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The purpose of this paper is to investigate the profitability of European Union (EU) firms with the aim of confirming the mean‐reverting pattern documented by earlier research in the USA. In addition, the paper classifies firms by industry sectors across countries to investigate potential differences.

The paper follows closely a model where the forecasting of profitability is done through year‐by‐year regressions. This approach allows the use of large samples and the year‐by‐year variation in the slopes. Both a linear and a nonlinear partial adjustment models are used for forecasting profitability.

Findings show that the profitability does follow a mean‐reverting process and that profitability forecasting can be improved substantially by exploiting the mean‐reverting feature. Further analysis shows that mean reversion does not play an important role in EU countries as in the USA and there is no evidence of nonlinearity in mean reversion. It was also found that mean‐reverting speed differ across industries, with utilities, financial and manufacturing among the lowest.

The sample companies are not originated from a single economy, but from 15 different countries with different macro‐economic conditions that might influence their profitability.

Studying the European market, where the institutional and financial structure of firms are different from the USA allows us to observe whether the US results are sample specific or can be generalized and applied elsewhere. The difference observed in these sample results is probably due to the fact that the US economy is more competitive than that of EU.

Forward rates in the money market are systematically higher than realised spot rates, reflecting an unobservable term premium. This paper uses a Kalman filter specification to produce time‐varying estimates of the term premia in New Zealand and Australia. Three time series specifications are used to examine the properties of the premia, such as the average size, volatility, and the degree of mean reversion. Compared to the constant term premia estimates, the time‐varying estimates explain significantly more of the difference between forward and spot rates. The results suggest that the premium in New Zealand is slowly mean‐reverting, while the Australian premium reverts quickly to the mean. It is not clear whether the method of monetary policy implementation affects the term premium, although in New Zealand the premium has been smaller and less variable since the introduction of the Official Cash Rate in March 1999. A related finding is that the size of the term premium is correlated with the volatility of short‐term rates.

This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios.

The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics.

The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations.

The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.

The purpose of this study is to analyze the overnight and intraday returns of the most traded American Depositary Receipts (ADRs) of Asian companies, understand the different levels of volatilities realized in these asynchronous markets and develop trading strategies based on empirical findings.

This study presents an empirical analysis on the overnight and intraday returns of Asian ADRs. The authors propose a measure to quantify the relative contributions of the intraday and overnight returns to the ADR's total volatility. Furthermore, the return difference between S&P500 index and each ADR is fitted to an Ornstein–Uhlenbeck model via maximum-likelihood estimation.

This study finds that ADRs' overnight returns are more volatile, whereas the intraday returns are significantly more strongly correlated with the US market returns. The return spreads between the S&P500 and ADRs are found to be a mean-reverting time series and motivate a pairs trading strategy.

The methodology used in this study is not limited to Asian ADRs and can be adapted to analyze the overnight and intraday returns of other non-Asian ADRs and stocks.

Investors should be aware of the overnight price fluctuations while intraday traders may consider strategies that capture the mean-reverting return spread between an ADR (or an Exchange-Traded Funds [ETF] of Asian stocks) and the S&P500 index ETF (SPY).

ADRs are among the most popular securities for investing in foreign (non-US) companies. The total global investments in ADRs are estimated to be close to US$1tn. Understanding the risks of ADRs is important to not only individual/institutional investors but also regulators.

This study provides a new measure to quantify and compare the relative contributions of volatility by overnight and intraday returns. Optimized pairs trading strategies involving ADRs and ETFs are developed and backtested.

The purpose of this paper is to demonstrate how managerial estimates of long-term market price trends can be included into investment analysis of metal mining. The inclusion of subjective market information with a new cycle reverting price process is proposed.

Subjective managerial estimates are included into stochastic metal price modeling by defining separately the following parameters of each price cycle phase: approximated length, approximated long-term price level and volatility. An net present value-based investment analysis model is applied together with Monte Carlo simulation.

It is plausible to combine managerial estimates about metal price trends and cycles with stochastic modeling for shorter term and to include the information into investment analysis. The results show that the difference between the proposed process and the commonly used mean reverting process is remarkable in terms of decision-making implications.

The proposed method allows the inclusion of more relevant information into the metal price modeling used in mining investment analysis. Results suggest that the cyclical nature of metal prices affects project value of metal mining projects, and it should be considered when making irreversible investment decisions. The proposed method can be generalized for any cyclical processes.

The volatility of a financial asset is an important input for financial decision‐making in the context of asset allocation, option pricing, and risk management. The authors compare and contrast four approaches to stochastic volatility to determine which is most appropriate to each of these various needs.

This paper aims to examine two hypotheses that have not been well investigated in the existing literature. One hypothesis is that the real interest rates of industrial countries tend to be mean‐reverting during the current floating exchange rate period. Another hypothesis is that the real interest rates of the countries involved in forming the Euro area are more likely to behave as nonlinear stationary series than those of other industrial countries.

The study applies the conventional linear unit root tests and recently developed nonlinear unit root tests, as well as the tests of specifying nonlinearity in time series, to the short‐term real interest rates of 16 industrial countries.

The results of the study provide support for both hypotheses.

The results imply that, having adopted target‐zone type stabilization policies for years, the central banks of European Monetary Union (EMU) countries were likely to have exercised monetary policies in a nonlinear way, especially in the process of meeting the requirements of joining EMU.

The study provides stronger evidence than previous studies for the theory that real interest rates of industrial countries tend to have mean‐reverting behavior. The study suggests that more active monetary policies for inflation control in the floating exchange rate period may have enhanced mean reversion in real interest rates.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

A widely accepted belief indicates that terror activities have negative impact on stock markets. Contrary to numerous empirical studies, the purpose of this paper is to consider this issue from another point of view in the sense that markets can become desensitized to terror.

Here, instead of directly analyzing the existing data, the stochastic nature of the events is taken into consideration.

The author compares three countries and found out that the correlation between terror and stock markets is almost nil when terror events become a commonplace.

This paper applies mean reverting stochastic processes to terror incidents and brings out interesting results.

Reviews previous research on the nature of beta and investigates the stochastic structure of time‐varying beta in Hong Kong, Malaysia and Singapore using the bi‐variate GARCH‐in‐mean model and fractional tests. Develops mathematical models and applies them to 1989‐1998 daily data from all three stock markets. Presents the results, which suggest, in contrast to other findings, that all three time‐varying betas are slowly mean‐reverting (long memory).