COVİD-19 Dezavantajlarına Yönelik Hisse Senedi Oynaklıkları: Ruanda Borsası Örneği

Abstract

Bu çalışmanın amacı, COVID-19'un çeşitli finansal kurum ve yatırımcıların ilgisini çeken Ruanda Menkul Kıymetler Borsası üzerindeki etkisini incelemektir. Ruanda Menkul Kıymetler Borsası'nda işlem gören dört şirketin 4 Ocak 2020 ile 12 Nisan 2021 arasındaki günlük hisse senedi getirisi verilerinin ampirik bir analizini gerçekleştirdik. Çalışmada, AR, MA, ARIMA ve ARCH modelleri birbiri ardına uygulanmıştır. Serilerin durağanlığını doğrulamak için bir ARIMA modeli oluşturmadan önce zaman serisi durağanlığını kontrol ettik. ARIMA modeli tüm şirketlerin verilerine iyi uyuyor, ancak Ljung-Box testini kullanarak boş hipotezi reddetmeyi başaramadık, ARCH etkisini gösteren BOK verileri için testin p-değerinin 0.05'ten küçük olmasına rağmen, BLR, KCB ve EQRY şirketleri için ARCH etkisinin olmadığı sonucuna vardık. Sonuçlar AR modelinin yeterli olduğunu göstermektedir ve seri korelasyonlu olmadığı için BLR, KCB ve EQTY' den verileri modellemek için kullanılabilir ve verilerde ARCH etkisi tespit edilmemiştir. BOK'un tek verisi ARCH modeli kullanılarak modellenebilir ve sonuç alfa 1'in 1.000e+00 olduğunu gösteriyor ki bu çok yüksek ve bu BOK piyasasının istikrarsız olduğunu göstermektedir. 

Keywords

• AR
• MA
• ARIMA
• ARCH
• COVID-19
• Finansal piyasalar

Stock Market Volatility Towards COVID-19 Drawbacks: Case of Rwanda Stock Exchange

Abstract

The purpose of this study is to look into the impact of the COVID-19 on the Rwanda Stock Exchange, which is of interest to a variety of financial institutions and investors. We conducted an empirical analysis of the daily stock returns data from January 4th, 2020 to April 12th, 2021 of four Rwanda Stock Exchange-listed companies. The AR, MA, ARIMA, and ARCH models were applied one after another. To verify the series stationarity, we checked the time series stationarity before building an ARIMA model. The ARIMA model fit the data of all companies well, but using the Ljung-Box test, we failed to reject the null hypothesis and concluded that there are no ARCH effects present for BLR, KCB, and EQRY companies, despite the fact that the p-value of test was less than 0.05 for BOK data, indicating the ARCH effect. The results show that the AR model is adequate and can be used to model data from BLR, KCB, and EQTY because there is no serial correlation and no ARCH effect depicted in the data, and the only data of BOK can be modelled using the ARCH model and the result shows that alpha 1 is 1.000e+00 which is very high and this indicates that the BOK market is jumpy (unstable).

Keywords

• AR
• MA
• ARIMA
• ARCH
• COVID-19
• Financial Markets

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