The interbank market in Kenya: Picking the distress signal from the treasury bills' market - Jared Osoro; David Muriithi (Kenya Bankers Association (KBA), 2017)
This paper seeks to explore the Kenyan interbank market characteristics at a time of liquidity stress and how it relates to the intervention by the Central Bank of Kenya (CBK) through the open market operation. We seek to ascertain whether the CBK's action during the interbank market distress - arising from either a market player being placed under receivership, or the collapse of a bank - is reactive or proactive. The ARCH family of models, specifically the EGARCH and TGARCH, are applied to explore the interbank market-Treasury bill market nexus. Three episodes of banking industry stress arising from the placement of Dubai Bank, Imperial Bank and Chase Bank placement under receivership within a nine-month period anchor the analytical stress triggers. The key findings are that 91-Day the Treasury Bill rate positively and significantly affects the interbank market rate, with the effect doubling in the wake of a distress in the banking system. The Treasury Bills market reveals two important attributes of a distressed interbank market. One is that the interbank market volatility is long-lived rather than transitory. This implies the CBK intervention through either lender of last resort window or open market operations, and the market response as could be inferred from the Treasury bill market does not solve the structural challenges within the interbank market. Two, there is an asymmetric reaction to news depending on whether they are good news or bad news. The reaction of the interbank market to bad news as would be picked by the Treasury Bills market significantly impacts on interbank market unlike when there are no bad news hence evidence for leverage effect. We further note that even with the intervention by the CBK or the market reaction through liquidity portfolio shifts, the leverage effect in the interbank market still remains.
Keywords: 330; Interbank; Volatility Clustering; GARCH models and leverage effect