Futures Trading, Spot Price Volatility and Structural Breaks: Evidence from Energy Sector

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.


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. 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." Wang and Moore, 2009)

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 andHolmes (1995), andYao (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); ; 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.

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).

Testing for ARCH Effect
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: In this R 2 is the R-squared from the Regression of ε t 2 in the equation and defined by: Under the null hypothesis, the test statistics NR 2 is distributed as a Chi-squared distribution with q degrees of freedom. H 0 is rejected when LM > 2 ( ) q α χ suggests that the ARCH effect exists in the time-series.

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.

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: 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.

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. 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.  Increased=04 Decreased=07 Increased=05 Decreased=06 Increased=03 Decreased=08

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  (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. The rate of adjustment of volatility to new information increased in five stocks, while it decreased in six stocks. The mixed result may probably be attributed to different stock characteristics which could also play a significant role in volatility development. The study results indicate that Stock Futures trading may not inherently be correlated with the underlying stock destabilization.