Environmental reverberations of geopolitical risk and economic policy uncertainty resulting from the Russia-Ukraine conflict: A wavelet based approach for sectoral CO2 emissions.

After the COVID-19 pandemic, Russia invaded Ukraine in February 2022, and a natural gas crisis between the European Union (EU) and Russia has begun. These events have negatively affected humanity and resulted in economic and environmental consequences. Against this background, this study examines the impact of geopolitical risk (GPR) and economic policy uncertainty (EPU) caused by the Russia-Ukraine conflict, on sectoral carbon dioxide (CO2) emissions. To this end, the study analyzes data from January 1997 to October 2022 by using wavelet transform coherence (WTC) and time-varying wavelet causality test (TVWCT) approaches. The WTC results show that GPR and EPU reduce CO2 emissions in the residential, commercial, industrial, and electricity sectors, while GPR increases CO2 emissions in the transportation sector during the period from January 2019 to October 2022, which includes Russia-Ukraine conflict. The WTC analysis also indicates that the reduction in CO2 emissions provided by the EPU is higher than that of the GPR for several periods. According to the TVWCT, there are causal impacts of the GPR and the EPU on sectoral CO2 emissions, but the timing of the causal impacts differs between the raw and decomposed data. The results suggest that the EPU has a larger impact on reducing sectoral CO2 emissions during the Ukraine-Russia crisis and that production disruptions due to uncertainty have the greatest impact on reducing CO2 emissions in the electric power and transportation sectors.

Housing prices in emerging countries during COVID-19: evidence from Turkey

PurposeBy considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.Design/methodology/approachA quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.FindingsPredictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables;the pandemic and rent have the highest effect on the indices;The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles;the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles;the results for RPPI, NRPPI and ORPPI are consistent and robust.Research limitations/implicationsThe results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.Practical implicationsThe COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.Social implicationsEmerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.Originality/valueThe QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.

The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis.

This study examines the relationship between mobility (a proxy for transport) and the COVID-19 pandemic by focusing on Turkey as an example of an emerging country. In this context, eight types of mobility and two indicators of COVID-19 were analyzed using daily data from March 11, 2020 to December 7, 2020 by applying Toda-Yamamoto causality test. The findings revealed that (i) there is cointegration between the variables in the long term; (ii) there is an econometric causality between mobility indicators (mobility of grocery, park, residential, retail, and workplace) and pandemic indicators; (iii) various mobility indicators have an econometric causality with different pandemic indicators; (iv) neither driving mobility nor walking mobility has an econometric causality with the pandemic indicators whereas some of the other types of mobility, such as grocery, park, and retail do. These results generally show the effects of mobility and highlight the importance of appropriate mobility restrictions in terms of the pandemic.

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.

The Impacts of Foreign Portfolio Flows and Monetary Policy Responses on Stock Markets by Considering COVID-19 Pandemic: Evidence from Turkey

This study researches the impacts of foreign portfolio flows (proxied by foreign investors’ retention share) and monetary policy responses (proxied by the repurchase interest rate) on Turkey’s stock market index taking the COVID-19 pandemic into consideration. A volatility index, credit default swap spreads, and foreign exchange rates are used as control variables, with a daily dataset between January 2, 2017, and October 20, 2020. After examining the stationarity and nonlinearity characteristics of the variables, we applied a nonlinear autoregressive distributed lag (NARDL) model and then conducted a Markov switching regression (MSR) for a robustness check. The results reveal that both foreign portfolio flows and monetary responses have an important effect on the index, and foreign portfolio flows have a higher effect than monetary responses. Accordingly, the results obtained from the NARDL and MSR models are robust and consistent.

Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms.

Some countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study's findings can help support economic growth by achieving low-level benchmark rates.

Changes of gold prices in COVID-19 pandemic: Daily evidence from Turkey's monetary policy measures with selected determinants

The study examines the impacts of monetary policy measures on the gold prices in Turkey by using daily data between January 2, 2020 and August 04, 2020 so that reactions of gold prices to the COVID-19 pandemic can be defined. In this context, the effects of 11 (including 3 global, 3 national, 3 monetary policy, 2 COVID-19) determinants on gold prices are examined by adopting machine learning algorithms. The empirical results reveal that (i) the most significant determinant of gold prices is the foreign exchange rate in the pre-pandemic period whereas securities amount bought by the central bank is important in the pandemic period;(ii) the number of confirmed cases and deaths have an important and intermediate effect on gold prices in the pandemic period;(iii) monetary policy measures are important for gold prices;(iv) global factors have a relatively high impact in both periods.