Impact of futures trading on volatility of spot market-a case of guar seed

2015 ◽  
Vol 75 (3) ◽  
pp. 416-431 ◽  
Author(s):  
Dinesh Kumar Sharma ◽  
Meenakshi Malhotra
Keyword(s):  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Spot Price ◽  
Futures Trading ◽  
Market Prices ◽  
Content Type ◽  
Commodity Derivatives ◽  
Spot Volatility ◽  
Guar Seed

Purpose – Guar Seed crop is ruling the Indian International business mainly due to its application as a drilling fluid in shale energy industry concentrated in the USA. One of the allegations against futures market is its possible role in increasing the volatility of underlying physical market prices. Suspension of guar seed futures contract in 2012 at National Commodity Derivatives Exchange of India (NCDEX)-India, has reignited the controversy and raised an alarm bell to peek into obscure world of Indian commodity derivatives market. Against the backdrop of fiasco in guar futures trading, the purpose of this paper is to investigate whether sudden surge in futures trading volume leads to increase in the volatility of spot market prices. Design/methodology/approach – Guar seed spot returns volatility is modeled as a GARCH (1, 1) process. Futures trading volume and open interest are segregated into expected and unexpected components. The data are analyzed from 2004 to 2011 using Augmented GARCH model to study the contemporaneous relationship between spot volatility and unexpected futures trading activity and Granger Causality test for examining the dynamic relationship between them and ascertaining causality. Findings – Augmented GARCH model reports positive relationship between unexpected futures trading volume (UTV) and spot returns volatility, and, Granger Causality flows from UTV to spot volatility. Therefore, when the level of futures trading volume increases unexpectedly, the volatility of spot prices increases pointing toward the destabilizing impact of futures trading. However, hedger’s activity, represented by open interest is not seen to have any causal/destabilizing impact on spot price volatility of guar seed. Practical implications – The study provides empirical evidence to support the concern of regulators, genuine hedgers and other traders about the presence of excessive speculation and market manipulations perpetrated through futures market that is disturbing the underlying physical market instead of strengthening it by aiding in price discovery and risk mitigation. Originality/value – There are very few studies which have empirically investigated the temporal relation between volume and volatility in Indian agricultural commodity markets. With guar seed as a special case the present study investigates statistically the impact of futures trading on spot price volatility. In light of the findings of the study, the curb imposed on guar seed futures trading in 2012 was justified.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

scholarly journals Single Stock Futures Trading and Stock Price Volatility: Empirical Analysis

2009 ◽  
Vol 48 (4II) ◽  
pp. 553-563 ◽  
Author(s):  
Safi Ullah Khan ◽  
Syed Tahir Hijzi
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Contracts ◽  
Sufficient Evidence ◽  
Spot Price ◽  
Return Volatility ◽  
Futures Trading ◽  
Underlying Stocks ◽  
Single Stock Futures

This study examines impact of the introduction of single stock futures contracts on the return volatility of the SSFs-listed underlying stocks. The study documents a significant decrease in return volatility for the SSFs-underlying stocks following the introduction of single stock futures contracts on the Karachi Stock Exchange. The multivariate analysis in which the spot trading volume, the futures trading volume and open interest were partitioned into news and informationless components, the estimated coefficient of expected futures volume component is statistically significant and negatively related to volatility, suggesting that equity volatility is mitigated when the expected level of futures activity is high. The findings of the decreased spot price volatility of the SSFs-underlying stocks associated with large expected futures activity is important to the debate of regarding the role of equity derivatives trading in stock market volatility. These empirical results for the Pakistan’s equity market support theories implying that equity derivates trading improves liquidity provision and depth in the equity markets, and appear to be in contrast to the theories implying that equity derivates markets provide a medium for destabilising speculation. Finally, the SSFs-listed stocks were grouped with a sample of non-SSFs stocks to examine cross-sectional data for comparing changes in return volatility. After controlling for the effects of a number of determinants of volatility, sufficient evidence is found to support that, this multivariate test, like the previous analysis, provides no evidence that the volatility of the SSFsunderlying stocks is positively related to the introduction of the single stock futures trading in the Pakistan’s stock market.

scholarly journals Volatility Dynamics in Oil and Oilseeds Spot and Futures Market in India

2016 ◽  
Vol 41 (2) ◽  
pp. 132-148 ◽  
Author(s):  
Meenakshi Malhotra ◽  
Dinesh Kumar Sharma
Keyword(s):  
Futures Markets ◽  
Garch Model ◽  
Price Volatility ◽  
Spot Market ◽  
Futures Market ◽  
Spot Price ◽  
Volatility Spillover ◽  
Trading Activity ◽  
Futures Trading ◽  
The Impact

Executive Summary India occupies the fifth position in the vegetable oil economy of the world. The demand for oilseeds and vegetable oil has far exceeded the domestic output necessitating huge imports. Futures market helps to bring price stability for the development of the underlying physical market. The present study investigates the volatility dynamics in spot and futures markets of select oil and oilseeds commodities. The objectives of this article are to study (a) the information transmission process between spot and futures markets, also called volatility spillover and (b) the impact of futures trading activity on the volatility of physical market prices. The commodities selected from oil and oilseeds segment are refined soya oil, mustard seed, crude palm oil, and mentha oil. The study uses basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to capture volatility in prices of the selected commodities. Bivariate GARCH model makes use of information in the history of two different markets for testing volatility spillover between two markets of the same underlying commodity. The relationship between futures trading activity and spot price volatility is investigated for examining the impact of futures trading activity on the volatility of underlying spot market. Two variables, viz., futures trading volume and open interest are decomposed into expected and unexpected components and are taken as a proxy for the level of trading activity. The contemporaneous and dynamic relationships are studied with the help of augmented GARCH model and Granger causality, respectively. It is observed that there is an efficient transmission of information between spot and futures markets but it is the spot market which leads to the flow of information to futures and hence causes greater spillover of volatility. The spot market has a greater impact on the volatility of futures market, indicating that informational efficiency of oilseeds spot market is stronger than that of the futures market. The contemporaneous and dynamic relationship between spot price volatility and futures trading activity tested with econometric models provide evidence of the destabilizing impact of an unexpected increase in futures trading activity (volume or open interest) on the spot price volatility in three out of four commodities studied. This indicates that badly informed traders present in futures market are destabilizing the underlying spot market by inducing noise and lowering the information content of prices.

Crop price insurance in China: pricing and hedging using futures market

2017 ◽  
Vol 9 (4) ◽  
pp. 567-587 ◽  
Author(s):  
Minghua Ye ◽  
Rongming Wang ◽  
Guozhu Tuo ◽  
Tongjiang Wang
Keyword(s):  
Market Price ◽  
Futures Market ◽  
Insurance Premium ◽  
Spot Price ◽  
Agricultural Commodity ◽  
Content Type ◽  
Price Series ◽  
Insurance Firms ◽  
Price Insurance ◽  
S Model

Purpose The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market. Design/methodology/approach Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market. Findings The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks. Originality/value This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

Does Ban on Futures trading (de)stabilise spot volatility?

2019 ◽  
Vol 9 (2) ◽  
pp. 145-166 ◽  
Author(s):  
Neharika Sobti
Keyword(s):  
Granger Causality ◽  
Price Discovery ◽  
Futures Trading ◽  
Causality Test ◽  
Content Type ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
Spot Volatility ◽  
Non Parametric

Purpose The purpose of this paper is to ascertain the possible consequences of ban on futures trading of agriculture commodities in India by examining three critical issues: first, the author explores whether price discovery dominance changes between futures and spot in the pre-ban and post-relaunch phase both in the long run and short run. Second, the author examines the impact of ban and relaunch of futures trading on its underlying spot volatility for five sample cases of agriculture commodities (Wheat, Sugar, Soya Refined Oil, Rubber and Chana) using both parametric and non-parametric tests. Third, the author revisits the destabilization hypothesis in the light of ban on futures trading by examining the impact of unexpected component of liquidity of futures on spot volatility. Design/methodology/approach The author uses widely adopted methodology of co-integration to examine long-run relationship between spot and futures, while the short-run relationship is investigated using vector error correction model (VECM) and Granger causality to test price discovery in the pre-ban and post-relaunch phases. The second objective is explored using a combination of parametric and non-parametric tests such as Welch one-way ANOVA and Kruskal–Wallis test, respectively, to gauge the impact of ban on futures trading on spot volatility along with post hoc tests to investigate pairwise comparison of spot volatility among three phases (pre-ban, ban and post-relaunch) using Dunn Test. In addition, extensive robustness test is undertaken by adopting augmented E-GARCH model to ascertain the impact of ban and relaunch of futures trading on spot volatility. The third objective is investigated using Granger causality test between spot volatility and unexpected component of liquidity of futures estimated using Hodrick and Prescott (HP) filter to re-visit the destabilization hypothesis. Findings The author found extensive evidence for the dominance of futures market in the price discovery of agriculture commodities both in the pre-ban and post-relaunch phases in India. The ban on futures trading is found to have a destabilizing impact on spot volatility as evident from the findings of Wheat, Sugar and Rubber. In addition, it is observed that spot volatility was highest during the ban phase as compared to the pre-ban and post-relaunch phases for all four commodities barring Chana. The author found that destabilisation hypothesis holds true during the pre ban phase, while weakening of destabilization hypothesis is observed in the post-relaunch phase as unexpected futures liquidity has no role in driving the spot volatility. Originality/value This study is a novel attempt to empirically examine the potential impact of ban and relaunch of futures trading of agriculture commodities on two key market quality dimensions – price discovery and spot volatility. In addition, destabilization hypothesis is revisited to investigate the impact of futures trading on spot volatility during the pre-ban and post-relaunch period.

Returns and volume

2020 ◽  
Vol 37 (3) ◽  
pp. 457-473
Author(s):  
Panos Fousekis
Keyword(s):  
Trading Volume ◽  
Time Series Data ◽  
Price Volatility ◽  
Statistical Testing ◽  
Model Specification ◽  
Series Data ◽  
Causal Order ◽  
Content Type ◽  
Daily Data ◽  
The Usa

Purpose The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA. Design/methodology/approach Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing. Findings The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works. Originality/value The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

Monotonicity, linearity and symmetry in the price volatility–volume relationship

2020 ◽  
Vol 37 (1) ◽  
pp. 110-133 ◽  
Author(s):  
Panos Fousekis ◽  
Dimitra Tzaferi
Keyword(s):  
Trading Volume ◽  
Futures Markets ◽  
Statistical Tests ◽  
Price Volatility ◽  
Local Linear Regression ◽  
Linear Regression Models ◽  
Content Type ◽  
Nonparametric Approach ◽  
Volume Relationship ◽  
The Usa

Purpose This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy. Design/methodology/approach Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA. Findings Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally. Originality/value To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

Empirical Investigation on Food Inflation and Efficiency Issues in Indian Agri-futures Market

2017 ◽  
Vol 3 (2) ◽  
pp. 156-165 ◽  
Author(s):  
Narinder Pal Singh ◽  
Archana Singh
Keyword(s):  
Trading Volume ◽  
Wholesale Price ◽  
Time Frame ◽  
Futures Market ◽  
Sufficient Evidence ◽  
Futures Trading ◽  
Causality Test ◽  
Short Period ◽  
The Government ◽  
Food Inflation

In early 2007, the Government of India (GoI) banned futures trading on some essential agro-commodities such as wheat, rice, and two varieties of lentils due to rising food inflation. However, futures trading in agri-commodities such as chana (chickpea), soy oil, rubber, and potato were temporarily suspended. Professor Abhijit Sen’s committee, constituted to study the relationship between futures trading and agricultural commodities inflation, did not find sufficient evidence of inflationary impact of futures trading in India due to too short period of commodity futures trading. Also, an efficient futures market is required for the producers, traders, and consumers to hedge their price risk. Thus, in this study, we analyze the market efficiency of agricultural futures market and the effect of futures trading on inflation with special reference to chana (chickpea) market in India. This study is for a time frame of 10 years from 2005–2014. The data on closing prices of chana in futures and spot markets and futures trading volume has been collected from National Commodity and Derivatives Exchange, and chana wholesale price index (WPI) monthly data from Office of the Economic Adviser, GoI. The collected data is analyzed for efficiency using Johansen cointegration approach and vector error correction (VEC) restrictions and inflationary effect using Toda Yamamoto (TY) version of Granger causality test. From the results, we find that the spot and futures prices for chana are cointegrated and unbiased, that is, the chana (chickpea) futures market is efficient. But, the futures trading of chana has inflationary impact, that is, futures trading volume of chana affects chana WPI. This research has got direct implications for government and market participants. India is the largest consumer of chana (chickpea)—the third most important pulse crop produced in the world. Thus, the inflationary impact of chana futures trading is a matter of concern for GoI.

The impact of order imbalance on returns, liquidity, and volatility in agricultural commodity markets

2018 ◽  
Vol 78 (5) ◽  
pp. 571-591 ◽  
Author(s):  
Steffen Volkenand ◽  
Guenther Filler ◽  
Martin Odening
Keyword(s):  
Trading Volume ◽  
High Speed ◽  
Price Volatility ◽  
Agricultural Commodity ◽  
Content Type ◽  
Order Imbalance ◽  
New Information ◽  
Agricultural Futures ◽  
Best Offer ◽  
The Impact

PurposeThe purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.Design/methodology/approachUsing Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.FindingsResults confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.Practical implicationsFor some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.Originality/valueThis paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.

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