The relationship between trading volume and exchange rate volatility: linear or nonlinear?

2019 ◽  
Vol 15 (1) ◽  
pp. 19-38
Author(s):  
Satish Kumar
Keyword(s):  
Granger Causality ◽  
Trading Volume ◽  
Causal Relation ◽  
Futures Market ◽  
Content Type ◽  
Currency Futures ◽  
Information Hypothesis ◽  
Hedging Strategies ◽  
Linear And Nonlinear ◽  
Mixture Of Distribution Hypothesis

PurposeThe purpose of this paper is to examine the linear and nonlinear relations between returns volatility and trading volume for the Indian currency futures market.Design/methodology/approachTo examine the contemporaneous relation between returns volatility and volume, the author uses the generalized method of moment estimator. For the linear causal relation, the author makes use of Granger (1969) bivariate vector autoregression model. The author tests for nonlinear Granger causality between returns volatility and trading volume based on a modified version of the Baek and Brock (1992) nonparametric technique developed by Hiemstra and Jones (1994).FindingsThe results indicate a negative contemporaneous relation between returns volatility and trading volume; therefore, the mixture of distribution hypothesis is not supported. The results of both linear and nonlinear Granger causality between futures returns volatility and trading volume indicate a significant bidirectional relation between the two variables lending support to the sequential arrival of information hypothesis. The results are robust to divergence of opinions as proxied by open interest.Practical implicationsThe findings of this paper are important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant causal relation between returns volatility and trading volume implies that trading volume helps predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Furthermore, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market.Originality/valueTo the best of the author’s knowledge, there is no study that investigates the forecast ability of trading volume to futures returns volatility in an emerging currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price–volume relation in the Indian currency futures market.

Revisiting the price-volume relationship: a cross-currency evidence

2017 ◽  
Vol 13 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Satish Kumar
Keyword(s):  
Granger Causality ◽  
Trading Volume ◽  
Generalized Method Of Moments ◽  
Futures Market ◽  
Content Type ◽  
Currency Futures ◽  
Hedging Strategies ◽  
Volume Relationship ◽  
Volume Relation ◽  
Mixture Of Distribution Hypothesis

Purpose The purpose of this paper is to examine the contemporaneous and causal relationship between returns (volatility) and trading volume in the Indian currency futures market for selected currency pairs; USD-INR, EUR-INR, GBP-INR and JPY-INR, from August 2008 to December 2014. Design/methodology/approach The data for all the currency futures series has been taken from National Stock Exchange of India Limited which represents the daily settlement prices along with trading volume. The contemporaneous returns-volume relation is tested using the generalized method of moments, and Granger-causality framework impulse response function is used to test the predictive ability of returns (volatility) and volume for each other. Findings The author reports a positive contemporaneous relationship between futures returns and trading volume which persists even after controlling for heteroskedasticity providing support to mixture of distribution hypothesis. The results show a unidirectional Granger causality from futures returns to volume. However, there is a significant bidirectional Granger causality between returns volatility and volume lending support to sequential arrival of information hypothesis. Next, the results for cross-currencies show significant influence of US dollar on the volume and returns of all other currencies. Overall, the author suggests that the short- to medium-term movements in the currency markets are dominated by market microstructure and not by fundamentals. Practical implications The findings of this paper are very important for the participants in the market and regulators. The participants in the market require alternatives to diversify their risk. The significant relationship between futures returns (volatility) and trading volume implies that the current trading volume help predict the futures prices and should lead to creation of more reliable hedging strategies for investment purposes. Further, it may interest the regulators who need to decide upon the appropriateness of their policies in the currency futures market. Based on returns-volume relation, they need to set forth market restrictions such as daily price movement and position limits. Originality/value To the best of the knowledge, no study has yet investigated the forecast ability of trading volume to price changes and their volatility in the Indian currency futures market. Given that currency futures market is one of the largest markets in the world, and Indian rupee has seen wide fluctuations in the recent years, it seems exciting to explore the price-volume relationship in the Indian currency futures market.

Intraday analysis of regulation change in microstructure: evidence from an emerging market

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eyup Kadioglu
Keyword(s):  
Trading Volume ◽  
Emerging Market ◽  
Lunch Break ◽  
Content Type ◽  
Call Auction ◽  
Regulation Change ◽  
Intraday Volatility ◽  
Continuous Trading ◽  
The Impact ◽  
Mixture Of Distribution Hypothesis

PurposeThis study investigates the impact of simultaneously replacing both midday single-price call auction and lunch break with multi-price continuous trading on intraday volatility–volume patterns as well as the intraday volatility–volume nexus.Design/methodology/approachThe analysis utilises 150 m tick-by-tick transaction data related to 333 stocks traded on Borsa Istanbul Equity Market covering a period of 2 months prior to and following the change. In addition to graphic comparisons, the study uses difference in mean tests, panel-fixed generalized least squares (GLS), panel-random GLS and random-effects linear models with AR(1) disturbance regression estimations.FindingsThe results show that intraday volatility and trading volume form an inverse J-shape and are positively correlated. It is observed that the implementation of the regulation change decreased intraday volatility and increased trading volume. Additionally, the results indicate a negative volatility–liquidity and a positive volume–liquidity relationship, supporting the mixture of distribution hypothesis.Research limitations/implicationsEnhanced market efficiency provides greater opportunity for investment and risk management. Investors can benefit from the findings on the intraday volatility–volume nexus, which is an indicator of informed trading, and regulatory authorities can use volume to oversight volatility.Originality/valueThis very rare regulation change of the simultaneous replacement of the lunch break and midday call auction with continuous trading is investigated in the context of intraday volume and volatility. This study also expands upon some important findings on the volume–volatility nexus for the Turkish Stock Market.

Hedging and effectiveness of Indian currency futures market

2020 ◽  
Vol 14 (5) ◽  
pp. 581-597
Author(s):  
Varuna Kharbanda ◽  
Archana Singh
Keyword(s):  
Dynamic Model ◽  
Quantitative Methods ◽  
Accounting Standards ◽  
Futures Market ◽  
Least Square ◽  
Multivariate Garch ◽  
Content Type ◽  
Currency Futures ◽  
Hedging Effectiveness ◽  
Method Of Evaluation

Purpose The purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation. Design/methodology/approach The paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model. Findings The overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models. Practical implications The new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness. Originality/value Many previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

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 Modeling the determinants of dry bulk FFA trading volume from a cross-market perspective of spot and forward

2018 ◽  
Vol 3 (3) ◽  
pp. 256-275
Author(s):  
Shiyuan Zheng ◽  
Shun Chen
Keyword(s):  
Trading Volume ◽  
Model Specification ◽  
Positive Relation ◽  
Content Type ◽  
Hedging Strategies ◽  
Step Model ◽  
Covariance Term ◽  
Demand Volatility ◽  
Procurement Strategies ◽  
Market Perspective

Purpose This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective. Findings A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors. Originality/value The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

Overconfidence bias and stock market volatility in Ghana: testing the rationality of investors in the Covid-19 era

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Raphael Kuranchie-Pong ◽  
Joseph Ato Forson
Keyword(s):  
Stock Market ◽  
Granger Causality ◽  
Private Information ◽  
Trading Volume ◽  
Variance Estimation ◽  
Public Information ◽  
Investment Decisions ◽  
Stock Market Volatility ◽  
Content Type ◽  
Overconfidence Bias

PurposeThe paper tests the overconfidence bias and volatility on the Ghana Stock Exchange (GSE) during the pre-Covid-19 pandemic and Covid-19 pandemic period.Design/methodology/approachThe study employs pairwise Granger causality to test the presence of overconfidence bias on the Ghana stock market as well as GARCH (1,1) and GJR-GARCH (1, 1) models to understand whether overconfidence bias contributed to volatility during pre-Covid-19 pandemic and Covid-19 pandemic period. The pre-Covid-19 pandemic period spans from January, 2019 to December, 2019, and Covid-19 pandemic period spans from January, 2020 to December, 2020.FindingsThe paper finds a unidirectional Granger causality running from weekly market returns to weekly trading volume during the Covid-19 pandemic period. These results indicate the presence of overconfidence bias on the Ghana stock market during the Covid-19 pandemic period. Finally, the conditional variance estimation results showed that excessive trading of overconfident market players significantly contributes to the weekly volatility observed during the Covid-19 pandemic period.Research limitations/implicationsThe empirical findings demonstrate that market participants on the GSE exhibit conditional irrationality in their investment decisions during the Covid-19 pandemic period. This implies investors overreact to private information and underreact to available public information and as a result become overconfident in their investment decisions.Practical implicationsFindings from this paper show that there is evidence of overconfidence bias among market players on the GSE. Therefore, investors, financial advisors and other market players should be educated on overconfidence bias and its negative effect on their investment decisions so as to minimize it, especially during the pandemic period.Originality/valueThis study is a maiden one that underscores investors’ overconfidence bias in the wake of a pandemic in the Ghanaian stock market. It is a precursor to the overconfidence bias discourse and encourages the testing of other behavioral biases aside what is understudied during the Covid-19 pandemic period in Ghana.

Effectiveness of Currency Futures Market in India: An Empirical Investigation

2017 ◽  
Vol 6 (2) ◽  
pp. 196-203 ◽  
Author(s):  
T. K. Dhaneesh Kumar ◽  
B. G. Poornima ◽  
P. K. Sudarsan
Keyword(s):  
Granger Causality ◽  
Garch Model ◽  
Granger Causality Test ◽  
Futures Market ◽  
Marginal Effect ◽  
Model Framework ◽  
Causality Test ◽  
Currency Futures ◽  
Before And After ◽  
Spot Volatility

This article investigates the role of currency futures market in India in the context of high volatility of Indian rupee (INR) in recent years. It examines whether the spot volatility before and after the introduction of currency futures were significantly different. It also examines the volatility causation between currency spot and futures market in India. The study considers three international currencies, namely US dollar (USD), British pound (GBP) and Euro in relation to INR for the period of 2006–2013. It made use of GARCH model framework and Granger causality test. The GARCH model results indicate that after the introduction of futures, there is less volatility for GBP and Euro but not in the case of USD. The Granger causality test reveals that USD and Euro has unidirectional causality, which means that spot causes future fluctuations, while in the case of GBP, there is bidirectional causality. The study concludes that the introduction of futures is not effective in reducing spot volatility for INR–USD but there is a marginal effect for INR–GBP and INR–Euro.

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.

Price discovery and volatility spillover: an empirical evidence from spot and futures agricultural commodity markets in India

2020 ◽  
Vol 10 (4) ◽  
pp. 447-473 ◽  
Author(s):  
Manogna R L ◽  
Aswini Kumar Mishra
Keyword(s):  
Granger Causality ◽  
Futures Markets ◽  
Price Discovery ◽  
Spillover Effects ◽  
Futures Market ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Agricultural Commodity ◽  
Content Type ◽  
Spot Prices

PurposePrice discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.Design/methodology/approachThis study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).FindingsThe VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.Research limitations/implicationsThese results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.Practical implicationsThe findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.Originality/valueThere are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

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