Precious Metals Price Forecast with Arch Family Models in Covid-19 Pandemic Period

The COVID-19 virus, which was detected for the first time in Wuhan, China in December 2019, spread to all countries of the world and, therefore, became a global epidemic. Although more than two years have passed since the outbreak of the COVID-19 pandemic, the economic effects of it continue. One of these is the effect of the pandemic on precious metal prices. Precious metals, which are called safe harbours and used as investment tools, have had a serious volatility in the last century as a result of the economic, political and pandemic factors changing the international balances. From this point of view, in this study, it is aimed to determine the appropriate forecasting model to predict the volatility of gold, silver, platinum and palladium prices, which are called precious metals, during the COVID-19 pandemic period. The econometric analysis covers the period between March 11, 2020, when the global epidemic was declared by the World Health Organization, and September 13, 2021, and includes 326 days of observation. To determine the appropriate forecasting model, ARCH, GARCH, T-GARCH, E-GARCH and PARCH are used as symmetrical and asymmetrical volatility models.

Spillover Effects of Stock Markets in the COVID-19 Pandemic Period: Evidence From Central and East European Countries

This study aims to investigate the correlation and the spillover effects between Central and East European (CEE) Countries' stock markets during the Covid-19 Pandemic Period. CEE countries are listed as Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, the Slovak Republic, Slovenia, Estonia, Latvia, and Lithuania by OECD. The data set was obtained from the Bloomberg data services and includes 308 observations of daily returns between March 11th, 2020 and August 1st, 2021. As a result of the empirical analysis using the Pearson Correlation, the Multivariate VAR Model, and the Granger Causality Test, a high correlation was found between the stock markets of CEE countries, and 15 causality relationships were determined. The analysis also revealed bidirectional relationships between the Bulgaria Stock Exchange Index and Romania Bucharest Stock Exchange Index, the Polish Warsaw Stock Exchange Index and Croatia Zagreb Stock Exchange Index, the Romania Bucharest Stock Exchange Index and Bulgaria Stock Exchange Index, and the Croatia Zagreb Stock Exchange Index and Polish Warsaw Stock Exchange Index. High correlation and causality relationships, which are also supported by impulse-response and variance decomposition test results, reveal that there is a spillover effect between the stock markets of CEE countries.