An empirical analysis of the impact of futures on spot market volatility: evidence from National Stock Exchange (NSE), India.
This article examines empirically the impact of futures on spot market volatility in India. A generalised auto regression conditional hetroscedasticity (GARCH) model is selected to measure the spot return volatility in the present stud),. The study also employed the vector autoregression (VAR) model to investigate the relationship between spot return volatility and futures market. The daily data from 12th, June, 2000 through 28th, December, 2006 has been considered for the analysis which has been retrieved from National Stock Exchange (NSE). The results indicate that the volatility in the spot market has been declined after the introduction of futures market.
Since the introduction of derivatives market in India almost six years ago, the appreciable spread of derivatives trading activity makes a great interest of academic research on the impact of derivatives trading on the underlying market. The trading in futures markets commenced from 12, June, 2000, which is an important instruments of derivatives. It provides the function of price discovery to help market efficiency and also transfers risk through hedging. The introduction of futures market makes a significant influence on spot market. The movements of the prices of spot market have been hugely influenced by the speculation, hedging and arbitrage activity of futures markets. Therefore, research on the relationship between futures trading and spot market volatility has been important issues to generate for academicians, regulators and investors alike.
From a theoretical stand of view, the impact of futures trading activity on the volatility of the underlying market provides quite mixed evidence. One view is that derivatives securities increase volatility in the spot market due to more highly leveraged and speculative participants in the futures market. Conversely, the derivatives markets reduce spot market volatility by providing low cost contingent strategies and enabling investors to minimise portfolio risk by transferring speculation from spot markets to futures markets. The low margin, low transaction costs and the stabilised contracts and trading conditions attract risk taking speculators to futures. Hence, futures are expected to have stabilising influence as it attracts more informed traders to the spot market and making it more liquid. Hence, it is less volatile. Cox (1976) defines that the transaction costs in the derivatives market are lower than those in the spot market; new information may be transmitted to the spot market more quickly.
After the theoretical discussion, let us examine the earlier literature pertaining to the study areas which will be immensely useful to identify the gaps of the study. The study by Edward (1988), Harris (1989), Antoniou and Holms (1995), Kyriacou and Sarno (1999), Gulen and Mayhen (2000) and Vipul (2006) supported that the volatility of spot market has decreased after the introduction of futures trading. Besides, the study concluded that due to the higher degree of leverage, futures markets tend to attract uniformed speculative investors and thus destabilise cash markets by increasing volatility. Yu (2001) and IIIneca and Lafuente (2003) did not get any significant changes in the volatility on spot market and it is attributed to macroeconomic factors and structures of the markets.
However, James (1993), Perieli and Koutomos (1997), Tenmozhi (2001), Raju & Karande (2003) and Nath (2003), Bae, et al. (2004) found that the volatility of spot market has been declined after introduction of futures markets. It has been pointed out that futures markets increase the overall market depth and informativeness. These are important for price discovery, allow the transfer risk and it reduces spot volatility.
However, most of the studies mentioned the above were related to the international level. But its relevance to a developing economy like India is limited. At the national level, the introduction of S&P CNX Nifty Index futures market started from 2000. It is important to examine the spot market volatility after the introduction of futures market in India. Most of the studies have examined the futures market by comparing the unconditional variance of returns before and after the introduction of futures market. The present study investigates the relationship between spot market volatility and futures trading activities (FTA) such as: open interest and volume by considering post-futures period. Though, open interest and volumes are the important variables for futures market, it can give well clarification on impact on spot market volatility. For finding spot volatility, the study can employ GARCH techniques because GARCH is expected to explain sufficiently the time varying volatility of spot market. Also, vector autorgression (VAR) can be taken for this analysis which can investigate the relationship between spot volatility and FTA. By this context, it is worth mentioning that VAR model can better reveal the underlying process and that simultaneous equation model (SEM) can be misleading and may yield unreliable inferences (Chan and Chung, 1995).
On the above background, the present article investigates the spot market volatility after the introduction of futures market in India. The rest of article is as follows: After the brief introduction of the subject, Section-II presents the data and methodology of the study. Empirical results and discussions are presented in Section-III. Finally, concluding remarks are presented in Section IV.
All the required data information for the study has been retrieved from the National Stock Exchange (NSE) website. Daily closing value of the S&P CNX Nifty spot index and data on futures volume and open interest have been employed for the study. The data on futures are collected for near-month contracts as they are most heavily traded. The study has been considered daily data from 12th, June, 2000 through 28th, December, 2006. Returns are calculated as log of ratio of present day's price to previous day's price. The measure for futures trading activity is denoted by FTA. Therefore, the daily volume of futures is standardised by open interest. [FTA.sub.t] are constructed as follows:
[FTA.sub.t] = V[(FUT).sub.t] / OI[(FUT).sub.t] (1)
Where, V (FUT) and OI (FUT) denote daily volume and open interest for futures.
A generalised auto regression conditional heteroscedasticity (GARCH) model is selected as the most adequate measure of spot market volatility in the present study. Hence, a natural way to capture the time varying nature of volatility is to model the conditional variance as a GARCH process (Engle, 1982 and Bollerslev, 1986). A volatility proxy is constructed using the conditional variance of returns and [h.sub.t] retrieved from the maximum likelihood estimation of a GARCH (1, 1) of the form:
[R.sub.t] = [[beta].sub.0] + [[beta].sub.1] [R.sub.t-1] + [[epsilon].sub.t], [[epsilon].sub.t] | ([[epsilon].sub.t-1], [[epsilon].sub.t-2], ....) ~ N (o, [h.sub.t]) (2)
[h.sub.t] = ([[alpha].sub.o] + [[alpha].sub.1] [[epsilon].sup.2.sub.t-1] + [[alpha].sub.2] [h.sub.t-1] (3)
Where, equation (2) and (3) denote the conditional mean equation and the conditional variance equation respectively; [[alpha].sub.1] and [[alpha].sub.2] are nonnegative, and [[epsilon].sub.1] is an error term.
The dynamic relationship between the spot return volatility and futures trading activity (FTA) is examined in the framework of a vector auto-regression (VAR) models for volatility and futures trading. Before running the VAR model, it is necessary to test the stationary of the series.
The augmented Dickey-Fuller (1979) and Phillips-Perron (1988) test are employed to infer the stationary of the series. If the variables are stationary, it is not to proceed since standard time-series methods apply to stationary variables. All the variables included in the model should be stationary for the VAR estimation.
Then the study employed the vector autoregression (VAR) model to investigate the relationship between spot return volatility and FTAs. VAR model is the most appropriate model to examine the study in that all the variables are considered to be endogenous. However, each endogenous variable is explained by its lagged and the lagged values of all other endogenous variables included in the model. Usually, there are no exogenous variables in the model. Thus, by avoiding the imposition of priori restrictions on the model, the VAR adds significantly to flexibility of the model. In other words, a VAR system consists of a set of regression equations, each of which has an adjustment mechanism such that even small changes in one variable component in the system may be accounted automatically by possible adjustments in the rest of the variables. Furthermore, by incorporating the lagged terra of the variables, the VAR becomes useful in capturing the empirical regularities embedded in the data. Now the model can be written as:
[[sigma].sub.t] = [[alpha].sub.1] + [n.summation over (i=1)] [[beta].sub.11] [[sigma].sub.t-i] + [n.summation over (i=1)] [[beta].sub.12] FT [A.sub.t-i] + [[epsilon].sub.1t] (4)
[FTA.sub.t-] = [[alpha].sub.2] + [n.summation over (i=1)] [[beta].sub.21] [[sigma].sub.t-i] + [n.summation over (i=1)] [[beta].sub.22] FT [A.sub.t-i] + [[epsilon].sub.2t] (5)
where [sigma] denotes the spot market volatility measure employed; [[beta].sub.11], [[beta].sub.12], [[beta].sub.21], and [[beta].sub.22] are parameters; n is chosen on standard statistical grounds; and [[epsilon].sub.1t] and [[epsilon].sub.2t] are the stochastic error term.
The lagged values of the right-hand side variables in the equation (4) and (5) of VAR are estimated by ordinary least squares (OLS). It executes Granger (1969) causality tests by testing for zero restrictions on subsets of lagged parameters in each equation of the VAR in order to investigate the relationship between spot return and FTAs. The lag length of n is selected using the multi-variate generalizations of akaike information criteria (AIC) and schwarz's criteria (SC) due to fact that the results of the test are quite sensitive to the lag length.
III. EMPIRICAL RESULTS AND DISCUSSIONS
The volatility of Nifty spot returns are estimated by GARCH (1, 1) model, where volatility is modeled as a GARCH (1, 1) process. These are considered as statistically reliable and consistent. The advantage of a GARCH model is that it captures the tendency in financial data for volatility clustering. The GARCH (1, 1) estimated series of Nifty returns volatility have been used for further analysis.
The tests of stationary developed by Dickey and Fuller (1979), Phillips and Perron (1988) have been performed for the series. Before conducting the ADF and PP tests, the optimal lag number of each differenced series should be tested by using the Akaike's Information Criteria (AIC) and Schwarz Criteria (SC). According to AIC and SC, five lags for the ADF test, and seven lags for the PP test have been selected for spot return volatility and FTAs.
The unit root test was conducted for Nifty spot return volatility and futures trading activities (FTAs) for near month contracts separately for determining stationary. The estimates of the ADF and PP tests at the levels of the series are given in table (1) and it reveals that Nifty spot return volatility and futures trading activities (FTA) are stationary at their levels at one percentage. Therefore, the stationary of the series in level justify the use of VAR model.
The VAR estimation results are presented in table (2). The results reveal that the futures trading activities (FTAs) influence to the Nifty spot volatility returns. In the three and four lags of FTA, the coefficient is statistically significant at ten percent and five percent level simultaneously. The significantly negative estimated coefficients on the lag values of FTA suggest that greater futures trading in previous days reduce volatility of Nifty spot return. The high F-stat of the table signifies the overall significance of VAR model.
The paper examined the relationship between Nifty spot volatility and futures trading activity. It empirically evaluated the impact of introduction of futures trading on spot market volatility. The results of the empirical analysis provide strong evidence that spot market volatility is time-varying and well characterized by a GARCH process. The relationship between the spot market volatility and futures market are determined in a vector autoregression (VAR) models. The results indicate that the volatility in the spot market has been declined after the introduction of futures market which also supported the earlier study by Indian authors. The results concluded that futures market increase the over market depth, increased liquidity and informativeness. It also plays an important function of price discovery and allows transfer risk through hedging. Therefore, it generates to reduce spot volatility.
The result found that volatility has been reduced after the introduction of Index futures. The following implications may be suggested to further improve efficiency, liquidity and reduce volatility: (a) the more number of futures contracts on the stock indices can be introduced (b) more institutional participation is needed in the total turnover to enhance in derivatives participants and to improve the derivatives market and (c) right now institutional participation appear to be negligible in the total turnover, therefore, efforts should be made to enhance their role in derivations participation.
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KAILASH CHANDRA PRADHAN
National Council of Applied Economic Research (NCAER), New Delhi
K. SHAM BHAT
Department of Economics, Pondicherry University, Pondicherry
Table-1 Unit Root Test Constraint ADF PP Levels Panel-A: Futures Trading Activity (FTA) Intercept and trend -7.8298 * -15.6694 * Intercept -5.7542 * -11.5800 * Panel-B: Spot Market Volatility Intercept and trend -10.1335 * -9.7798 * Intercept -10.1356 * -9.7821 * Note: * Significant at one percent level. Table 2 Vector Autoregression (VAR) Models VAR estimation results (Volatility measure is GARCH) [sigma] FTA [[sigma].sub.t-1] 1.233099 * -577.2791 [49.7557] [-0.44385] [[sigma].sub.t-2] -0.433648 * 2352.105 [-11.0356] [1.14058] [[sigma].sub.t-3] 0.080898 ** -1563.335 [2.05828] [-0.75793] [[sigma].sub.t-4] -0.002452 472.487 [-0.09892] [0.36324] [FTA.sub.t-1] 2.71E-07 0.664910 * [0.58326] [27.2292] [FTA.sub.t-2] 1.51E-07 0.025870 [0.26879] [0.87848] [FTA.sub.t-3] -9.21E-07 *** 0.018829 [-1.64276] [0.64028] [FTA.sub.t-4] -9.19E-07 ** 0.182534 * [-1.97866] [7.48945] 3.84E-06 * 0.220337 * [4.43590] [4.85213] F-statistic 1064.904 * 482.5310* Note: t-Statistic in parenthesis * Null rejected at one percent level ** Null rejected at five percent level *** Null rejected at ten percent level
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|Author:||Pradhan, Kailash Chandra; Bhat, K. Sham|
|Publication:||Indian Journal of Economics and Business|
|Date:||Dec 1, 2008|
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