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This study aims to investigate whether changes in consumer interest rate, exchange rate and housing supply have permanent effects on housing inflation in Turkey.

For this purpose, data from 2010M01 to 2020M06 and changes in consumer interest rate, exchange rate, housing supply and housing inflation were used. Relationships between variables are analyzed first by the Granger causality tests and then the conditional frequency domain causality tests. The conditional frequency domain causality test specifically reveals the permanent causality between variables, whether there is a permanent effect.

According to the Granger causality test results, there are causality relationships from changes in the consumer interest rate and exchange rate to housing inflation. However, there is no causality relationship between housing supply and housing inflation. According to the conditional frequency domain causality test results, there is causality for the permanent and mid-term from changes in the consumer interest rate to housing inflation and causality for the mid-term and temporary from changes in the exchange rate to housing inflation. Additionally, it was found that there are causality relationships between changes in the consumer interest rate and changes in the exchange rate.

The first limit of the study is that only 2010M01-2020M06 months can be considered. Because the date that variables started common is 2010M01. Besides, there is a limit in the study in variables used. Many variables, both micro and macro, can be added to affect housing inflation.

Housing inflation is a remarkable issue in Turkey. There is an increase in the number of studies on the subject in recent years. For this reason, the study is trying to contribute by approaching the subject from a different angle. The most important contribution of the study is that it has not been investigated whether the determinants of housing inflation have permanent or temporary effects, which were not done in previous studies. In addition, the method used reveals how many months the effects of changes in exchange rates, consumer interest rates and housing supply on housing inflation last. Based on the findings obtained from the methods, important economic and political implications have been put forward in depth.

The purpose of this study is to examine the impact of tourism activities on house prices in Turkey from January 2010 to March 2020.

It is used newly developed cointegration and causality tests based on Fourier approximation. These methods consider smooth structural breaks and do not need to recognize a priori date number and/or form of breaks.

Empirical findings show that international tourism activities have a substantial role in the escalation of house prices in Turkey. Findings also indicate a rise in industrial production enhances house prices while the mortgage lending rate exhibits a negative influence on house prices. Additionally, the evidence from Fourier causality tests reveals the unilateral causal linkage from tourism to house prices. This situation also shows that the tourism sector has a substantial role in stabilizing house prices’ rapid rise as a policy implication.

Although a large number of papers have been analyzing the link between house prices and macroeconomic variables, this study eliminates the lack of papers examining the link between tourism and house prices in Turkey by using the new cointegration and causality methods that consider smooth structural changes.

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

The aim of this study is to investigate the relationship between economic policy uncertainty (EPU) and stock prices during the period from March 2003 to March 2021.

The study uses asymmetric and symmetric frequency domain causality tests and focuses on BRIC countries, namely, Brazil, Russia, India and China.

The findings of the symmetric causality test confirm unidirectional permanent causality from EPU to stock prices for Brazil and India and bidirectional causality for China. However, according to the asymmetric causality test, the findings for China show that there is no causality between the variables. The results for Brazil and India indicate that there is unidirectional permanent causality from positive components of EPU to positive components of stock prices. Moreover, for Brazil, there is unidirectional temporary causality from the negative components of EPU to the negative components of stock prices. For India, there is temporary causality in the opposite direction.

The reactions of financial markets to positive and negative shocks differ. In this context, to the best of the authors’ knowledge, this study is the first attempt to examine the causal relationships between stock prices and uncertainty using an asymmetric frequency domain approach. Thus, the study enables the analysis of the effects of positive and negative shocks in the stock market separately.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

The purpose of this paper is to revisit the dynamic linkages between the US and the national securitized real estate markets of each of the nine Asian-Pacific (APAC) economies in time-frequency domain.

Wavelet decomposition via multi-resolution analysis is employed as an empirical methodology to consider time-scale issue in studying the dynamic changes of the US–APAC cross-real estate interdependence.

The strength and direction of return correlation, return exogeneity, shock impulse response, market connectivity and causality interactions change when specific time-scales are involved. The US market correlates with the APAC markets weakly or moderately in the three investment horizons with increasing strength of lead-lag interdependence in the long-run. Moreover, there are shifts in the net total directional volatility connectivity effects at the five scales among the markets.

Given the focus of the five approaches and associated indicators, the picture that emerges from the empirical results may not completely uniform. However, long-term investors and financial institutions should evaluate the time-scale based dynamics to derive a well-informed portfolio decision.

Future research is needed to ascertain whether the time-frequency findings can be generalizable to the regional and global context. Additional studies are required to identify the factors that contribute to the changes in the global and regional connectivity across the markets over the three investment horizons.

This study has successfully decomposed the various market linkage indicators into scale-dependent sub-components. As such, market integration in the Asia-Pacific real estate markets is a “multi-scale” phenomenon.

This paper aims to analyze forecasting problems from the perspective of information extraction. Circumstances are studied under which the forecast of an economic variable from one domain (country, industry, market segment) should rely on information regarding the same type of variable from another domain even if the two variables are not causally linked. It is shown that Granger causality linking variables from different domains is the rule and should be exploited for forecasting.

This paper applies information economics, in particular the study of rational information extraction, to shed light on the debate on causality and forecasting.

It is shown that the rational generalization of information across domains can lead to effects that are hard to square with economic intuition but worth considering for forecasting. Information from one domain is shown to affect that from another domain if there is at least one common factor affecting both domains, which is not (or not yet) observed when a forecast has to be made. The analysis suggests the theoretical possibility that the direction of such effects across domains can be counter-intuitive. In time-series econometrics, such effects will show up in estimated coefficients with the “wrong” sign.

This study helps forecasters by indicating a wider set of variables relevant for prediction. The analysis offers a theoretical basis for using lagged values from the type of variable to be forecast but from another domain. For example, when forecasting the bond risk spread in one country, introducing in the time-series model the lagged value of the risk spread from another country is suggested. Two empirical examples illustrate this principle for specifying models for prediction. The application to risk spreads and inflation rates illustrates the principles of the approach suggested here which is widely applicable.

The present study builds on a probability theoretic analysis to inform the specification of time-series forecasting models.

The volatility of bitcoin (BTC) and time horizon is the center point for investment decisions. However, attention is not often drawn to the relationship between BTC and equity indices. Thus, the purpose of this paper is to investigate the volatility and time frequency domain of BTC with stock markets.