Modelling volatility in Indian currency market

Mittal, Sanjiv; Kumar, Ashish

doi:10.1504/ijbd.2016.075455

Cited by 5 publications

(4 citation statements)

References 54 publications

(89 reference statements)

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“…This is consistent with other studies' findings that TGARCH and PARCH are the best models for describing asymmetric volatility. (Banumathy & Azhagaiah, 2015), (Goudarzi & Ramanarayanan, 2010), (Mittal, Arora, & Goyal, 2012). The study also found that bad news (bad shocks) have a greater effect on the volatility of DFMGI returns than good news (positive shocks).…”

Section: Discussionmentioning

confidence: 70%

The Impact of Shocks on the Volatility of the Dubai General Market Index (Dfmgi), Using Asymmetric Garch Models

Khelifa,

Dekkiche,

Belgoum

2023

Economics and Finance

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This research paper aims to study the effects of good and bad news on the volatility of the Dubai General Market Index (DFMGI) return series for 5,5 years during the period spanning from January 1, 2018, to June 30, 2023, using the asymmetric GARCH models: EGARCH(1,1), TGARCH(1,1) and PARCH(1,1). The study concluded that positive and negative shocks asymmetric impact fluctuations in price returns in the Dubai financial market. This means that negative shocks significantly impact volatility more than positive shocks. The study also concluded that the best asymmetric GARCH model among the three used is the PARCH(1,1) model.

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Section: Discussionmentioning

confidence: 70%

The Impact of Shocks on the Volatility of the Dubai General Market Index (Dfmgi), Using Asymmetric Garch Models

Khelifa,

Dekkiche,

Belgoum

2023

Economics and Finance

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“…It was found that the futures market come out as the leading market (Kharbanda and Singh, 2017). In a study, a bidirectional causal association was also found between volume and returns (Mittal and Kumar, 2016). Similarly, in a study effect of currency derivatives on foreign exchange rate volatility of Pound sterling was examined and it was found that trading of currency futures in India has reduced the foreign exchange rate volatility.…”

Section: Currency Futuresmentioning

confidence: 96%

Impact of Currency Futures Issuance on Foreign Exchange Rate Volatility in India

Rani¹,

S²,

Narwal³

2022

OJC

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This article examines the effect of currency futures on foreign exchange rate volatility in India focusing on USD-INR, EUR-INR, GBP-INR and JPY-INR. For the period from February 2002 to February 2020, the daily exchange rate values of all the four currencies against the Indian rupee (INR) were collected. The Augmented Dicky Fuller (ADF) test of the unit root was performed to check the stationarity of the time series data utilized in the study. After verifying the presence of heteroskedasticity with the ARCH LM test, GARCH (1, 1) modelling is employed to assess the impact of the launch of currency futures on the volatility of India's foreign exchange rate. The findings clearly depict that presence of volatility persistence is there for USD-INR, GBP-INR, EUR-INR, and JPY-INR. The volatility of returns of exchange rate before and after currency futures was discovered to be different as it is higher in pre futures period for USD-INR, and GBP-INR whereas for EUR-INR and JPY-INR it is higher in post futures period.The major implication is that when constructing hedging strategies, investors must account for volatility persistence between currency futures and spot markets.

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“…Large number of researches (Karolyi, 1995;Hong, 2001;Koopman et al, 2005;Hammoudeh and Li, 2008;Khemiri, 2011;Bonilla and Sepulveda, 2011;Fiszeder and Orzeszko, 2012;Salma, 2015;Mittal and Kumar, 2016;Demirer, Gupta and Wong, 2019) adopted non-linear ARCH/GARCH family models (Engle, 1982;Bollerslev, 1986) for probing precariousness of different types of time series data. The literature further state that predominance of the research studies in Asia have implemented vector autoregression (VAR) framework, Cointegration and Granger causality test only (Joshi, 2011;Tuan et al, 2015).…”

Section: Review Of Literaturementioning

confidence: 99%

Shock and Volatility Spillovers between Stock Markets of India and Select Asian Economies

Kumar¹

2019

rpm

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Flow of information and volatility coming from stock markets of other countries have significant impact on the stock market of a country. Volatility is even higher in the case the countries enjoy good economic conditions among themselves. The present manuscript aims to probe into the spread of impacts over a large range of returns and volatility in four major equity markets of Asia viz. India, China, Hongkong and Japan for a period of 18 years ranging from 2000 to 2017. The study uses VAR based GARCH model to determine the volatility spillover among the chosen countries for the period under assessment. The empirical outcomes of the study present that all selected markets have responded to their own lag of conditional volatility along with news shocks. The impact of conditional variance is higher in comparison to shocks which is an indication that markets fundamentals are stronger than corrections or shocks. The results of cross country spillover show that volatility of Shanghai Stock Exchange of China and shocks from Japan and Hongkong markets assert a significant effect over volatility of Indian equity market. Volatility of stock markets of Japan and China is not affected by the cross market volatility and shocks spillover from India. In contrast, volatility of Hongkong market is affected by shocks and volatility of Indian equity markets. Findings of the research have meaningful insights for the Governments and regulators, academicians, researchers, investors and fund managers in framing investment strategies in the chosen markets.

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Modelling volatility in Indian currency market

Cited by 5 publications

References 54 publications

The Impact of Shocks on the Volatility of the Dubai General Market Index (Dfmgi), Using Asymmetric Garch Models

The Impact of Shocks on the Volatility of the Dubai General Market Index (Dfmgi), Using Asymmetric Garch Models

Impact of Currency Futures Issuance on Foreign Exchange Rate Volatility in India

Shock and Volatility Spillovers between Stock Markets of India and Select Asian Economies

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