during COVID-19 period i.e. the period of market stress. However, weak causality is also observed from Nifty futures to curren- cy during this period for EUR, GBP and USD. Currency futures may be leading Nifty futures due to the fact that during COVID-19 period, net foreign portfolio investment in equity segment became negative (Figure 2), which led to fall in equity market. In addition, Nifty futures is causing currency futures for all four currencies in pre-COVID-19 period and strong uni-directional causality is observed from Nifty to currency futures in case of USD during the full sample period (Yadav, 2016). This result is also consistent with argument given by Aravind (2017) finanCialization and dynamiCs of CurrEnCy futurEs markEt… 5353 that, when equity market flourishes, it attracts foreign investment. Further- more, no causality is observed during full sample period in case of Yen and GBP (Aravind, 2017), which may be due to the fact that biggest percentage of as- set holding at NSE in equity segment is of United States of America, who holds 34.3 percentage of total equity assets of FPIs as on March 31, 2021, however, United Kingdom and Japan holds 5.3 percent and 2.6 percent shares respec- tively7. Therefore, there may be no causality in case of YEN and GBP either to or from equity futures market. Table 4. Granger Causality Test Event Null Hypothesis YEN F Statistics EUR F Statistics GBP F Statistics USD F Statistics Full Sample Period NFR does not cause CFR 1.01 1.63 1.30 4.48* CFR does not cause NFR 1.38 1.80*** 0.82 1.09 Pre-COVID-19 NFR does not cause CFR 5.25** 6.84* 3.03** 4.67* CFR does not cause NFR 1.52 0.59 0.82 1.50 During COVID-19 NFR does not cause CFR 1.28 1.73*** 4.27* 2.35** CFR does not cause NFR 4.37* 3.55* 0.83 2.84* *Significant at 1% level of significance **Significant at 5% level of significance ***Significant at 10% level of significance Note: • NFR denotes Nifty Futures Returns • CFR denotes Currency Futures Returns S o u r c e : based on author’s calculations. 7 Please see Annual Report of Securities and Exchange Board of India, 2020-21, page no. 163 available at https://www.sebi.gov.in/sebiweb/home/HomeAction.do?doList- ing=yes&sid=4&ssid=80&smid=101. Adish Kumar, Kapil Gupta 5454 Johansen Co-integration TestJohansen Co-integration Test The results are reported in table 5 (a-d). There is an evidence of long-run re- lationship between equity futures and currency futures in case of YEN during COVID-19 period only (Kutty, 2010). Nevertheless, the evidence is weak. There- fore, Vector Error Correction Methodology (VECM) is not applied. For other currencies namely GBP, EUR and USD, there is no evidence of long-run relation- ship between equity futures and currency futures market in any sub-period as well as full period. Table 5. Johansen’s Co-integration Test (a) Yen and Nifty Futures Event Hypothesized No. of CE(s) Eigen Value Maximum Eigen Value Test Trace Test Critical Values Test Statistics Test Statistics Full Sample Period None 0.00 8.01 11.95 At Most 1 0.00 3.93 3.93 Pre-COVID-19 None 0.00 8.21 13.76 At Most 1 0.00 5.55 5.55 During COVID-19 None 0.04 17.92*** 27.46** At Most 1 0.02 9.54 9.54 *Significant at 1% level of significance **Significant at 5% level of significance ***Significant at 10% level of significance S o u r c e : based on author’s calculations. finanCialization and dynamiCs of CurrEnCy futurEs markEt… 5555 (b) EUR and Nifty Futures Event Hypothesized No. of CE(s) Eigen Value Maximum Eigen Value Test Trace Test Critical Values Test Statistics Test Statistics Full Sample Period None 0.00 9.01 15.25 At Most 1 0.00 6.24 6.24 Pre-COVID-19 None 0.00 10.12 14.90 At Most 1 0.00 4.78 4.78 During COVID-19 None 0.03 15.22 21.75 At Most 1 0.01 6.53 6.53 S o u r c e : based on author’s calculations. (c) GBP and Nifty Futures Event Hypothesized No. of CE(s) Eigen Value Maximum Eigen Value Test Trace Test Critical Values Test Statistics Test Statistics Full Sample Period None 0.00 5.43 9.61 At Most 1 0.00 4.19 4.19 Pre-COVID-19 None 0.00 9.56 12.81 At Most 1 0.00 3.24 3.24 During COVID-19 None 0.03 12.75 21.95 At Most 1 0.02 9.20 9.20 S o u r c e : based on author’s calculations. Adish Kumar, Kapil Gupta 5656 (d) USD and Nifty Futures Event Hypothesized No. of CE(s) Eigen Value Maximum Eigen Value Test Trace Test Critical Values Test Statistics Test Statistics Full Sample Period None 0.00 8.64 14.31 At Most 1 0.00 5.66 5.66 Pre-COVID-19 None 0.00 10.52 17.21 At Most 1 0.00 6.68 6.68 During COVID-19 None 0.03 13.17 22.17 At Most 1 0.02 8.99 8.99 S o u r c e : based on author’s calculations. Vector Auto Regression (VAR)Vector Auto Regression (VAR) The results in Table 6 (a-d) exhibits that during COVID-19 period, currency fu- tures lead Nifty futures by one day. It may be due to the reason that during COVID-19 period, there was so much volatility in foreign portfolio investment (Figure 2), which created a spillover effect on equity futures market and it started following currency futures market and also there is difference in tim- ing of market operation. However, in pre-COVID-19 period, Nifty futures was leading currency fu- tures by one day. This is consistent with the argument given above that during bull-run of equity market, more foreign investment is attracted. In full sample period, lead-lag relationship is observed only in case of YEN and EURO. In case of YEN, currency futures lead Nifty futures by one day and in case of EURO, it is other way around. finanCialization and dynamiCs of CurrEnCy futurEs markEt… 5757 Table 6. Vector Auto Regression Methodology (a) Yen Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR Constant 0.77 2.02** 0.78 1.76*** -0.12 0.76 CFR(-1) -0.43 2.05** -0.57 1.23 -1.51 3.65* CFR(-2) -1.27 -0.52 NA NA 2.17** -2.80* CFR(-3) -1.39 0.48 NA NA -0.03 0.80 CFR(-4) 1.95*** -0.48 NA NA 0.96 -1.15 CFR(-5) 0.16 0.39 NA NA 1.01 -1.23 CFR(-6) 0.25 -1.31 NA NA -0.36 -1.21 CFR(-7) -0.88 -1.07 NA NA -0.66 -1.63 NFR(-1) -0.96 0.22 -2.29** 1.98** 0.89 0.36 NFR(-2) -1.35 0.63 NA NA 1.59 -1.53 NFR(-3) -0.49 0.74 NA NA 1.53 2.12** NFR(-4) 0.08 -0.93 NA NA 0.16 -2.27** NFR(-5) -1.21 3.37* NA NA -1.33 4.20* NFR(-6) 0.87 -3.83* NA NA 0.64 -3.55* NFR(-7) -1.34 1.95*** NA NA -1.01 2.83* *Significant at 1% level of significance **Significant at 5% level of significance ***Significant at 10% level of significance S o u r c e : based on author’s calculations. (b) EUR Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR Constant 1.04 2.04** 0.94 1.85*** 0.43 0.84 CFR(-1) 3.96* -0.06 3.19* -1.07 2.03** 2.37** Adish Kumar, Kapil Gupta 5858 Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR CFR(-2) -1.50 1.02 -1.81 0.24 -0.43 1.75*** CFR(-3) -0.76 -1.16 NA NA 1.76*** -0.70 CFR(-4) 0.87 2.06** NA NA 1.48 0.39 CFR(-5) 2.42** -1.62 NA NA 1.51 -2.49** CFR(-6) -1.01 0.25 NA NA -0.85 -1.95*** CFR(-7) -0.26 -2.12** NA NA -0.61 -2.18** NFR(-1) -2.22** -0.51 -3.36* 1.38 1.39 -1.52 NFR(-2) -0.49 0.92 -1.48 0.12 1.53 -0.13 NFR(-3) 0.22 0.49 NA NA 1.03 1.51 NFR(-4) -1.81*** -0.48 NA NA -1.56 0.62 NFR(-5) -1.02 3.20* NA NA -1.81*** 4.46* NFR(-6) 1.23 -3.59* NA NA 1.01 -2.99* NFR(-7) -0.95 2.34** NA NA -0.40 2.86* S o u r c e : based on author’s calculations. (c) GBP Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR Constant 0.94 2.04** 0.93 1.83*** 0.21 0.74 CFR(-1) 3.68* -0.08 3.44* -0.84 -0.11 1.77*** CFR(-2) -1.21 1.69*** -2.35** 1.01 0.58 0.84 CFR(-3) -0.44 -0.91 NA NA 0.68 -0.21 CFR(-4) 0.64 0.06 NA NA 1.02 0.04 CFR(-5) 0.92 -0.95 NA NA -1.36 -0.34 CFR(-6) 0.91 -0.75 NA NA 0.33 -0.88 CFR(-7) -1.01 -0.82 NA NA -3.91* -0.92 Table 6. Vector… finanCialization and dynamiCs of CurrEnCy futurEs markEt… 5959 Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR NFR(-1) -1.44 -0.62 -2.34** 1.35 1.33 -1.56 NFR(-2) 1.66*** 1.03 -0.71 0.29 4.39* -0.16 NFR(-3) 0.73 0.55 NA NA 3.06* 1.47 NFR(-4) -1.79*** -0.84 NA NA -0.83 0.16 NFR(-5) -0.78 3.31* NA NA 0.11 4.40* NFR(-6) 0.27 -3.68* NA NA -0.41 -3.07* NFR(-7) 0.17 2.67* NA NA 0.65 3.12* S o u r c e : based on author’s calculations. (d) USD Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR Constant 2.06** 1.85*** 1.92*** 1.81*** 0.89 0.59 CFR(-1) 0.07 0.25 0.41 -0.57 -2.22** 2.27** CFR(-2) -3.79* -1.85*** -3.50* -2.33** -1.57 0.69 CFR(-3) 0.14 -0.66 -0.05 -1.47 0.13 1.49 CFR(-4) 0.16 0.56 0.23 -0.26 -0.71 1.59 CFR(-5) 2.81* -0.33 2.84* -1.06 0.61 0.35 CFR(-6) -0.58 -1.69*** -0.89 -0.63 0.67 -3.11* CFR(-7) 2.11** -0.29 2.16** -0.76 0.56 -0.68 CFR(-8) 0.00 0.81 -0.13 1.49 NA NA NFR(-1) -3.75* 0.97 -3.97* 1.89*** -1.09 -0.53 NFR(-2) -1.89*** -1.71*** -1.39 -2.83* -1.28 0.53 NFR(-3) -2.49** 0.40 -2.46** -0.72 -0.38 2.48** NFR(-4) -2.59* -0.81 -2.86* -1.12 -0.48 1.21 NFR(-5) 0.23 1.45 1.43 -1.53 -2.61* 4.54* Table 6. Vector… Adish Kumar, Kapil Gupta 6060 Event Full Sample Period Pre-COVID 19 During COVID-19 CFR NFR CFR NFR CFR NFR NFR(-6) 2.21** -4.86* 1.06 -2.42** 2.11** -4.24* NFR(-7) 0.01 0.02 -0.21 0.63 -0.78 2.17** NFR(-8) 1.33 2.32** 1.51 2.71* NA NA S o u r c e : based on author’s calculations. ARCH and DCC GARCH ParametersARCH and DCC GARCH Parameters From table 7, it is observed that heteroscedasticity is present in all return se- ries of currencies as well as NIFTY. All the p-values are significant at 1% level of significance. Therefore, checking correlation through static models can be misleading and give false results. Hence, DCC-GARCH model is applied, which is a heteroscedastic model and also allows for variation in correlation due to time. Table 7. ARCH Parameters Event ARCH (NIFTY) ARCH (YEN) ARCH (EURO) ARCH (GBP) ARCH (USD) Full Period 64.42* 37.71* 29.91* 24.79* 62.34* Pre-COVID-19 Period 51.32* 16.91* 20.07* 13.72* 52.27* During COVID-19 Period 3.42* 36.51* 16.73* 23.22* 4.74* *Significant at 1% level of significance S o u r c e : based on author’s calculations. From table 8, it is clear that there exists time varying correlation between cur- rency returns for all 4 currencies and Nifty. Both parameters i.e theta 1 and theta 2 are significant in full period, which states that there is both short-run and long-run persistence of shock on dynamic conditional correlation. All the results for full period and pre-COVID 19 period are significant at 1% level of significance. However, during COVID-19 period, short run persistence of shock became insignificant at 5% level of significance, which clearly indicates that Table 6. Vector… finanCialization and dynamiCs of CurrEnCy futurEs markEt… 6161 COVID-19 changed some dynamics of market for both currency and equity for short term. This may be due to the reason that there was a huge outflow of For- eign Portfolio Investment (FPI) from Indian equity markets. However, long-run persistence indicated by theta 2 became significant again for all currencies ex- cept GBP, which indicates that once the initial period of COVID-19 passed, FPI inflow again became significant. Therefore, with these changed dynamics of market, time varying correlation again became significant. In addition, for all sub-periods, stability condition of model i.e all parameters should be definite positive and their sum should be less than or equal to 1 is also met. There- fore, there is a definite time varying correlation between both markets in both short-run and long-run. Table 8. DCC-GARCH Parameters Event DCC Parameters GARCH (YEN) GARCH (EURO) GARCH (GBP) GARCH (USD) Full Period θ1 0.02* 0.01* 0.01* 0.01* θ2 0.93* 0.97* 0.98* 0.96* Pre COVID -19 Period θ1 0.03* 0.01* 0.00** 0.01* θ2 0.92* 0.97* 0.98* 0.97* During COVID -19 Period θ1 0.01 0.01*** 0.11*** 0.02 θ2 0.97* 0.97* 0.85 0.83* Stability Condition Theta (1) + Theta (2) < 1 is met. *Significant at 1% level of significance S o u r c e : based on author’s calculations.  Conclusion Conclusion Present study examines inter-linkages between currency and Nifty futures markets in India. Daily closing prices of currency futures of four major cur- rencies namely YEN, EUR, GBP and USD to Indian Rupee and Nifty futures con- tracts has been downloaded from the website of NSE from the date of incep- tion of trading of respective currency futures contracts to November, 2021. COV ID- 19 is taken as structural break intuitively and data is divided into three sub-periods i.e. Full sample period, pre-COVID-19 and during COVID-19. Adish Kumar, Kapil Gupta 6262 It is stated that except GBP, all currencies display significantly negative cor- relation with Nifty futures contracts (Aravind, 2017), which may be an evi- dence of financialization. In addition, currency futures is causing Nifty futures during COVID-19 i.e. period of market turmoil, which may be due to volatility in net foreign portfolio investment during this period. However, in pre-COV ID- 19 period, Nifty was causing currency futures (Yadav, 2016). Furthermore, there is no long-run relationship between these two markets (Kutty, 2010). Weak evidence of co-integration is found in case of Yen. Moreover, currency futures is leading Nifty futures during COVID-19 period by one day. However, in pre- COV ID-19 period, Nifty futures was leading by one day. Due to presence of ARCH effect, DCC GARCH model is applied, which indicates that time varying correlation is present and both shock short-run and long-run shocks persist be- tween two markets except for the short-run period of COVID-19. In a nutshell, study suggests that there is an evidence of financialization of currency futures market in India, which is not documented yet in literature to the best of author’s knowledge. This study also contributes to the ongoing dis- cussion in literature by adding that integration between asset classes increas- es during period of market turmoil. These results are consistent with Kutty (2010), Yadav (2016) and Aravind (2017) in different aspects. The findings may be useful for policymakers as they need to pay attention to the consequences of financialization. The investors may diversify their port- folio and make proportionate investment in different assets based on volatility and integration between markets. Furthermore, this study examines financial- ization of currency market only, whereas, the scope of study can be extended to other assets also i.e. bitcoin, commodities etc. This study also limits the spillo- ver analysis to India only, whereas one can study cross-country spillover also.  References References Adams, Z., & Glück, T. (2015). Financialization in commodity markets: A passing trend or the new normal?. Journal of Banking & Finance, 60, 93-111. https://doi.org/10.1016/j. jbankfin.2015.07.008. Adjasi, C., Harvey, S.K., & Agyapong, D. (2008). Effect of exchange rate volatility on the Ghana Stock Exchange. African Journal of Accounting, Economics, Finance and Bank- ing Research, 3(3), 31–47. Agrawal, G., Srivastav, A.K., & Srivastava, A. (2010). A study on exchange rate move- ments and stock market volatility. International Journal of Business and Manage- ment, 5(12), 62–73. finanCialization and dynamiCs of CurrEnCy futurEs markEt… 6363 Aravind, M. (2017). FX volatility impact on Indian Stock Market: An empirical investiga- tion. Vision, 21(3), 284-294. Barberis, N., & Shleifer, A. (2003). Style investing. Journal of Financial Economics, 68(2), 161-199. https://doi.org/10.1016/S0304-405X(03)00064-3. Büyüksahin, B., & Robe, M.A. (2014). Speculators, commodities and cross-market linkag- es. Journal of International Money and Finance, 42, 38-70. https://doi.org/10.1016/j. jimonfin.2013.08.004. Casey, T. (2011). Financialization and the future of the neo-liberal growth model. In: Political Studies Association Conference Proceedings. https://www.academia. edu/2743106/Financialization_and_the_Future_of_the_Neo_liberal_Growth_Model (ac- cessed: 02.02.2022). Chatziantoniou, I., Filippidis, M., Filis, G., & Gabauer, D. (2021). A closer look into the global determinants of oil price volatility. Energy Economics, 95, 105092. https:// doi.org/10.1016/j.eneco.2020.105092. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate gener- alized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350. https://doi.org/10.1198/073500102288618487. Frankel, J.A., & Rodriguez, C.A. (1975). Portfolio equilibrium and the balance of pay- ments: A monetary approach. American Economic Review, 65(4), 674–688. Gerlach, R., Wilson, P., & Zurbruegg, R. (2006). Structural Breaks and Diversification: The Impact of the 1997 Asian Financial Crisis on the Integration of Asia-Pacific Real Es- tate Markets. Journal of International Money and Finance, 25(6), 974–991. https://doi. org/10.1016/j.jimonfin.2006.07.002. Joshi, N.A. (2022). Impact Of Covid-19 On Performance On Indian Stock Indices: A Study For Nse Composite And Sectoral Indices. Copernican Journal of Finance & Account- ing, 11(4), 125-146. https://doi.org/10.12775/CJFA.2022.022. Kang, S.H., & Lee, J.W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications, 526, 1-14. Kang, S.H., Maitra, D., Dash, S.R., & Brooks, R. (2019). Dynamic spillovers and connect- edness between stock, commodities, bonds, and VIX markets. Pacific-Basin Finance Journal, 58(C). https://doi.org/10.1016/j.pacfin.2019.101221. Kumar, A., & Gupta, K.A. (2023) Bibliometric Analysis on Financialization: Current Sta- tus and Future Directions. Journal of Commerce and Accounting Research, 12(4), 55-62. Kutty, G. (2010). The relationship between exchange rates and stock prices: The case of Mexico. North American Journal of Finance and Banking Research, 4(4), 1–12. Liu, H., Manzoor, A., Wang, C., Zhang, L., & Manzoor, Z. (2020). The COVID-19 out- break and affected countries stock markets response. International Journal of En- vironmental Research and Public Health, 17(8), 2800. https://doi.org/10.3390/ijer- ph17082800. Maitra, D., & Dawar, V. (2019). Return and volatility spillover among commodity fu- tures, stock market and exchange rate: Evidence from India. Global Business Re- view, 20(1), 214-237. https://doi.org/10.1177/0972150918803801. Adish Kumar, Kapil Gupta 6464 Mirza, N., Naqvi, B., Rahat, B., & Rizvi, S.K.A. (2020). Price reaction, volatility timing and funds’ performance during Covid-19. Finance Research Letters, 36, 101657 https://doi.org/10.1016/j.frl.2020.101657. Mittal, A., Sehgal, S., & Mittal, A. (2019). Dynamic currency linkages between select emerging market economies: An empirical study. Cogent Economics & Finance, 7(1), 1681581. https://doi.org/10.1080/23322039.2019.1681581. Mlambo, C., Maredza, A., & Sibanda, K. (2013). Effects of exchange rate volatility on the stock market: A case study of South Africa. Mediterranean Journal of Social Sciences, 4(14), 561. Sahoo, M. (2021). COVID-19 impact on stock market: Evidence from the Indian stock market. Journal of Public Affairs, 21(4), e2621. https://doi.org/10.1002/pa.2621. Shen, H., Tang, Y., Xing, Y., & Ng, P. (2020). Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula- -CoVaR approach, International Journal of Emerging Markets, 16(5), 929–945. Singhal, S., & Ghosh, S. (2016). Returns and volatility linkages between internation- al crude oil price, metal and other stock indices in India: Evidence from VAR-DCC- GARCH models. Resources Policy, 50, 276-288. https://doi.org/10.1016/j.resour- pol.2016.10.001. Solnik, B. (1987). Using financial prices to test exchange rate models: A note. The jour- nal of Finance, 42(1), 141-149. Yadav, S. (2016). Integration of exchange rate and stock market: Evidence from the Indian stock market . Indian journal of finance, 10(10), 56-63. Zghal, R., & Ghorbel, A. (2022). Bitcoin, VIX futures and CDS: a triangle for hedging the international equity portfolios, International Journal of Emerging Markets, 17(1), 71- 97. https://doi.org/10.1108/IJOEM-01-2020-0065.