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Estimates the output‐oriented and input‐specific technical efficiency in two samples of Greek, durum wheat farms – organic and conventional ones – using Kalirajan and Obwona’s stochastic varying coefficient regression model. Findings indicate that the organic wheat farms examined are relatively more efficient. Reasons may include lower profit margins and restrictions on inputs permitted, which may force organic farmers to be more cautious with input use. However, technical efficiency scores are still relatively low for both types of wheat farming. Therefore, considerable scope for cost reducing and farm income improvement may exist in both farming modes. This realization could prove crucial for the long‐run viability and the future course of organic wheat farming.

This paper aims to introduce a new indicator to measure redenomination risks in Euro area countries. The measure is based on survey data. The influence of this indicator in determining sovereign bond yield spreads is estimated.

An autoregressive distributed lag approach is used to estimate the effects of redenomination risks on sovereign bond yields. Additional control variables are added.

The results for 10 European Economic and Monetary Union (EMU) countries in the period June 2012 to May 2019 show that the risk of depreciation is almost abandoned for most Euro area countries, i.e. the former crisis countries Ireland and Portugal. If anything an appreciation may occur for some countries once they leave the EMU. The only countries facing depreciation problems once leaving the monetary union are Italy and to some extent Spain.

With this new indicator, the literature on sovereign bond determination and i.e. on redenomination risks is expanded by an additional approach. Moreover, this study is one of few also looking at the period after the most severe tensions of the sovereign debt crisis in the Euro area in 2012.

In this chapter, we consider the nonparametric estimation of the average treatment effect (ATE) based on direct estimation of the conditional treatment effect. We establish the asymptotic distribution of the proposed ATE estimator. We also consider consistent testing for a parametric functional form for the conditional treatment effect function. A small-scale Monte Carlo simulation study is reported to examine the finite sample performance of the proposed estimator.

No previous research has considered the changing agglomeration effect of foreign direct investment (FDI). The purpose of this paper is to fill the gap in the literature.

The paper uses China as the object of study and examines the centripetal and centrifugal forces associated with FDI clustering over time.

Through studying the FDI determinants for the 29 Chinese provinces from 1993 to 2008, the empirical analysis supports a weakening agglomeration effect of FDI over time in China and further suggests that the effect has nearly vanished in the past few years.

Data availability restricts the analysis to using provincial aggregate data and so further research is called for. It would provide more accurate and insightful information to study the FDI agglomeration effects at a finer level, using more disaggregated city‐level data by sector and by source country.

As the Chinese government has been making efforts to direct FDI to inland areas, this research provides immediate policy implications. Policy‐makers' investment incentives to direct FDI could go to waste when the agglomeration effect of FDI is too strong. The incentives should be able to achieve a much larger effect when the agglomeration effect becomes less strong.

The purpose of the study is to examine the moderating impact of absorptive capacity on the foreign direct investment (FDI)–growth link using the data for the period 1995–2019.

The authors apply the autoregressive distributed lag (ARDL) model and threshold analysis for empirical analysis.

The findings indicate that the link between FDI and economic growth is influenced indirectly by absorptive capacities, such as financial development, institutional quality, technological capability, and trade openness. However, while examining the linear FDI–growth nexus, the authors noticed that human capital and infrastructure did not affect the relationship; when the non-linearity in the link is considered, the authors noted that all absorptive capacities (including human capital and infrastructure), when interacted with FDI, have a positive effect on growth. Furthermore, FDI stimulates growth if the absorptive capacities have exceeded a certain threshold level.

From a practical standpoint, it is reasonable to conclude that improving absorptive capacities is critical in order to perceive FDI as a growth driver.

India has been able to position itself as a preferred destination for FDI (when the major economies are facing a sharp decline in FDI inflows) despite the Covid-19 pandemic. However, it still suffers from low growth. Although much of the literature admits that absorptive capacity is crucial for FDI to promote growth, no study in the case of India examines FDI–growth nexus conditioned upon absorptive capacity. Moreover, the authors have used threshold analysis for assessing the non-linearities in FDI–growth nexus contingent on absorptive capacity.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The purpose of this paper is to examine the nature and extent of instability of capital asset pricing model (CAPM) beta in a small emerging capital market.

Inter‐period as well as intra beta instability are examined. Inter‐period instability is examined by Mann‐Whitney z‐scores and Blume's regressions. Intra‐period beta instability is examined using Bruesch‐Pagan LM test and Chow break point test. Robustness tests are performed applying time‐varying parameter models.

Beta instability increases with increase in holding (sample) periods. There is evidence of inter‐period as well as intra‐period beta instability. Analysis of the full eight‐year interval reveals a very high incidence of beta instability, namely, about 26 per cent of the individual stocks tested and about 31 per cent of individual stocks have structural break. The extent of beta instability does not significantly decline when corrected for non‐synchronous trading and thin trading as represented by Dimson beta. However, the extent of beta instability is similar to that of developed market. Time‐varying parameter model under Kalman filter approach using AR(1) specification performs better than any other models in terms of in‐sample forecast errors. Dominance of AR(1) approach suggests that stock betas in DSE are time varying, and shocks to the conditional beta have some degree of persistence which ultimately reverts to a mean. This result is in contrast to the findings of Faff et al. revealing the dominance of Random Walk specification in Australian market, suggesting that shocks to stock beta in Australian market persist indefinitely into the future. These contrasting findings may indicate that beta instability in different markets and for different stocks in the same market are of different nature and different models may be suitable for different markets and different stocks in the same market in capturing the time‐varying nature of beta coefficients.

This study covers only 110 stocks of Dhaka Stock Exchange. It can be extended to include more stocks. The study can also be done in other developing markets.

While the issues of beta instability have been extensively explored for developed markets, evidence for emerging markets is less readily available. The present study contributes to the emerging market literature on beta instability by investigating the extent of beta instability and its time‐varying properties in Dhaka Stock Exchange (DSE), Bangladesh. Understanding the systematic risk behaviour of individual stocks in DSE is important for both local and international investors. With the saturation of investment opportunities in developed markets due to their high integration, and with the enhanced deregulation and liberalization of emerging economies, emerging financial markets like DSE provide suitable and a relatively safe investment environment for international investors and fund managers seeking global diversification for better risk‐return trade‐offs. When most of the world markets declined during the recent global financial crisis, stock prices in DSE experienced a continuous rise. This makes it more interesting as an emerging market to study beta instability.

The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of foreign currencies during the Covid-19 pandemic.

The authors consider a multivariate time series model where marginal dynamics are driven by an autoregressive moving average (ARMA)–Glosten-Jagannathan-Runkle–generalized autoregressive conditional heteroscedastic (GARCH) model, and the dependence structure among the residuals is given by an elliptical copula function. The correlation coefficient follows an autoregressive equation where the autoregressive coefficient is a function of the past values of the correlation. The model is applied to a portfolio of a couple of exchange rates, specifically US dollar–Japanese Yen and US dollar–Euro and compared with two alternative specifications of the correlation coefficient: constant and with autoregressive dynamics.

The use of the new model results in a more conservative evaluation of the tail risk of the portfolio measured by the value-at-risk and the expected shortfall suggesting a more prudential capital allocation policy.

The main contribution of the paper consists in the introduction of a time-varying correlation model where the past values of the correlation coefficient impact on the autoregressive structure.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.