Evidence of Interdependence between Listed Companies of Major Sector in Dhaka Stock Market
Abstract
This article focuses on the major companies of different sectors that trade on the Dhaka stock market. In the Dhaka stock exchange, different sectors played a significant role. These sectors have a significant influence on the Dhaka stock market index. Previous research mainly focused on the connection between stock markets and GDP, currency rates, commodities, oil, and so on, but did not focus on the connection between companies inside stock markets. Therefore, it is necessary to estimate the correlation between the companies in various sectors. This article explores the volatility and interrelationships between the companies that can be modeled in DCC-GARCH framework. Concurrently, Diebold and Yilmaz's technique was applied to investigate spillover effects and sector-wise company interconnectedness for robustness purposes. From the sample data, it was observed that the pairwise correlation in the companies is positive and significant. The DCC-GARCH model result revealed that there is evidence of volatility and that it exists over a longer period. The empirical findings indicate significant volatility as well as evidence of interdependence among the listed companies. Dhaka Bank, Aftabauto, RENATA, PRIMETEX, and HRTEX are the most commonly identified shock receivers and transmitters. Diebold and Yilmaz's findings are similar to those obtained using the DCC-GARCH method in that there is an indication of strong interdependence and spillover effects. The findings are essential for micro-investors and the policymakers to make further advancements not only important for a single nation but also for other countries.
Keywords: interdependence, DCC-GARCH model, Dhaka stock market, volatility, the Diebold-Yilmaz method.
Full Text:
PDFReferences
ANYIKWA I. & LE ROUX P. Integration of African Stock Markets with the Developed Stock Markets: An Analysis of Co-Movements, Volatility and Contagion. International Economic Journal, 2020, 34(2): 279–296. https://doi.org/10.1080/10168737.2020.1755715
BOLLERSLEV T. & ENGLE R. F. Modelling the Persistence of Conditional Variances. Econometric Reviews, 1986, 5(1): 1–50. https://doi.org/10.1080/07474938608800095
HOSSAIN M. J. & ISMAIL M. T. Performance of a Novel Hybrid Model Through Simulation and Historical Financial Data. Sains Malaysiana, 2022, 51(7): 2249–2264. http://doi.org/10.17576/jsm-2022-5107-25
RIZAN M., SALIM M. Z., MUKHTAR S. and DALY K. Macroeconomics of Systemic Risk: Transmission Channels and Technical Integration. Risks, 2022, 10(9): 1–27. https://doi.org/10.3390/risks10090174
YOUSAF I., HANIF H., ALI S. and MOUDUD-UL-HUQ S. Linkages between gold and Latin American equity markets: portfolio implications. Journal of Economics, Finance and Administrative Science, 2021, 26(52): 237–251. https://doi.org/10.1108/JEFAS-04-2020-0139
YOUSUF M. & ZHAI J. The financial interconnectedness between global equity markets and crude oil: evidence from the GCC. Journal of Chinese Economic and Business Studies, 2022, 20(2): 183–206. https://doi.org/10.1080/14765284.2021.1989884
HOSSAIN M. J., ISMAIL M. T., AKTER S. and HOSSAIN M. R. Can Bitcoin Become a Hedge, Diversifier, or Safe-Haven for Emerging and Frontier Stock Markets? Journal of Chinese Economic and Business Studies, 2022, 12(1): 587–596. https://dx.doi.org/10.12785/ijcds/120147
DIEBOLD F. X. & YILMAZ K. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 2012, 28(1): 57–66. https://doi.org/10.1016/j.ijforecast.2011.02.006
DIEBOLD F. X. & YILMAZ K. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 2014, 182(1): 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
BOLLZERSLEV T. Generalized Auroregressive Conditional Hetroskedasticity. Journal of Econometrics, 1986, 31: 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
HELLIWELL F. J. Linkages between National Capital Markets: Does Globalization Expose Policy Gaps? 1. In: HELLIWELL F. J. (Ed.) Critical Issues in International Financial Reform. Routledge, Abingdon-on-Thames, 2018: 153–174. http://dx.doi.org/10.4324/9781351323765-6
SONG W., PARK S. Y. and RYU D. Dynamic conditional relationships between developed and emerging markets. Physica A: Statistical Mechanics and its Applications, 2018, 507: 534–543. https://doi.org/10.1016/j.physa.2018.05.007
KAMALUDIN K., SUNDARASEN S. and IBRAHIM I. Covid-19, Dow Jones and equity market movement in ASEAN-5 countries: evidence from wavelet analyses. Heliyon, 2021, 7(1): e05851. https://doi.org/10.1016/j.heliyon.2020.e05851
AJMI H., ARFAOUI N. and SACI K. Volatility transmission across international markets amid COVID 19 pandemic. Studies in Economics and Finance, 2021, 38(5): 926–945. https://doi.org/10.1108/SEF-11-2020-0449
ASHRAF B. N. Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance, 2020, 54: 101249. https://doi.org/10.1016/j.ribaf.2020.101249
MAGHYEREH A. & ABDOH H. COVID-19 and the volatility interlinkage between bitcoin and financial assets. Empirical Economics, 2022, 63: 2875–2901. https://doi.org/10.1007/s00181-022-02223-7
BERNINGER, M., KIESEL F. and KOLARIC S. Should I stay or should I go? Stock market reactions to companies’ decisions in the wake of the Russia-Ukraine conflict. SSRN Electronic Journal, 2022. https://dx.doi.org/10.2139/ssrn.4088159
ALAM M. K., TABASH M. I., BILLAH M., KUMAR S. and ANAGREH S. The Impacts of the Russia–Ukraine Invasion on Global Markets and Commodities: A Dynamic Connectedness among G7 and BRIC Markets. Journal of Risk and Financial Management, 2022, 15(8): 352. https://doi.org/10.3390/jrfm15080352
ADJASI C. K. D. & BIEKPE N. B. Stock market development and economic growth: The case of selected African countries. African Development Review, 2006, 18(1): 144–161. https://doi.org/10.1111/j.1467-8268.2006.00136.x
MOHAMED DAHIR A., MAHAT F., AB RAZAK N. H. and BANY-ARIFFIN A. N. Revisiting the dynamic relationship between exchange rates and stock prices in BRICS countries: A wavelet analysis. Borsa Istanbul Review, 2018, 18(2): 101–113. https://doi.org/10.1016/j.bir.2017.10.001
AMEWU G., OWUSU P. and AMENYITOR E. A. Co-movement between equity index and exchange rate: Fresh evidence from COVID-19 era. Scientific African, 2022, 16: e01146. https://doi.org/10.1016/j.sciaf.2022.e01146
MOBAREK A., MURADOGLU G., MOLLAH S. and HOU A. J. Determinants of time varying co-movements among international stock markets during crisis and non-crisis periods. Journal of Financial Stability, 2016, 24: 1–11. https://doi.org/10.1016/j.jfs.2016.03.003
FARID S., NAEEM M. A., PALTRINIERI A. and NEPAL R. Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities. Energy Economics, 2022, 109: 105962. https://doi.org/10.1016/j.eneco.2022.105962
BILLAH M., KARIM S., NAEEM M. A. and VIGNE S. A. Return and volatility spillovers between energy and BRIC markets: Evidence from quantile connectedness. Research in International Business and Finance, 2022, 62: 101680. https://doi.org/10.1016/j.ribaf.2022.101680
UMAR Z., POLAT O., CHOI S.-Y. and TEPLOVA T. The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 2022, 48: 102976. https://doi.org/10.1016/j.frl.2022.102976
SHARIF A., ALOUI C. & YAROVAYA L. COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 2020, 70: 101496. https://doi.org/10.1016/j.irfa.2020.101496
HUNG N. T. Dynamic spillover effect and hedging between the gold price and key financial assets. New evidence from Vietnam. Macroeconomics and Finance in Emerging Market Economies, 2021: 1–31. https://doi.org/10.1080/17520843.2021.1947614
LING Y. X., XIE C. and WANG G. J. Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective. Emerging Markets Review, 2022, 52: 100912. https://doi.org/10.1016/j.ememar.2022.100912
QAMRUZZAMAN M., KLER R., THEIVANAYAKI M. and KARIM S. Stock market volatility transmission and interlinkage: Evidence from BRICS. Universal Journal of Accounting and Finance, 2021, 9(5): 1142–1158. http://dx.doi.org/10.13189/ujaf.2021.090524
ENGLE R. F. New frontiers for ARCH models. Journal of Applied Econometrics, 2002, 17(5): 425–446.
HOSSAIN M. J. & ISMAIL M. T. Is there any influence of other cryptocurrencies on bitcoin? Asian Academy of Management Journal of Accounting and Finance, 2021, 17(1): 125–152. http://dx.doi.org/10.21315/aamjaf2021.17.1.5
GABAUER D. Volatility impulse response analysis for DCC-GARCH models: The role of volatility transmission mechanisms. Journal of Forecasting, 2020, 39(5): 788–796. https://doi.org/10.1002/for.2648
Refbacks
- There are currently no refbacks.
