TX_1~AT/TX_2~AT International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021230 International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2021, 11(4), 230-239. Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector Sanjeeta Shirodkar*, Guntur Anjana Raju Goa Business School, Goa University, Goa, 403206, India. *Email: sanjeeta.parab@unigoa.ac.in Received: 16 January 2021 Accepted: 20 April 2021 DOI: https://doi.org/10.32479/ijeep.11086 ABSTRACT The present study empirically examines the impact of Stock Futures on India’s underlying Energy Sector Stocks by incorporating the Structural breaks in the AR (1)-GARCH (1, 1) model. Although the issues relating to the effect of Derivatives trading on Cash Market Volatility have been empirically discussed in two ways: by evaluating Cash Market Volatilities during the Pre-and Post-Derivatives trading periods and, secondly, by determining the influence of Derivatives trading on the conduct of Cash Markets by comparing it with proxies. Nevertheless, these methodologies cannot isolate the influence of derivatives trading from the effects of other market reforms on the volatility of the underlying Cash Market. The study offers mixed results for the select sample of Energy sector stocks. However, there is evidence of a reduction in unconditional volatility for most energy sector stocks. The study’s findings suggest that trading in Stock Futures may not necessarily be associated with the destabilization of the underlying Energy sector Stocks. Keywords: Stock Futures, Volatility Modelling, ICSS Test, AR (1)-GARCH (1, 1), Structural Breaks, Futures Trading, Energy Sector JEL Classifications: G11, G14 1. INTRODUCTION Energy and Power sector is one of the most critical infrastructure components crucial to nations’ economic growth and well-being. For the sustainable growth of the Indian economy, the presence and construction of adequate infrastructure are essential. Power generation options range from traditional sources such as coal, lignite, natural gas, shale, hydro and nuclear power, to suitable non-conventional sources such as wind, solar, and household and agricultural waste. The country’s energy demand has grown steadily and is expected to grow more in the years to come. A significant addition to the installed generating capacity is expected to satisfy the growing demand for electricity in the region. India ranked fourth out of 25 nations in the Asia Pacific region in May 2018 on an index that assessed their total strength. As of 2018, India was ranked fourth in wind power, seventh in solar power and fifth in installed renewable power capacity. In the list of countries to make significant investments in renewable energy, India placed sixth at US$ 90 billion. Modelling financial asset volatility has remained one of the essential facets of economic analysis as it advises investors on risk trends found in investment and transaction processes. Trading of derivatives started in the Indian Markets in 2000 by introducing Futures Contracts on the National Stock Exchange (NSE) S&P CNX Nifty Index and BSE Sensex Bombay Stock Exchange (BSE). Trading options began in Indian markets in June 2001. Until then, the F&O market has expanded in terms of the number of contracts exchanged, price, and new product offering. The impact of introducing derivatives on Spot Market volatility and, in turn, its role in stabilizing or destabilizing cash markets have remained an essential subject of analytical and empirical interest. Issues relating to the effect of Derivatives trading on Cash Market Volatility have been empirically discussed in two ways: by evaluating Cash Market Volatilities during the Pre-and Post- Futures/Options trading periods and, secondly, by determining the influence of Options and Futures trading on the conduct of Cash Markets by comparing it with proxies. Furthermore, most of the This Journal is licensed under a Creative Commons Attribution 4.0 International License Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021 231 studies that analyzed the effect of Derivatives on the volatility of the underlying Spot Market used some form of GARCH Model with Dummy Variable Repressors. However, this approach is based on the implied presumption that any adjustments are observed during the time following Derivatives trading’s implementation due solely to Derivatives trading activity. Various factors such as introducing the Rolling Settlement System, Circuit Breakers, and stock exchange regulatory changes can also contribute to market volatility reduction. Failure to identify structural breaks in variances in the financial series under consideration will lead to a significant upward change in projected GARCH models’ Persistence. Various research studies such as Diebold (1986); Mikosch and Starica (2000); Diebold and Inoue (2001) have reported that neglect of structural disturbances may cause the GARCH model to be spuriously estimated. The presence of structural breaks in the volatility of financial markets has long been assumed. “The primary explanations for these systemic breaks may be due to changes in exchange rate system structures, global financial markets turmoil, or stock market evolution. The shocks caused by such significant economic or political events can cause financial time series behaviour to deviate from its tranquil time.” (Andreou and Ghysels, 2002; Wang and Moore, 2009) 2. LITERATURE REVIEW The derivatives market’s effect on the underlying spot market remains a topic frequently discussed with arguments both in favour and against. Bae et al. (2004) analyzed the effect of the Listing of Index Futures on the volatility and market efficiency of the underlying KOSPI 200 stocks, using non-KOSPI 200 stocks, and observed a parallel increase in volatility and market efficiency during the post-derived era. Other studies that find substantial rises in index return volatility following the implementation of Futures include Harris (1989), Brorsen (1991), Lee and Ohk (1992), Antoniou and Holmes (1995), and Yao (2016). Others argue that the introduction of Futures reduces the Spot Market’s volatility and thereby stabilizes the market. “One of the clarifications for the Destabilizing hypothesis is that a derivative trading destabilizes the underlying Spot Market by providing an additional route for information transmission and reflection in the Spot Market” (Cox and Ross, 1976; Ross, 1989). Gulen and Mayhew (2000) analyzed Index Futures’ effect on international stock markets’ volatility by using the GJR-GARCH and BEKK model to sample 21 European countries and found that Spot Market volatility has declined for most of the countries under study. Another school of thought suggests that Spot Market Volatility is increasing due to the liquidity provided by speculators. This extra liquidity helps Spot traders to hedge their position, thereby curbing uncertainty due to an order imbalance. Several studies such as Stoll and Whaley (1990); Pilar and Rafael (2002); Bandivadekar and Ghosh (2003); T. Mallikarjunappa (2008); Thenmozhi (2002); Kavussanos (2008); Raju and Karande (2003); Sarangi and Patnaik (2006) reported substantial declines in Indian spot market volatility. Rahman (2001) investigated the impact of Index Futures trading on the volatility of component stocks for the Dow Jones Industrial Average (DJIA) by employing the GARCH (1, 1) model and reported no change in conditional volatility. T.Mallikarjunappa (2008) and Afzal (2008); Thenmozhi (2002); Kavussanos (2008) inferred that the changes in the volatility process are not due to the introduction of Derivatives, but due to many other factors such as better information dissemination and more transparency. Anjana Raju and Shirodkar (2020) stated that “the listing of stock futures may not have any clear effect on the underlying stock’s volatility.” Chen et al. (2014) investigated the impact of structural breaks on the spot–futures oil prices and concluded that existing breakpoint indeed affects the forecast of oil futures volatility. Tabak and Cajueiro (2007) investigated the Brent and WTI crude oil markets’ performance and noticed that oil spot markets had been more competitive over time. Alvarez- Ramirez et al. (2008) have indicated that oil markets have demonstrated inefficiency in the short term, but have been influential in the long term. However, the literature is inconclusive about whether the introduction of derivatives leads to Spot Market volatility increasing or decreasing. The vast majority of studies in the derivative segment arena focus on Index Futures’ spot market impact. Indian Stock Futures studies concentrate on conceptual specifics or span a short time. The index-focused analysis does not consider the stock’s unique characteristics, which may also play a significant role in volatility creation. This study contributes in two ways to the on-going discussion of the effect derivatives on the underlying stock market volatility. First, this research uses a different methodology based on Aggarwal et al. (1999); Andreou and Ghysels (2002); Malik and Hassan (2004); Kang et al. (2009); Wang-Chen (2007). The analysis attempts to model with Stock Futures the volatility of the underlying Energy Sector Stocks by considering the volatility breaks. The present study investigates the effect of Stock Futures on the underlying Energy Sector stocks empirically; by defining the structural break, if any, in stock price volatility since the advent of derivatives trading, using Inclan and Tiao’s (1994) ICSS test. The Energy sector or industry comprises those companies involved in the exploration and expansion of Oil or gas reserves, oil and gas drilling, and refining. It also includes integrated power utility companies such as renewable energy and coal. Second, studying the impact of Single Stock Futures would allow us to directly examine a company’s response to Futures trading instead of Index Futures’ market-wide influence. 3. METHODS The Individual Stock Futures (ISF) has proven to be a principal financial instrument, and the NSE continues to account for most of the total volumes traded worldwide on the ISF. Our study’s resulting sample consists of 14 stocks in the energy sector and their respective future contracts. Data is sourced from the Bloomberg database. The analysis period ranges from 1 January 2000 to 31 March 2019, or the stock listing date (whichever is prior). Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021232 3.1. Testing for ARCH Effect Testing for ARCH involves testing the presence of heteroscedasticity in the time-series model. Engle introduced the Lagrange Multiple (LM) test to check for ARCH disorders. Let εt=yt−ut be the residual series. The squared series 2t∈ is utilized to implement the LM test for checking conditional heteroscedasticity. Under the null hypothesis, we have: 0 i: = 0, = 1, 2,……,H i qα Versus 1 : 0,≠iH for at least one iα In the Linear Regression 2 2 2 1 1 , 1, ..,t t q t q t q Nε ω α ε α ε− −= + +…+ = + … , Where q is the length of ARCH lags, and N is the number of observations used in the Regression equation. The test statistic for LM-test is defined by: LM = NR2 In this R2 is the R-squared from the Regression of εt 2 in the equation and defined by: R2 = Regressionsumof squares totalsumof squares Under the null hypothesis, the test statistics NR2 is distributed as a Chi-squared distribution with q degrees of freedom. H0 is rejected when LM > 2 ( )qαχ suggests that the ARCH effect exists in the time-series. 3.2. Testing for Multiple Structural Breaks (Iterated Cumulative Sums of Squares [ICSS]) Algorithm of Inclan and Tiao (1994) The Inclan and Tiao (1994) proposed Iterative Cumulative Sum of Squares (ICSS) algorithm enables identifying several breakpoints in variance in a time series. The idea behind the ICSS algorithm provided by Inclan and Tiao can be summarized in sequential steps. A time series of interest has an absolute stationary variance over an initial period before a sudden split occurs. The unconditional variance is stationary before the next abrupt shift occurs. This process repeats through time, giving a time series of observations with multiple breakpoints in n observations’ unconditional variance. 3.3. Associating the Volatility Breaks with Derivative Trading First, the dates of structural breaks in the stocks will be predicted, and later we will seek to correlate those dates with the dates of launch of derivative trading on individual stocks. AR (1)-GARCH (1, 1) is a GARCH family model, in which the mean is modelled by a first-order auto-regressive AR (1), with a GARCH (1, 1) error: [ ] 2, E 0, E 1, i.i.d.t t t t t t tx u σ ∈ ∈ ∈ ∈ = + = =  .., � �t tX� �1 , 2 2 2 0 1 1 1( )σ µ σ− − −= + − +t t t ta a X b Once all structural breakpoints have been identified, dummy variables are created for each break detected. Each dummy variable is denoted with a value ‘1’ from the location identified to the end of the data series and ‘0’ elsewhere. 4. RESULTS AND DISCUSSION Augmented Dickey-Fuller test results are shown in Table 1. All variables are non-stationary at the level since the P-value is more than 0.05%. The Unit Root test is, therefore performed in the first difference for all variables. All the series are stationary at a 1% level of significance at the first difference. The results of the ADF test indicate that all variables are integrated in the same order. Table 2 depicts the ARCH test results for all the fourteen Stocks traded at the Cash segment of NSE. The standard diagnostic test Table 1: Unit root test (Augmented Dickey-Fuller test) Stock Spot Futures Stock Spot Futures ADF at level ADF at First Difference ADF at level ADF at First Difference ADF at level ADF at First Difference ADF at level ADF at First Difference ADANIPOWER −2.669 (−0.079) −77.982 (−0.000) −1.840 (−0.361) −25.085 (−0.00) NTPC −1.903 (−0.330) −252.62 (−0.000) −1.840 (−0.361) −251.08 (−0.000) BPCL −3.075 (−0.112) −14.385 (−0.000) −3.067 (−0.114) −14.026 (−0.000) OIL −2.843 (−0.052) −264.13 (−0.000) −2.696 (−0.074) −264.04 (−0.000) GAIL −2.496 (0.116) (−240.73) (−0.000) −420.76 (−0.000) −420.76 (0.000) ONGC −1.793 (−0.389) −435.00 (−0.000) −1.887 (−0.333) −297.51 (−0.000) HINDPETRO −1.471 (−0.548) −305.75 (−0.000) −1.505 (−0.531) −189.26 (−0.000) PETRONET −1.436 (−0.565) −169.53 (−0.000) −1.450 (−0.558) −218.42 (−0.000) IGL −1.476 (−0.546) −296.19 (−0.000) −1.189 (−0.681) −186.67 (−0.000) POWERGRID −2.496 (0.116) −240.73 (−0.000) −420.76 (−0.000) −420.76 (0.000) IOC −1.903 (−0.330) −252.62 (−0.000) −1.840 (−0.361) −251.08 (−0.000) TATAPOWER −1.683 (−0.389) −435.00 (−0.000) −1.797 (−0.333) −298.51 (−0.000) MGL −2.843 (−0.052) −264.13 (−0.000) −2.696 (−0.074) −264.04 (−0.000) TORNTPOWER −1.803 (−0.320) −242.62 (−0.000) −1.740 (−0.351) −241.08 (−0.000) Note: ( ) denote P value Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021 233 of the Residuals from the model confirms the presence of ARCH effect. The absence of the ARCH effect hypothesis is false in the closing return series of all the variables. Following the detection of structural breaks in the return series of 14 Energy Sector stocks, an attempt has been made to relate these dates to the launch of Derivatives trading on the individual stocks as shown in Figure 1. After incorporating the detected structural breaks into the AR (1)-GARCH (1, 1) Model, detailed analysis is presented in the appendix. If a structural break is observed within 6 months following the introduction of Derivative trading, it has been attributed as possible to Derivative trading. Following this structural break date, the change in volatility persistence, the unconditional volatility and the rate of adjustment of the volatility to the new information are observed and reported in Table 3. In the case of BPCL, GAIL, and HINDPETRO, the Persistence of the volatility have increased; while, the adjustment coefficient and unconditional volatility declined for the period after this break. On the contrary, IOC, NTPC, and OIL demonstrated a decline in the Persistence of volatility, unconditional volatility, and rate of volatility adjustment to new information. We noticed a rise in the adjustment coefficient, Persistence of volatility and the unconditional volatility of ONGC and PETRONET for the period following the introduction of Derivative Trading. For MGL and TATAPOWER, the adjustment coefficient and unconditional volatility are reduced. Still, the persistence rate of adjustment volatility has increased during the observed volatility structural break. However, no structural break is found in proximity to the introduction of Derivatives trading for ADANIPOWER, IGL and POWERGRID. The results of this study show a mixed picture. Out of the fourteen stocks, no structural break has been observed in three stocks within the 6 months following Derivative Trading’s introduction. Out of the remaining eleven stocks, which show a structural break during the vicinity of Derivative trading, the unconditional volatility of Eight Stocks declined. The study’s findings show that, following the Futures contracts’ implementation, the unconditional volatility of most stocks declined. Volatility persistence increased in four stocks and decreased in seven stocks. The rate of adjustment of volatility to new information increased in five stocks, while it decreased in six stocks. Table 2: Results of ARCH test Stock P-value Result Stock P-value Result ADANIPOWER 0.000 Present NTPC 0.000 Present BPCL 0.000 Present OIL 0.000 Present GAIL 0.000 Present ONGC 0.000 Present HINDPETRO 0.000 Present PETRONET 0.000 Present IGL 0.000 Present POWERGRID 0.000 Present IOC 0.000 Present TATAPOWER 0.000 Present MGL 0.000 Present TORNTPOWER 0.000 Present Table 3: Impact of derivatives trading on volatility of underlying stock Stock Impact on the volatility This structural break caused by derivative trading Direction of impact Persistence α Unconditional volatility ADANIPOWER No - - - BPCL Yes Decreased Increased Decreased GAIL Yes Decreased Increased Decreased HINDPETRO Yes Decreased Increased Decreased IGL No - - - IOC Yes Decreased Decreased Decreased MGL Yes Increased Decreased Decreased NTPC Yes Decreased Decreased Decreased OIL Yes Decreased Decreased Decreased ONGC Yes Increased Increased Increased PETRONET Yes Increased Increased Increased POWERGRID No - - - TATAPOWER Yes Increased Decreased Decreased TORNTPOWER Yes Decreased Decreased Increased Total=14 Yes=11 No=03 Increased=04 Decreased=07 Increased=05 Decreased=06 Increased=03 Decreased=08 Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021234 5. CONCLUSION In this analysis, an attempt was made to model with Stock Futures the volatility of the underlying Energy Sector stocks by considering the breaks in volatility. We used the Iterated Cumulative Sums of Squares (ICSS) algorithm to detect multiple structural breaks for 14 Energy Sector stocks. The results of this study show a mixed picture. Out of the fourteen stocks, no structural break has been observed in three  Figure 1: Multiple structural breaks (iterated cumulative sums of squares [ICSS]) algorithm of (Inclan and Tiao, 1994) stocks within the 6 months following Derivative Trading’s introduction. Out of the remaining eleven stocks, which show a structural break within the 6 months of Derivative trading, Eight Stocks’ unconditional volatility declined. The study’s findings show that, following the Futures contracts’ implementation, the unconditional volatility of most stocks declined. Volatility persistence increased in four stocks and decreased in seven stocks. 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Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021236 Volatility Breaks in ADANIPOWER Date of commencement of Derivative trading: 30-July-2010 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_16 November 2001 3.256 0.310 0.540 0.850 21.713 17 November 2001_01 January 2003 0.142 0.266 0.784 1.051 −2.803 02 January 2003_18 November 2004 0.172 0.096 0.888 0.984 10.853 19 November 2004_04 May 2006 3.323 0.085 0.411 0.497 6.601 05 May 2006_18 January 2008 2.728 0.259 0.453 0.712 9.478 19 January 2008_18 August 2009 2.281 0.079 0.815 0.894 21.560 19 August 2009_07 June 2012 1.175 0.146 0.558 0.704 3.962 08 June 2012_20 November 2014 0.056 0.039 0.940 0.979 2.657 21 November 2014_24 September 2015 0.840 0.032 0.703 0.735 3.169 25 September 2015_31 January 2017 1.287 −0.019 0.264 0.245 1.705 01 February 2017_29 March 2019 1.037 0.276 0.123 0.400 1.726 Volatility Breaks in BPCL Date of commencement of Derivative trading: 02-July-2001 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_04 October 2000 5.439 0.159 0.458 0.617 14.200 05 October 2000_17 September 2001 0.006 −0.021 1.017 0.996 1.761 18 September 2001_16 July 2004 0.793 0.060 0.815 0.875 6.353 17 July 2004_12 September 2005 0.480 0.033 0.692 0.725 1.749 13 September 2005_13 March 2007 0.331 0.224 0.736 0.960 8.348 14 March 2007_21 January 2008 0.720 0.038 0.748 0.786 3.368 22 January 2008_06 October 2009 1.128 0.096 0.776 0.872 8.822 07 October 2009_03 July 2012 1.592 0.241 0.100 0.341 2.415 04 July 2012_25 July 2013 1.019 0.167 0.085 0.252 1.362 26 July 2013_10 March 2015 0.166 0.087 0.861 0.947 3.148 11 March 2015_05 August 2016 0.354 0.104 0.623 0.728 1.299 06 August 2016_29 March 2019 0.513 0.022 0.807 0.829 3.004 Volatility Breaks in GAIL Date of commencement of Derivative trading: 26-September-2003 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_05 January 2001 1.467 0.188 0.651 0.839 9.129 06 January 2001_09 October 2003 0.336 0.187 0.744 0.931 4.841 10 October 2003_11 May 2004 0.968 −0.108 0.862 0.754 3.933 12 May 2004_18 May 2006 0.416 0.081 0.799 0.881 3.488 19 May 2006_27 June 2008 0.160 0.056 0.921 0.976 6.773 28 June 2008_22 December 2011 0.050 0.055 0.934 0.990 4.850 23 December 2011_06 August 2013 0.904 0.023 0.553 0.576 2.133 07 August 2013_06 October 2015 0.178 0.054 0.890 0.944 3.172 07 October 2015_29 March 2019 0.216 0.052 0.833 0.885 1.872 Volatility Breaks in HINDPETRO Date of commencement of Derivative trading: 02-July-2001 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_19 July 2000 13.355 0.229 0.021 0.249 17.791 20 July 2000_23 October 2001 1.187 0.049 0.772 0.820 6.605 24 October 2001_28 April 2003 0.779 0.046 0.466 0.513 1.599 29 April 2003_06 July 2004 1.756 0.187 0.476 0.663 5.214 07 July 2004_02 February 2006 1.546 0.100 0.384 0.484 2.994 03 February 2006_18 August 2009 0.745 0.135 0.729 0.864 5.466 19 August 2009_15 August 2014 0.946 0.014 0.549 0.562 2.162 16 August 2014_03 September 2015 0.217 0.011 0.930 0.941 3.664 04 September 2015_28 December 2016 1.343 0.252 0.138 0.390 2.201 29 December 2016_23 May 2017 0.210 0.197 0.547 0.744 0.818 24 May 2017_29 March 2019 0.530 0.144 0.646 0.790 2.527 Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021 237 Volatility Breaks in MGL Date of commencement of Derivative trading: 28-April-2017 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 12 November 2015_22 January 2016 11.610 0.304 −0.109 0.196 14.436 23 January 2016_16 February 2016 10.775 −0.123 0.663 0.540 23.432 17 February 2016_19 August 2016 2.533 −0.050 0.600 0.550 5.632 20 August 2016_29 April 2017 2.401 0.212 −0.098 0.114 2.711 30 April 2017_29 March 2019 0.977 0.024 0.828 0.852 6.613 Volatility Breaks in NTPC Date of commencement of Derivative trading: 23-August-2004 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 27 January 2004_26 April 2004 0.149 0.047 0.919 0.966 4.339 27 April 2004_15 October 2005 0.634 0.012 0.608 0.619 1.666 16 October 2005_25 July 2006 0.463 0.169 0.709 0.878 3.796 26 July 2006_06 July 2007 1.290 0.342 0.159 0.501 2.588 07 July 2007_29 October 2008 0.333 0.116 0.856 0.971 11.669 30 October 2008_13 August 2009 11.142 0.241 −0.171 0.070 11.982 14 August 2009_05 August 2011 1.133 0.134 0.460 0.594 2.794 06 August 2011_10 May 2012 0.168 0.041 0.921 0.961 4.366 11 May 2012_26 June 2013 0.021 −0.041 1.030 0.988 1.823 27 June 2013_20 October 2014 0.808 0.027 0.692 0.719 2.873 21 October 2014_29 December 2017 1.048 0.151 0.232 0.383 1.699 30 December 2017_29 March 2019 0.363 0.047 0.799 0.846 2.353 Volatility Breaks in IGL Date of commencement of Derivative trading: 30-September-2010 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 26 July 2013_19 September 2013 10.009 0.113 −0.079 0.034 10.358 20 September 2013_02 June 2014 1.584 0.032 0.771 0.803 8.039 03 June 2014_22 March 2016 1.929 0.037 0.589 0.626 5.158 23 March 2016_01 November 2018 0.271 0.086 0.855 0.942 4.641 02 November 2018_29 March 2019 3.118 −0.063 0.716 0.652 8.969 Volatility Breaks in IOC Date of commencement of Derivative trading: 26-September-2005 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_27 February 2001 0.710 0.100 0.852 0.951 14.612 28 February 2001_03 November 2001 6.251 −0.196 1.046 0.850 41.605 05 November 2001_17 May 2004 1.033 0.108 0.778 0.886 9.097 18 May 2004_28 February 2006 0.447 0.005 0.810 0.814 2.408 29 February 2006_24 July 2006 5.691 0.336 −0.126 0.210 7.206 25 July 2006_01 May 2009 0.053 0.061 0.931 0.992 6.624 02 May 2009_12 July 2012 0.628 0.230 0.598 0.828 3.650 13 July 2012_11 January 2013 0.360 0.030 0.737 0.767 1.545 12 January 2013_13 March 2014 0.560 1.277 0.205 1.482 −1.163 14 March 2014_18 July 2016 0.850 −0.018 0.699 0.681 2.661 19 July 2016_ 3/29/2019 1.292 0.170 0.137 0.307 1.864 Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021238 Volatility Breaks in ONGC Date of commencement of Derivative trading: 31-January-2003 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_15 March 2001 0.263 0.071 0.893 0.964 7.290 16 March 2001_25 April 2003 0.427 0.266 0.707 0.973 15.935 26 April 2003_27 April 2004 0.073 0.082 0.900 0.981 3.916 28 April 2004_26 July 2005 0.149 0.047 0.919 0.966 4.339 27 July 2005_15 May 2006 0.767 0.074 0.639 0.713 2.671 16 May 2006_08 October 2007 0.305 0.015 0.919 0.935 4.669 09 October 2007_31 July 2009 0.569 0.079 0.875 0.954 12.340 01 August 2009_01 August 2011 0.271 0.060 0.861 0.921 3.418 02 August 2011_24 October 2017 0.215 0.071 0.874 0.946 3.953 25 October 2017_08 June 2018 0.484 −0.111 0.976 0.865 3.582 09 June 2018_29 March 2019 0.179 0.081 0.869 0.950 3.598 Volatility Breaks in OIL Date of commencement of Derivative trading: 29-October-10 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_15 March 2001 2.278 0.189 0.655 0.844 14.638 16 March 2001_06 February 2002 2.859 0.356 0.041 0.397 4.743 07 February 2002_05 May 2003 0.372 0.081 0.855 0.937 5.862 06 May 2003_07 December 2006 1.365 0.118 0.754 0.872 10.630 08 December 2006_09 March 2007 0.969 −0.211 1.177 0.966 28.330 10 March 2007_22 July 2009 0.736 0.094 0.872 0.966 21.552 23 July 2009_02 November 2010 3.850 0.260 0.223 0.483 7.450 03 November 2010_02 April 2012 5.351 0.184 −0.181 0.002 5.364 03 April 2012_20 June 2014 0.049 0.057 0.933 0.989 4.644 21 June 2014_16 November 2016 0.362 0.034 0.808 0.842 2.292 16 November 2016_29 March 2019 0.127 0.101 0.833 0.935 1.957 Volatility Breaks in PETRONET Date of commencement of Derivative trading: 14-May-2007 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 13 March 2007_10 April 2007 1.942 −0.050 0.589 0.540 4.219 11 April 2007_15 October 2009 0.982 0.093 0.831 0.923 12.803 16 October 2009_06 August 2010 0.384 0.004 0.935 0.940 6.357 07 August 2010_04 June 2013 3.145 0.198 0.007 0.205 3.958 05 June 2013_12 January 2017 3.275 0.150 0.600 0.750 13.101 13 January 2017_29 March 2019 7.650 0.306 −0.082 0.224 9.861 Volatility Breaks in POWERGRID Date of commencement of Derivative trading: 05-October-2007 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 October 2007_29 October 2008 0.333 0.116 0.856 0.971 11.669 30 October 2008_13 August 2009 11.142 0.241 −0.171 0.070 11.982 14 August 2009_05 August 2011 1.133 0.134 0.460 0.594 2.794 06 August 2011_10 May 2012 0.168 0.041 0.921 0.961 4.366 11 May 2012_26 June 2013 0.021 −0.041 1.030 0.988 1.823 27 June 2013_20 October 2014 0.808 0.027 0.692 0.719 2.873 21 October 2014_29 December 2017 1.048 0.151 0.232 0.383 1.699 30 December 2017_29 March 2019 0.363 0.047 0.799 0.846 2.353 Shirodkar and Raju: Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector International Journal of Energy Economics and Policy | Vol 11 • Issue 4 • 2021 239 Volatility Breaks in TATAPOWER Date of commencement of Derivative trading: 02-July-2001 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 05 January 2000_05 February 2001 0.602 0.241 0.724 0.965 17.222 06 February 2001_16 October 2001 1.111 0.386 0.474 0.860 7.939 17 October 2001_22 May 2003 0.501 0.306 0.484 0.791 2.393 23 May 2003_14 May 2004 1.487 0.145 0.448 0.593 3.656 15 May 2004_30 March 2006 0.548 0.028 0.770 0.798 2.712 31 March 2006_28 November 2008 0.352 0.103 0.855 0.958 8.337 29 November 2008_08 November 2010 0.036 0.045 0.941 0.985 2.477 09 November 2010_04 January 2012 3.047 −0.064 0.009 −0.055 2.889 05 January 2012_03 June 2014 0.032 0.039 0.948 0.986 2.355 04 June 2014_07 October 2015 0.598 0.024 0.521 0.545 1.314 08 October 2015_29 March 2019 0.407 0.057 0.461 0.517 0.843 Volatility Breaks in TORNTPOWER Date of commencement of Derivative trading: 30 December 2015 ω α β Total Persistence: (α+β) Unconditional volatility: ω/(1−α−β) 28 December 2012_07 June 2013 1.175 0.146 0.558 0.704 3.962 08 June 2013_20 November 2014 0.056 0.039 0.940 0.979 2.657 21 November 2014_24 January 2016 0.840 0.032 0.703 0.735 3.169 25 January 2016_31 January 2017 1.287 −0.019 0.264 0.245 1.705 01 February 2017_29 March 2019 1.037 0.276 0.123 0.400 1.726