Liquidity of Government of India Bonds : Trading Volume Based Analysis

Objective: Bonds experience high trading volume right after their issuance. Outstanding amount of a bond is built by multiple follow on reissuances. Trading volume also picks up when bond is reissued. This paper investigates impact of bond's age since issuance on its weekly trading volume. In addition it also investigates impact of trade size, outstanding amount, Foreign Institutional Investor (FII) trading, Repo rate volatility, Yield to Maturity volatility and Mumbai Interbank Offer Rate (MIBOR) volatility in the Indian context.Design, Methodology & Approach: This paper builds on Hong and Warga (2000), Kalimipalli and Warga (2002), and Diaz et. al. (2006). Non-linear regression is used to investigate exponential decay of weekly trading market share of 5, 10 and 30 Year bonds with respect to age. The model also explores linear relationship of other factors on the weekly trading volume.Findings: (This) Our research finds evidence of decay in trading with respect to bond age, reissuance and trade size. The model suggests two issuances of 5 year, three issuances of 10 Year and one issuance of 30 year bond per year for optimum trading volume in the Government of India (GoI) bond market. Implications: GoI publishes a tentative schedule with no indication of which tenure will be auctioned. This uncertainty impacts the trading as market lacks anticipation of maturity segments in the yield curve. A regular schedule of 5, 10 and 30 year bonds provides for better public debt management with enhanced trading liquidity and lower cost of public debt. Originality & Value: Literature review provides no evidence of similar study in Indian context. Hence it is the first study of this kind. This study provides a bond issuance approach for enhanced public debt management, market trading and lower cost of public debt. Research Limitations: Due to lack of data, over the counter (OTC) trades are not incorporated in the analysis. Inclusion of treasury bills trades in the model may provide better understanding of trading risk