Published Online:October 2024
Product Name:The IUP Journal of Accounting Research & Audit Practices
Product Type:Article
Product Code:IJARAP131024
Author Name:A Sumera, BVR Vishnu Tej and Nayana M S
Availability:YES
Subject/Domain:Finance
Download Format:PDF
Pages:302-320
The paper aims at a comprehensive exploration of a critical aspect of algorithmic trading (AT), focusing on evaluation of intra-day volume modeling and prediction techniques, with volume weighted average price (VWAP) as a central element. The study demonstrates the effectiveness of intra-daily volume modeling and prediction for AT, particularly the model’s volatility predictions, which exhibits improved execution quality and consistently outperforms traditional trading strategies. The GARCH model provides valuable insights into volatility clustering, an essential characteristic of financial time series. The critical evaluation of two variations of the VWAP algorithm, 2-VWAP and 5-VWAP, each using a different window for calculating the VWAP, provides insightful inferences for traders, risk managers, microstructure researchers, and regulatory bodies.
Financial markets have always remained dynamic and puzzling, and stock markets are more so. Trading in equity markets has been persistently challenging owing to market dynamism. With the advent of technology, trading strategies have significantly changed