{"title":"Hedging With Futures","authors":"Deepika Krishnan","doi":"10.4018/ijabe.333859","DOIUrl":null,"url":null,"abstract":"This research explores the utilization of wavelet transform decomposition as an effective tool for hedging in the Indian stock market, particularly focusing on hedging with index futures contracts. Utilizing daily data obtained from the National Stock Exchange (NSE) of India spanning from 2010 to 2022, the study investigates the lead-lag relationships, correlations, and hedge ratios across different time scales through the wavelet transform method. The findings indicate a clear relationship between the Nifty 50, Nifty Bank, and Index futures in both short and longer time frames. However, in intermediate time scales, the Nifty Bank contract exhibits a leading position in the market. The correlation analysis underscores that time plays a crucial role in determining the variations, resulting in a wide range of correlations. The effectiveness of hedging, measured by the hedge ratio, displays an increasing trend across different time zones.","PeriodicalId":41154,"journal":{"name":"International Journal of Applied Behavioral Economics","volume":"18 8","pages":""},"PeriodicalIF":0.2000,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Behavioral Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijabe.333859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
This research explores the utilization of wavelet transform decomposition as an effective tool for hedging in the Indian stock market, particularly focusing on hedging with index futures contracts. Utilizing daily data obtained from the National Stock Exchange (NSE) of India spanning from 2010 to 2022, the study investigates the lead-lag relationships, correlations, and hedge ratios across different time scales through the wavelet transform method. The findings indicate a clear relationship between the Nifty 50, Nifty Bank, and Index futures in both short and longer time frames. However, in intermediate time scales, the Nifty Bank contract exhibits a leading position in the market. The correlation analysis underscores that time plays a crucial role in determining the variations, resulting in a wide range of correlations. The effectiveness of hedging, measured by the hedge ratio, displays an increasing trend across different time zones.
本研究探讨了如何利用小波变换分解作为印度股票市场套期保值的有效工具,尤其侧重于利用指数期货合约进行套期保值。研究利用从印度国家证券交易所(NSE)获得的 2010 年至 2022 年的每日数据,通过小波变换方法研究了不同时间尺度上的领先-滞后关系、相关性和对冲比率。研究结果表明,在短期和长期时间范围内,Nifty 50、Nifty Bank 和指数期货之间都存在明显的关系。然而,在中时间尺度上,Nifty Bank 合约在市场中处于领先地位。相关性分析强调,时间在决定变化方面起着至关重要的作用,从而导致了广泛的相关性。以套期保值比率衡量的套期保值效果在不同时区呈现出上升趋势。