Comparative investment decisions in emerging textile and FinTech industries in India using GARCH models with high-frequency data

• Published in: Industria textilă (Bucharest, Romania : 1994) Vol. 74; no. 6; pp. 741 - 752
• Main Authors: MEHER, BHARAT KUMAR, PUNTAMBEKAR, G.L., BIRAU, RAMONA, HAWALDAR, IQBAL THONSE, SPULBAR, CRISTI, SIMION, MIRCEA LAURENTIU
• Format: Journal Article
• Language: English
• Published: Bucharest The National Research & Development Institute for Textiles and Leather - INCDTP 01.01.2023
• Subjects: Autoregressive models; Clothing industry; COVID-19; Electronic trading systems; High frequency trading; Textiles; Volatility; Ukraine; Russia; India
• ISSN: 1222-5347
• DOI: 10.35530/IT.074.06.202311

The domestic textiles and apparel industry stood at $152 billion in 2021, growing at a CAGR of 12% to reach $225 billion by 2025. The textiles and apparel industry in India has strengths across the entire value chain from fibre, yarn, and fabric to apparel. On the other hand, many FinTech companies gained enough importance and attention during the Demonetization and COVID-19 pandemic situation where most people are dependent and prefer cashless payments and receipts over hard cash payments and receipts. Due to the growth of FinTech companies in India, consumer lending FinTech companies in India make up 17% of total FinTech enterprises. Many angel investors are coming forward to invest in such FinTech companies as this industry has much potential to grow in future. As there is enough scope for the expansion of FinTech companies in India, retail investors come forward to invest in the stocks of listed FinTech companies. As retail investors always look forward to returns either in the form of dividends or appreciation of stock prices, it is also necessary to analyse and model the stock price volatility of FinTech companies in India before investing. Hence, this research study is an attempt to use high-frequency data i.e. 1-minute closing prices, to formulate suitable GARCH (Generalised Autoregressive Conditional Heteroscedasticity) models for stock price volatility of listed textiles and FinTech companies that could also capture the asymmetric volatility if it exists due to third phase of COVID-19 pandemic and Russia-Ukraine war. The results concluded that there is a presence of positive shocks which might be due to the third wave of the COVID-19 pandemic that might have again shot the demand for financial products and services of these FinTech companies namely Paytm and PolicyBazaar and there is no negative shock of Russia-Ukraine war.