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Keywords = Nifty Index futures

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16 pages, 845 KB  
Article
Information Transmission Performance of the GIFT Nifty Futures: Evidence from High-Frequency Data
by Rajib Sarkar, Soumya Guha Deb and Amrit Panda
J. Risk Financial Manag. 2025, 18(9), 527; https://doi.org/10.3390/jrfm18090527 - 19 Sep 2025
Viewed by 2748
Abstract
This paper investigates the information transmission performance of GIFT Nifty futures using high-frequency data, a novel area of study given their recent introduction. We employ Johansen cointegration tests, Granger causality tests, GARCH models, Hasbrouck’s Information Share (IS) model, and Gonzalo–Granger’s Component Share (CS) [...] Read more.
This paper investigates the information transmission performance of GIFT Nifty futures using high-frequency data, a novel area of study given their recent introduction. We employ Johansen cointegration tests, Granger causality tests, GARCH models, Hasbrouck’s Information Share (IS) model, and Gonzalo–Granger’s Component Share (CS) model to assess market integration, volatility, and price discovery dynamics. Our findings reveal significant bidirectional Granger causality and cointegration between the GIFT Nifty futures price and the Nifty index price, indicating a stable long-term equilibrium. Additionally, the GARCH model captures substantial volatility, reflecting the market’s responsiveness to new information. The IS and CS models confirm that the GIFT Nifty futures play a crucial role in the price discovery process, leading the Nifty index. This research is timely, within eight months of the first anniversary of GIFT Nifty futures trading since its launch. The findings highlight the information transmission performance and importance of the GIFT Nifty futures in enhancing market stability and transparency, offering valuable insights into market behaviour, integration, and forecasting abilities. Full article
(This article belongs to the Special Issue Advancing Research in International Finance)
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14 pages, 3334 KB  
Article
COVID-19 Pandemic and Indices Volatility: Evidence from GARCH Models
by Rajesh Mamilla, Chinnadurai Kathiravan, Aidin Salamzadeh, Léo-Paul Dana and Mohamed Elheddad
J. Risk Financial Manag. 2023, 16(10), 447; https://doi.org/10.3390/jrfm16100447 - 17 Oct 2023
Cited by 6 | Viewed by 5515
Abstract
This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on [...] Read more.
This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices. Full article
(This article belongs to the Special Issue Banking during the COVID-19 Pandemia)
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11 pages, 555 KB  
Article
Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model
by Parizad Phiroze Dungore and Sarosh Hosi Patel
Int. J. Financial Stud. 2021, 9(1), 7; https://doi.org/10.3390/ijfs9010007 - 14 Jan 2021
Cited by 9 | Viewed by 7902
Abstract
The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded [...] Read more.
The generalized autoregressive conditional heteroscedastic model (GARCH) is used to estimate volatility for Nifty Index futures on day trades. The purpose is to find out if a contemporaneous or causal relation exists between volatility volume and open interest for Nifty Index futures traded on the National Stock Exchange of India, and the extent and direction of these relationships. A complete absence of bidirectional causality in any particular instance depicts noise trading and empirical analysis according to this study establishes that volume has a stronger impact on volatility compared to open interest. Furthermore, the impulse originating from volatility of volume and open interest is low. Full article
(This article belongs to the Special Issue Advances in Behavioural Finance and Economics)
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