Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion
International Journal of Finance and Banking Research
Volume 4, Issue 5, October 2018, Pages: 79-90
Received: Oct. 5, 2018; Accepted: Oct. 17, 2018; Published: Nov. 7, 2018
Views 106      Downloads 13
Caspah Lidiema, Department of Economics, Accounts, & Finance, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Article Tools
Follow on us
The financial stability objective of any financial system authority is to maintain confidence, and promote the safety and soundness of the domestic financial system. Financial stability has been defined as the resilience of the financial system in the face of adverse shocks to enable the continued smooth functioning of the financial intermediation process. The Kenyan Financial service providers are diverse and they include 42 commercial banks, 49 insurance companies, 12 deposits taking microfinance banks, and 199 registered savings and credit cooperatives (SACCOs). This paper examined financial intra-market linkages (dynamic relationship and volatility spillovers) effects between the Commercial banks and other financial sector segments (Insurance and Capital Markets) in Kenya and the impact of this transmission on financial inclusion. The study evaluated the effect of intra-market linkages on financial inclusion using Bayesian Vector Autoregressive (BVAR) using monthly data from the Kenyan market during the period January 2004 - December 2016. Impulse-response analysis and forecast error variance decomposition were used to investigate these intra-market linkages and their causal effect to financial inclusion. Results show that there are significant market interactions and interlinkages with significant shocks transmission moving from banks to other markets. Interest rates shock transmission affected all markets. This means that monetary policy transmission as expected trickles down to the entire financial sector. The study also found out that, positive shocks from credit impacts positively on lending rate and the capital markets performance implying banking mechanism to reward increased loan uptake at cheaper prices and hence creating cash-flow that spills over to more investment on the Nairobi Securities Exchange. The study recommends that policy makers design policies that help minimize the adverse impact of volatility/shocks but create opportunities for growth on each market to foster price stability and increase investments through financial inclusion.
Spillover, Commercial Banks, BVAR, Shocks, Volatility, Financial Sector
To cite this article
Caspah Lidiema, Intra-Market Linkages in the Financial Sector and Their Effects on Financial Inclusion, International Journal of Finance and Banking Research. Vol. 4, No. 5, 2018, pp. 79-90. doi: 10.11648/j.ijfbr.20180405.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sahay, R., Cihak, M., N'Diaye, P., & Barajas, A. (2015). Rethinking financial deepening: Stability and growth in emerging markets. Revista de Economía Institucional, 17(33), 73-107.
Chakrabarty, K. C (2011, September). Financial Inclusion (Keynote Address), Mumbai.
Cheng, X., & Degryse, H. (2010). The impact of bank and nonbank financial institutions on local economic growth in China. Journal of Financial Services Research, 37(2-3), 179-199.
Cevik, S., & Teksoz, K. (2013). Lost in Transmission? The Effectiveness of Monetary Policy Transmission Channels in the GCC Countries. Middle East Development Journal, 5(3), 1350018-1.
Gali, J., & Gambetti, L. (2015). The effects of monetary policy on stock market bubbles: Some evidence. American Economic Journal: Macroeconomics, 7(1), 233-257.
Cummins, J. D., Lewis, C. M., & Wei, R. (2006). The market value impact of operational loss events for US banks and insurers. Journal of Banking & Finance, 30(10), 2605-2634.
Kim, H. S. (2008). Spillover Effects of Pension Funds on Capital Markets: The Mechanism and Preconditions. http://ssrn. com/abstract=2805804}.
Chodorow-Reich, G. (2014). Effects of unconventional monetary policy on financial institutions (No. w20230). National Bureau of Economic Research.
King, R. G., & Levine, R. (1993). Finance, entrepreneurship and growth. Journal of Monetary economics, 32(3), 513-542.
King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. The quarterly journal of economics, 108(3), 717-737.
Levine, R., & Zervos, S. (1998). Stock markets, banks, and economic growth. American economic review, 537-558.
Wei, C. C. (2008). The analysis of interest rate mean and volatility spillover to the industrial production index and stock markets: The case of China. Economics Bulletin, 3(65), 1-14.
Serwa, D., & Wdowinski, P. (2016). Macro-financial linkages in the Polish economy: combined impulse-response functions in SVAR models.
Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
Chen, F. Y., & Liao, S. L. (2009). Modelling VaR for foreign-asset portfolios in continuous time. Economic Modelling, 26(1), 234-240.
De Medeiros, O. R., Van Doornik, B. F. N., & de Oliveira, G. R. (2011). Modeling and forecasting a firm's financial statements with a VAR-VECM model. CEP, 70843, 030.
Pecican, E. S. (2010). Forecasting based on open VAR model. Romanian Journal of Economic Forecasting, 13(1), 59-69.
Sims (2007) Bayesian Methods in Applied Econometrics, or, Why Econometrics Should Always and Everywhere Be Bayesian. Technical Report, Princeton University Villani.
Villani, M. (2009). Steady-state priors for vector autoregressions. Journal of Applied Econometrics, 24(4), 630-650.
Tsay, R. S. (2005). Analysis of financial time series (Vol. 543). John Wiley & Sons.
Sims, Christopher A. and Tao A. Zha. 1998. “Bayesian Methods for Dynamic Multivariate Models.” International Economic Review 39(4): 949-968.
Adenomon, M. O., Michael, V. A., & Evans, O. P. (2016). On the Performances of Classical VAR and Sims-Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms. Open Journal of Statistics, 6(01), 96-132.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931