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The study examines the existence of calendar anomalies, including the day-of-the-week (DOW) effect and the January effect, in the Stock Exchange of Thailand.

Using daily stock returns from March 2014 to March 2019, the study performs regression analysis to examine predictable patterns in stock returns, the DOW effect and the January effect, respectively.

There is strong evidence of a persistent monthly pattern and weekday seasonality in the Thai stock market. Specifically, Monday returns are negative and significantly lower than the returns on other trading days of the week, and January returns are positive and significantly higher than the returns on other months of the year.

The findings offer managerial implications for investors seeking trading strategies to maximize the possibility of reaching investment goals and inform policymakers regarding the current state of the Thai stock market.

First, the study investigates calendar anomalies in the Thai stock market, specifically the DOW effect and the January effect, which have received relatively little attention in the literature. Second, this is the first study to examine calendar anomalies in the Thai stock market across different groups of companies and stock trading characteristics using a range of composite indexes. Furthermore, the study uses data during the period 2014–2019, which should provide up-to-date information on the patterns of stock returns in Thailand.

The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a scientific pricing framework.

The generating process of the historical loss cost ratio's (LCR's) are reviewed, and the Erlang mixture distribution is proposed. A modified credibility approach is developed based on the Erlang mixture distribution and the liability weighted LCR, and information from the observed data of the individual region/province is integrated with the collective experience of the entire crop reinsurance program in Canada.

A comprehensive data set representing the entire crop insurance sector in Canada is used to show that the Erlang mixture distribution captures the tails of the data more accurately compared to conventional distributions. Further, the heterogeneous credibility premium based on the liability weighted LCR's is more conservative, and provides a more scientific approach to enhance the reinsurance pricing.

Credibility models are in the early stages of application in the area of agriculture insurance, therefore, the credibility models presented in this paper could be verified with data from other geographical regions.

The credibility-based Erlang mixture model proposed in this paper should be useful for crop insurers and reinsurers to enhance their ratemaking frameworks.

This is the first paper to introduce the Erlang mixture model in the context of agricultural risk modeling. Two modified versions of the Bühlmann-Straub credibility model are also presented based on the liability weighted LCR to enhance the reinsurance pricing framework.

This paper aims to investigate the presence of the Fisher effect for the USA from a new methodological perspective differing it from all previous studies using the common linear representation of the Fisher equation.

The nonlinear ARDL model, recently developed by Shin et al. (2014), is applied for the 10-year US Government bond rates over the period of 1985M1-2017M10.

The empirical findings indicate that the US Federal Reserve (FED) is a more predominant arbiter in the determination of interest rates during periods of declining inflation rates than periods of rising inflation rates. This finding may allow the FED to apply more proactive and prudent monetary policy. Additionally, this study newly describes and introduces a different version of the partial Fisher effect and extends the Fisher equation to some degree in terms of the partial Fisher effect.

To the best the authors’ knowledge, this method is applied for the first time in testing the Fisher effect for the USA.

The purpose of this paper is to investigate the validity of the cost of carry model by examining the time series properties of the deviation between future and spot prices in the European Union Emissions Trading Scheme (EU-ETS) over the time period 2005-2012. The paper utilizes a non-linear mean reverting adjustment mechanism, and discovers that although deviations of future from spot prices can exhibit a region of non-stationary behaviour, overall they are stationary indicating market efficiency in the trading of carbon permits.

The methodology involves non-linear mean reverting unit root tests.

The findings provide insights into the functioning of the EU-ETS market. They suggest that it is informationally efficient and does not permit arbitrage between spots and futures.

The authors are the first study to examine efficiency in the EU-ETS by investigating the validity of the cost of carry model. The authors are also the only study to look at efficiency in both Phase I and Phase II of the scheme.