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The purpose of this paper is to examine the evidence of long-range dependence behaviour in both house price returns and volatility for fifteen main regions in Finland over the period of 1988:Q1 to 2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two-rooms, and more than three rooms apartments types.

For each house price return series, both parametric and semiparametric long memory approaches are used to estimate the fractional differencing parameter d in an autoregressive fractional integrated moving average [ARFIMA (p, d, q)] process. Moreover, for cities and sub-areas with significant clustering effects (autoregressive conditional heteroscedasticity [ARCH] effects), the semiparametric long memory method is used to analyse the degree of persistence in the volatility by estimating the fractional differencing parameter d in both squared and absolute price returns.

A higher degree of predictability was found in all three apartments types price returns with the estimates of the long memory parameter constrained in the stationary and invertible interval, implying that the returns of the studied types of dwellings are long-term dependent. This high level of persistence in the house price indices differs from other assets, such as stocks and commodities. Furthermore, the evidence of long-range dependence was discovered in the house price volatility with more than half of the studied samples exhibiting long memory behaviour.

Investigating the long memory behaviour in both returns and volatility of the house prices is crucial for investment, risk and portfolio management. One reason is that the evidence of long-range dependence in the housing market returns suggests a high degree of predictability of the asset. The other reason is that the presence of long memory in the housing market volatility aids in the development of appropriate time series volatility forecasting models in this market. The study outcomes will be used in modelling and forecasting the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.

To the best of the authors’ knowledge, this is the first research that assesses the long memory behaviour in the Finnish housing market. Also, it is the first study that evaluates the volatility of the Finnish housing market using data on both municipal and geographical level.

The biggest enlargement of the European Union (EU) took place in May 2004 when 10 new countries (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) joined the union, increasing the number of member states from 15 to 25. Of these newcomers, eight are former Eastern European countries with transition to Western-type market economies. These emerging markets provide increasingly growing investment opportunities and international diversification options for fund managers and individual investors. Well-known features of emerging equity markets are high returns, high volatility, and low correlation with developed markets. Bekaert and Harvey (2002) find that this correlation is on average increasing, particularly for those emerging markets that have liberalised their financial markets. Mateus (2004) finds similar results with EU access countries for recent years. Additional features of emerging markets are sparse data, low liquidity, and large price changes due to political changes or market crashes (e.g. Hwang & Pedersen, 2004).

The purpose of the paper is to study the relationship between stock return correlation and volatility.

Utilizing a logit‐type regression model, the paper analyzes the incremental effect of volatility on the level of correlation. The focus of the paper is set on the impact of the volatilities involved in the definition and calculation of the correlation as well as on the effects of external volatilities from other markets.

In the paper, an explicit model was constructed to investigate the contribution of the level of volatility on mutual correlations of the markets. The empirical results strongly support the findings that high volatility tends to increase correlations between the markets (see for example). An analysis of the small Nordic markets further showed that the local volatilities may play a role in the change of the level of correlation. However, it is the general world‐wide volatility level that mainly drives the changes in the correlations.

Particularly, the results of the paper show that market correlations tend to be dependent on the general world‐wide volatility rather than on local volatilities of single markets. This approach gives us important information about the behavior of the correlation with respect to the level of each market's risk as well as to the general global market‐risk level. The results can be directly utilized by portfolio managers in planning portfolio diversification strategies in accordance with the expected future volatility.

This study develops an equilibrium model of credit spreads on Japanese yen Eurobonds based on a model proposed by Collin-Dufresne, Goldstein and Martin (2001). We find the asset factor, as proxied by the change in the stock market index, has only a limited effect, while the interest rate factor has the over-riding influence. There is also evidence that currency volatility and changes in the term structure occasionally have an effect on spread behaviour. Analysis over several subperiods, based around key economic events, demonstrates that the relative weight of these explanatory variables change over time.

The history and prospects of European integration are both fascinating and exciting. Analysts of every aspect of this process, including its cultural, economic, financial, historical, political, and social dimensions, should recall that its main rationale remains as it has always been, to permanently end conflict and to secure peace and prosperity for all Europeans. As the European Union's (EU's) own website (see http://europa.eu.int) points out Europe has been the scene of many and frequent bloody wars throughout the centuries. In the 75-year period between 1870 and 1945, for example, France and Germany fought each other three times with huge loss of life. The history of modern European integration commenced in earnest with the realization in the early 1950s that the best way to prevent future conflict is to secure more economic and political integration. This led to the establishment of the European Coal and Steel Community in 1951, followed shortly by the European Economic Community (EEC) in 1957. Since then, the process of integration and enlargement has progressed at varying speeds, but always moving forwards. In 1967, the founding institutions of the EEC were merged to form today's European Commission (EC), the Council of Ministers, and the European Parliament. The members of the European Parliament were initially chosen by the member governments of the EEC, but direct elections commenced in 1979, and have continued every 5 years since then. The Treaty of Maastricht created the EU in 1992 and established the process of economic and monetary union (EMU) that culminated in the introduction of the euro in 12 of the 15 Member States in 2002.

Yusaf Akbar is Associate Professor of International Business at the Southern New Hampshire University, United States. His teaching and research interests are in foreign direct investment, public policy and strategy, and his geographical area interests are in East and Central Europe. He has published widely in peer-reviewed journals including Journal of World Business, Thunderbird International Business Review and World Competition. Yusaf has been Visiting Professor at various schools around the world, including the American University in Bulgaria, ESSCA, the KMBS, the MIB School of Management-Trieste, and Thunderbird.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.