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If the only difference between cities lies in their initial housing prices, the initially lower-price cities should eventually catch up with the initially higher-price ones, i.e., “absolute convergence.” Alternatively, if the major determinants of housing prices are city-specific, cities will converge to parallel growth paths of housing prices, i.e., “conditional convergence.” This study tests for the existence of absolute and conditional convergence in house prices among cities in China. The strong evidence for conditional convergence suggests that each city possesses its own steady-state housing price to which it is converging, which depends on the city's own socio-economic characteristics. In other words, differences in these socio-economic characteristics among cities can create permanent differences in housing price among them. The differences in steady-states house price across cities reflect differences in the level of socio-economic development among them. The findings inform the kinds of interventions and resources that are most likely to be effective in reducing income disparity.

Holbrook Working (1949) discovered that the percentage change in futures prices seemed to be largelyrandom. This led Paul Samuelson (1965) to develop the Efficient Market Hypothesis (EMH) which claims that the current spot and futures1 prices fully reflect all relevant information. Furthermore, because the future flow of information cannot be anticipated, price changes will not be serially correlated. These papers linked the notion of randomness of price changes to informational efficiency. From that point on, a major part of the empirical studies of asset markets has been the application of time series analysis to asset prices, in order to evaluate whether the price changes are random and whether futures prices reflect all available information. As the statistical tests became more sophisticated, the number of empirical studies increased and the results became more contradictory and difficult to interpret. An economic theorist can only be bemused by contemplating the empirical/econometric studies in the finance literature.

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.

This study aims to examine what underlies the estimated relation between idiosyncratic volatility and realized return.

Idiosyncratic volatility has a dual effect on stock pricing: it not only affects investors' expected return but also affects the efficiency of stock price in reflecting its value. Therefore, the estimated relation between idiosyncratic volatility and realized return captures its relations with both expected return and the mispricing-related component due to its dual effect on stock pricing. The sign of its relation with the mispricing-related component is indeterminate.

The estimated relation between idiosyncratic volatility and realized return decreases and switches from positive to negative as the estimation sample consists of proportionately more ex ante overvalued observations; it increases and switches from negative to positive as the estimation sample consists of proportionately more ex post overvalued observations. In sum, the relation of idiosyncratic volatility with the mispricing-related component dominates its relation with expected return in its estimated relation with realized return. Moreover, its estimated relation with realized return varies with research design choices and even switches sign due to their effects on its relation with the mispricing-related component.

The novelty of the study is evident in the implication of its findings that one cannot infer the sign of the relation of idiosyncratic volatility with expected return from its estimated relation with realized return.

This research aims to examine the impacts of the Russia–Ukraine war on the welfare of Indonesian palm oil producers and consumers.

The research uses time-series data from 2000 to 2022 for inferential, simulation and descriptive analyses, coupled with a world vegetable oil trade model in the form of a system of simultaneous equations. The two-stage least squares (2SLS) estimation method with 37 simultaneous equations is used to estimate parameters.

The research results show that the Russia–Ukraine conflict has led to an increase in the welfare of Indonesian palm oil producers and consumers. A combination of domestic policies (replanting, export tax, mandatory biodiesel and domestic market obligation) has boosted Indonesian palm oil industry players welfare after the Russia–Ukraine conflict. On the other hand, it caused a decline in the welfare of cooking oil and biodiesel producers due to the surge in raw material costs and the decline in selling prices. Therefore, market differentiation for palm oil derivative products into international markets is required.

The results of this research provide empirical evidence to the Indonesian government regarding the importance of policies that regulate the demand and supply of palm oil compared to increasing palm oil production. The results of this research emphasize that the development of policies for the Indonesian palm oil industry needs to pay attention to global vegetable oil market fluctuations as well as the importance of holistic policies from the on-farm to the off-farm level to ensure that producers and consumers experience increased welfare.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2024-0531

To the best of the authors’ knowledge, this study is the first to investigate the intricate relationship between crude oil spot and futures prices, focusing on both cointegration and market efficiency during the COVID-19 pandemic, and the beginning of the Russia–Ukraine conflict. Using daily West Texas Intermediate data from January 2020 to March 2024, like Cunado and Pérez de Gracia (2003), the authors use advanced statistical methods to identify structural breaks and assess cointegration levels. Linear and nonlinear Granger causality tests are used to reveal underlying dynamics.

This paper uses the Lagrange Multiplier test by Arai and Kurozumi (2007) to check for cointegration with various shifts in crude oil spot and futures markets. The two-step procedure by Kejriwal and Perron (2010) and Kejriwal et al. (2022) is then applied to assess partial parameter stability in cointegration models. Efficiency is examined using both bivariate and trivariate models based on non-arbitrage and expectations hypotheses. Finally, causality is analyzed with the vector error correction model for linear Granger causality, and the tests by Bai et al. (2018) and Diks and Panchenko (2006) for nonlinear causality.

The analysis reveals that futures prices generally lead spot prices through both linear and nonlinear causality during certain periods, while only linear causality is present in others. This inconsistency suggests fluctuating market efficiency and potential arbitrage opportunities. Structural breaks indicate that the equilibrium between spot and futures prices adjusts in response to significant events like the COVID-19 pandemic and the Russia–Ukraine war. The study identifies specific periods, particularly between January 2020 and March 2024, where both linear and nonlinear forecasting between futures and spot oil prices are effective, highlighting the dynamic nature of their relationship.

Despite extensive efforts, pinpointing the exact break date for COVID-19 remains challenging due to limitations in the data set and methodology. Additionally, the analysis of the Russia–Ukraine conflict is still ongoing. These challenges highlight the complexity of addressing structural breaks linked to unprecedented events.

The findings offer valuable insights for both academia and industry practitioners. The study reveals potential arbitrage opportunities stemming from inconsistent market efficiency and fluctuating causality between futures and spot prices, allowing traders to optimize their trades and timing. It also enhances risk management by identifying when linear and nonlinear causality is most effective. Policymakers can use these insights to evaluate market stability, especially during major disruptions such as the COVID-19 pandemic and geopolitical conflicts, guiding regulatory decisions. Furthermore, the study highlights the importance for investors to adjust their strategies in response to structural breaks and evolving market conditions.

This study’s social implications are diverse, extending beyond finance and academia. It influences economic stability by revealing inefficiencies and arbitrage opportunities in crude oil markets, aiding better resource allocation. Enhanced transparency benefits stakeholders, promoting fair market practices and consumer protection. Policymakers can refine regulations based on identified structural breaks, ensuring market stability. The study indirectly impacts environmental discussions by examining crude oil’s link to global energy consumption. Financially, it guides investment strategies, influencing resource distribution and the broader economy. Additionally, its educational contribution stimulates academic discourse, fostering growth in energy economics and financial market knowledge, shaping future research.

The originality and value of this paper lie in its comprehensive examination of the dynamic relationship between futures and spot oil prices, particularly through both linear and nonlinear causality across different periods. By identifying and analyzing periods of both linear and nonlinear causality, the study uncovers fluctuating market efficiency and potential arbitrage opportunities that are not typically addressed in conventional analyses. Additionally, the paper’s focus on the impact of significant global events, such as the COVID-19 pandemic and the Russia–Ukraine war, on the equilibrium between spot and futures prices offers a novel perspective on how structural breaks influence market dynamics. This nuanced understanding enhances both theoretical and practical knowledge, offering valuable insights for traders, investors and policymakers to navigate and respond to evolving market conditions.

The standard approach in measuring demand responses and consumer preferences assumes particular parametric models for the consumer preferences and demand functions, and subsequently fits these models to observed data. In principle, the estimated demand models can then be used (i) to test consistency of the data with the theory of consumer behavior, (ii) to infer consumer preferences, and (iii) to predict the consumer's response to, say, new prices following a policy reform. This chapter focuses on an alternative, nonparametric approach. More specifically, we review methods that tackle the earlier issues by merely starting from a minimal set of so-called revealed preference axioms. In contrast to the standard approach, this revealed preference approach avoids the use of parametric models for preferences or demand. The structure of the chapter is as follows. First, we introduce the basic concepts of the revealed preference approach to model consumer demand. Next, we consider issues like goodness-of-fit, power, and measurement error, which are important in the context of empirical applications. Finally, we review a number of interesting extensions of the revealed preference approach, which deal with characteristics models, habit-formation, and the collective model.

How well does the “Law of One Price” operate across countries? Interest in this question has been stimulated by the Monetary Aproach to the Balance of Payments (Frenkel and Johnson (1975)) which uses the law to determine the price of traded goods in open economies.

Massively multiplayer online role-playing games (MMORPGs) create quasi-real social systems in which players can interact with one another, and quasi-real economic systems where players can consume and trade in-game items with virtual currency. The in-game currency price, an important indicator of a virtual economy, is highly contingent on players’ behavioral interaction in MMORPGs. The purpose of this paper is to adopt a network perspective to examine how topological characteristics of social networks in an MMORPG, namely, network externalities, density, and closure, would exert impacts on the in-game currency price.

Players’ behavioral data were collected from a popular MMORPG in China on a weekly basis for 52 weeks. With a time series analytical approach, the empirical model for the price function of in-game currency was estimated with vector autoregression.

The results show that the number of core avatars and network density are positively associated with in-game currency price, while network closure has a negative effect on in-game currency price. However, in-game currency price is found to have no significant relationship with the trade volume of the currency.

This study fills in an important research gap by investigating factors influencing the in-game currency price of MMORPGs from a network perspective, which contributes to the existing literature of network effects and advances our understanding about how players’ interaction will influence the dynamics of a virtual economy. The findings could offer useful insights for online game companies to better understand their players’ social interaction and consumption behavior.