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In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK.

By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically.

The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK.

Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK.

To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

The study aims to examine the export-led growth (ELG) hypothesis for Bangladesh. The direction of causality between export and output largely determines the success of export-oriented trade policies. A unidirectional causality running from export to output growth is required according to the narrow definition, while bidirectional causality is allowed for the broader definition. The study offers the causality inference, both from narrow and broader senses.

The study uses the bootstrap version of Toda and Yamamoto-modified causality tests, a recent development in time series econometrics, robust against the regularity conditions such as stationarity, properties of integration and cointegration and constancy of parameters. It uses monthly secondary data for the period of 1990-2014.

Test results suggest a unidirectional positive causal relationship from exports to output growth, meaning that the policies and strategies supporting exports are promoting output growth and thereby approve the ELG hypothesis for Bangladesh from the narrow sense. However, the absence of bidirectional causality between export and output growth, necessary to support the ELG hypothesis from the broader perspective, discards the conjecture that output growth is reinvigorated through the probable second-round effects of ELG produced from output growth to exports.

Lower investments in infrastructure, technology and education are reasons for the absence of ELG from the broader sense. Therefore, directing returns generated from exports for the development of technology, infrastructure and human capital, with regular and continuous revision of trade-liberalization policies so as to make its exports more competitive in the world market, will help Bangladesh trigger the second-round effect of ELG produced from output growth to exports.

Beyond the conventional approaches, this is the first contemporary time series econometrics causality analysis between export and output growth of Bangladesh, both from narrow and broader senses.

Model estimation gives students insights beyond what they can gain from textbook presentations. This paper introduces a way to make doing this easier and more effective. It introduces the program Gnu Regression, Econometrics and Time‐series Library (GRETL) which may be downloaded free of charge, and which students can place on their computers quickly and easily. Using GRETL to produce ordinary least squares (OLS) estimates is an easy, intuitive exercise. Therefore, instructors may assign such exercises without taking a large amount of time to introduce the computer and OLS estimation. GRETL, though designed to facilitate instruction, has grown into a full econometrics package that instructors can use as a research tool as well as an instructional aid.

The paper provides an overview of GRETL's accessibility and its capabilities. Next it addresses the use of GRETL for instructional purposes. Then it shows how GRETL can be used as a research tool.

The paper shows that GRETL can be a useful addition to the instructor who is showing novices how to use regression models. Also, it can be used as a research tool.

Given software like GRETL, instructors no longer need to omit model estimation because of the difficulties in accessing software and showing students how to use it.

This paper introduces a relatively new option, the use of a powerful open‐source software package to instructors in finance and accounting courses.

In this paper we try to assess how a weighted shares index and corresponding futures index respond to a change in the short‐term interest rate. Three methods are applied in analysing the data: an error correction regression method, a state space method and a neural network method. Results indicate presence of cointegration in the data set. A sensitivity analysis of each model was carried out by studying the evolution of the predictions after the studied time period, using deterministic values of the inputs. An analysis of the influence of an interest rate shock yielded interesting results. In the neural network model, again, more complicated response patterns were observed.

Investigates the doctrine of Relative Purchasing Power Parity. Mixed evidence is found supporting the concept when using a method analogous to that used by Lucas in testing the quantity theory of money. Relative Purchasing Power Parity is not consistently rejected in the long run between Canada and the United States and between Japan and the United States using quarterly data covering two separate periods: 1957 QI‐1973 QII, and 1973 QIII‐1989 QIV. Given the inconclusive results associated with relying on the methodology of Lucas, considers two alternatives: first, where the requisite smoothed time series are obtained via appropriate autoregressive integrated moving average filters and, second, where cointegration techniques are employed. In these instances, the results are unequivocal. Relative Purchasing Power Parity does not hold.

Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics.

The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform?

The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic.

This analysis may help webmasters to design successful online advertising strategies.

This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website.

The purpose of this paper is to review three papers in this issue and contribute new results on commodity futures prices and volume using wavelet analysis.

The paper uses time series econometrics including variance ratio tests, fractional integration estimators, and wavelet transforms.

The role of time horizon is emphasized in the discussion of the three papers, and wavelet methods are shown to be a useful tool to better understand time horizon-specific risk. Moreover, changes in the time horizon of futures trading are documented and discussed.

In addition to discussing three papers on quantitative finance for agricultural commodities, this paper also looks at how the analysis and management of short-term and long-term risk may differ. To this end, wavelet transform-based time series methods are reviewed and applied.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The purpose of this paper is to analyze the time‐inconsistency problem between inflation and unemployment rate series for Turkey.

The validity of the Barro‐Gordon model's implications is tested by using state‐space form and a Kalman filter. In order to investigate the long‐run effects of the time‐inconsistency problem, unit root and co‐integration tests are applied. First, a Hodrick‐Prescott filter is used to test the short‐run effects. Then the modified Barro‐Gordon model's constraint is applied to the detrended inflation and unemployment rate.

The results of this study suggest that both inflation and unemployment series are not stationary and they include the unit root, but that first differences of the two series are stationary. The co‐integration test results also do not support the Barro‐Gordon model's implications for the long‐run behavior of inflation and unemployment: the two variables are not cointegrated.

The results of this study suggest that the time‐inconsistency problem for Turkey can be valid in the short‐run, but sufficient proof cannot be found to support the Barro‐ Gordon model's implications for the long‐run.

The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013.

First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles.

Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks.

Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets.

This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.