The securities market plays an integral role in any well functioning economy. In particular, it helps in raising of equity capital, provision of investment opportunities, and transfer of funds from surplus agents to deficit agents. In order to attain Vision 2030, the Nairobi Securities Exchange (NSE) is expected to play a key role in the economic pillar of this blueprint by perpetuating the mobilization of resources for implementation of envisaged flagship programs and projects. However, despite the key importance of the NSE in the local and regional economy, this market has exhibited high levels of volatility of returns. This is inferred by the huge responses to sectoral and systematic shocks that have been observed in the NSE, as well as extant empirical evidence of high levels of volatility persistence in its sectors. Therefore, it is vital to understand the NSE sectors‟ shock and volatility transmission linkages since this can enhance portfolio selection processes, help in forecasting of future sectoral volatility, and guide market regulators in mitigating the adverse effects of shock and volatility transmission. The main objective of this study was to establish the extent to which volatility of sectoral returns in the NSE, as at any specified time period, is influenced by lagged disturbances. In particular, the study investigated the influence of own past shocks; cross past shocks; own past volatility; and cross past volatilities on the volatility of each NSE sector. Further, the study also investigated the moderating effect of liquidity on the relationship between own past volatility & shocks and volatility of each NSE market sector. Weekly secondary time series data for the 6th June 2008 to 8th February 2019 period was used for all analysis processes. The study utilized a VAR(1)BEKK MGARCH(1,1) model to investigate the influence of lagged disturbances on sectoral volatility in the NSE. The moderating effect of liquidity on own shock and volatility transmission effects was appraised using the Irwin and McClelland (2001) two step process and estimation of GARCH(1,1) models as well as GARCH(1,1) models with exogenous variance regressors. All empirical analysis was undertaken using the WinRATS Version 10 software package. Results indicated that own past shocks influence volatility of all NSE sectors. In addition, volatility of each sector of the NSE in any given week was found to be responsive to cross shocks from the previous week, although the prevalence of these effects wasn‟t uniform across all sectors. Moreover, the study found evidence of significant own volatility transmission effects in six NSE sectors. Further, there was evidence of cross volatility transmission linkages in all sectors, though these linkages were more prevalent in some sectors than others. The study also found out that liquidity moderates own shock and volatility transmission effects in the Agricultural, Automobiles & Accessories, Commercial & Services, and Energy & Petroleum sectors only. On the basis of these results, several policy implications of the study are outlined: When sectoral shocks are detected, investors and portfolio managers should prepare for an elevation of idiosyncratic volatility in the near future and undertake mitigation measures in advance, depending on the shock transmission linkages of the affected sector(s). Regulatory authorities should also monitor markets proactively and be ready to enforce administrative measures to prevent speculative gains and dramatic losses that can lead to a spike in volatility immediately after a major sectoral shock. Moreover, portfolio managers and investors should use the unveiled shock transmission patterns in tandem with their respective portfolio allocation strategies when choosing the sectors to invest in. Additionally, investors should avoid forming portfolios with stocks of sectors that are linked by shock and volatility transmission linkages. Finally, since the study found out that liquidity moderates the transmission of own shocks and own volatility in four NSE sectors, it is suggested that volatility management measures for portfolios that include stocks from these four sectors should be robust enough to take into account the extant moderating effect on liquidity
Research, S. & LAIBONI, G (2021). Lagged Disturbances And Volatility Of Sectoral Returns In The Nairobi Securities Exchange, Kenya. Afribary. Retrieved from https://afribary.com/works/lagged-disturbances-and-volatility-of-sectoral-returns-in-the-nairobi-securities-exchange-kenya
Research, SSA, and GABRIEL LAIBONI "Lagged Disturbances And Volatility Of Sectoral Returns In The Nairobi Securities Exchange, Kenya" Afribary. Afribary, 27 May. 2021, https://afribary.com/works/lagged-disturbances-and-volatility-of-sectoral-returns-in-the-nairobi-securities-exchange-kenya. Accessed 05 Oct. 2022.
Research, SSA, and GABRIEL LAIBONI . "Lagged Disturbances And Volatility Of Sectoral Returns In The Nairobi Securities Exchange, Kenya". Afribary, Afribary, 27 May. 2021. Web. 05 Oct. 2022. < https://afribary.com/works/lagged-disturbances-and-volatility-of-sectoral-returns-in-the-nairobi-securities-exchange-kenya >.
Research, SSA and LAIBONI, GABRIEL . "Lagged Disturbances And Volatility Of Sectoral Returns In The Nairobi Securities Exchange, Kenya" Afribary (2021). Accessed October 05, 2022. https://afribary.com/works/lagged-disturbances-and-volatility-of-sectoral-returns-in-the-nairobi-securities-exchange-kenya