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The relationship between idiosyncratic risk and stock return has been debated for decades; this study reexamined this relationship in the Pakistani stock market by using the quantile regression approach along with the prospect theory.

The present study is quantitative, and secondary data obtained from an emerging market are used. The quantile regression method allows the estimates of idiosyncratic risk to vary across the entire distribution of stock returns, i.e. the dependent variable. In this study, the standard deviation of regression residuals from the Fama and French three-factor model was used to measure idiosyncratic risk. Convenience sampling is employed; the sample consists of 82 firms listed on the KSE-100 index, with 820 annual observations for the ten years from 2011 to 2020. After computing results by using quantile regression, the study's findings, ordinary least squares (OLS) and least sum of absolute deviation (LAD) regression techniques are also compared.

The quantile regression estimation results indicate that idiosyncratic risk is positively correlated with stock returns and that this relationship is contingent on whether prices are rising or falling. Consistent with the prospect theory, the finding suggests that stock investors tend to avoid risk when they anticipate a loss but are more willing to take risks when they anticipate a profit. The results of the OLS and LAD regressions indicate that the method typically employed in previous studies does not adequately describe the relationship between idiosyncratic risk and stock return at extreme points or across the entire distribution of stock return.

These empirical findings shed new light on the relationship between idiosyncratic risk and stock return in Pakistani stock market literature.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

This paper aims to investigate whether the establishment of commodity futures can effectively hedge systemic risk in the spot network, given the context of financialization in the commodity futures market.

Utilizing industry association data from the Chinese commodity market, the authors identify systemically important commodities based on their importance in the production process using multiple graph analysis methods. Then the authors analyze the effect of listing futures on the systemic risk in the spot market with the staggered difference-in-differences (DID) method.

The findings suggest that futures contracts help reduce systemic risks in the underlying spot network. Systemic risk for a commodity will decrease by approximately 5.7% with the introduction of each corresponding futures contract, since the hedging function of futures reduces the timing behavior of firms in the spot market. Establishing futures contracts for upstream commodities lowers systemic risks for downstream commodities. Energy commodities, such as crude oil and coal, have higher systemic importance, with the energy sector dominating systemic importance, while some chemical commodities also have considerable systemic importance. Meanwhile, the shortest transmission path for risk propagation is composed of the energy industry, chemical industry, agriculture/metal industry and final products.

The paper provides the following policy insights: (1) The role of futures contracts is still positive, and future contracts should be established upstream and at more systemically important nodes in the spot production chain. (2) More attention should be paid to the chemical industry chain, as some chemical commodities are systemically important but do not have corresponding futures contracts. (3) The risk source of the commodity spot market network is the energy industry, and therefore, energy-related commodities should continue to be closely monitored.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

Farmers' risk attitudes and risk perceptions play an essential role in shaping risk management strategies to address risks and uncertainties. Contract farming is considered as one of the feasible approaches to tackle farmers' concerns. However, risk perspectives under various categories have not been included in studies on farmers' preferences for contract farming in the literature, especially in Vietnam. This study aims to determine factors affecting farmers' choices of different contract farming practices.

The explanatory factor analysis (EFA) and multinomial logit model (MNL) were applied to explore the impacts of risk perspectives on farmers' preferences for contract farming. Data have been collected from 211 rice farmers in An Giang Province, “the rice bowl” of the Mekong Delta, Vietnam.

The study found that farm size, cooperatives, extension, market access and trust have significantly impacted on contract participation while a delay payment was a barrier for farmers' motivation to opt for the contract. Farmers' contract choices were also influenced by their risk attitudes and perceptions under different risk dimensions. The financial, policy and human risk-averse behavior predisposed farmers to single out the full contract while the policy and human risk-loving and production, market and finance risk-averse respondents were in favor of the marketing contract. Moreover, the findings indicated that the more farmers concerned about risk of weather and market, the more choices for the full contract, whereas the risk perceptions of weather and policy encouraged farmers to use the limited contract. By contrast, farmers who perceived the impacts of risk of diseases/pests and human were likely to adopt the marketing contract.

This study just focuses on collecting data from farmers’ perspective. Future studies involving stakeholders such as enterprises and policy makers are strongly recommended so as to design suitable contracts and enforce contract schemes effectively in Vietnam.

The findings also contribute to the literature on different types of contracts and the multidimensional aspect of risk for rice production in Vietnam.

This study aims to explore the adoption of enterprise risk management (ERM) in developing and developed countries. Is there a similarity or difference between the two contrasting institutional markets and the reasons behind them?

The adoption of ERM is analyzed on the basis of the institutional framework. The author draws empirical evidence by comparing the cases of a British and an Indian insurance company using evidence from multiple sources. This paper focuses on extra-organizational pressures exerted by economic, social and political situations across two countries that influenced the adoption decision of ERM.

The findings of this research revealed that early adopters of ERM in different institutional markets face coercive and normative pressure but not mimetic pressure. The adoption of ERM in India and the UK is dissimilar. Companies in the British insurance market encounter higher institutional forces than those in the Indian market because of higher coercive and normative pressure. The aspirations to adopt ERM in the Indian and UK markets included improved strategic decision-making to maintain stakeholder expectations and higher standards of corporate governance. In the UK, ERM was adopted to reduce surprises and fluctuations under flexible regulations but with stricter adoption and to improve credit ratings.

Previous literature has discussed ERM adoption in similar markets or within one market with similar institutional pressure. In contrast, this research is a comparative study that explains the analysis of institutional theory in two different institutional environments in the adoption of ERM.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Many studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.

This is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.

The study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.

The empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.

This is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

The purpose of the paper is to assess the systemic risk in the new energy stock markets of China.

This paper first uses the VaR method to study individual stock market risks. It then introduces the DCC model to capture the dynamic conditional correlation among the new energy stock markets.

The paper shows a generally upward trend of the stock market risk over time in the recent decade. Among all the markets considered, the solar power market demonstrates the highest risk, closely followed by the wind power market, while the hydropower market exhibits the lowest risk. Furthermore, the average dynamic conditional correlations among the new energy markets stay high during the period under investigation though daily correlations vary and significantly declined in 2020.

To the best of the authors’ knowledge, this paper is the first of its kind to study the systemic risk within the new energy stock market context. In addition, it not only investigates individual new energy stock market risks but also examines the dynamic linkages among those markets, thus providing comprehensive and unprecedented evidence of systemic risk in China new energy markets, which have useful implications for both regulators and investors.

This study aims to document the time varying risk premia for market, size, value and momentum factors for an emerging market using a sophisticated conditional asset pricing model. The focus of this study is Turkish stock market denominated in local currency with its peculiar risk premia.

The authors employ Gagliardini et al.'s (2016) econometric method that uses cross-sectional and time series information simultaneously to infer the path of risk premia from individual stocks.

Using this methodology, the authors assess several conditioning information and conclude that local dividend yield, inflation and exchange rates have the most explanatory power. The authors document the time varying risk premia in Turkey over three decades.

Existing studies on dynamic estimation of risk premia lack a consensus as to which state variables should be included and to what extent they impact the magnitude of the premium. The authors extend the conditioning information set beyond the ones existing in the literature to determine variables that are specifically important for an emerging market.