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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 this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

This present work aims at looking into whether or not introducing commodities in international equity portfolios helps reduce the market risk and if hedging is carried out with the same effectiveness across different regional stock markets.

The authors determine the optimal hedge ratios and hedging effectiveness of a number of commodity-hedged emerging and developed equity markets, using three versions of MGARCH model: DCC, ADCC and GO-GARCH. The authors also use a rolling window estimation procedure for the purpose of constructing out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios.

Empirical results evince that commodities significantly display effective risk-reducing hedge instruments in short and long runs. The main finding is that commodities do not seem to hedge regional stock markets in the same way. They tend to provide evidence of a rather effective hedging regarding mainly the East European and Latin American stock markets.

The authors study whether commodities can hedge stock markets at regional context and if hedging effectiveness differ from one region to another.

Keeping in view the robust growth of Islamic banking around the globe, this study aims to comparatively analyze the association between liquidity creation and liquidity risk for Islamic banks (IBANs) and conventional banks (CBANs) in Pakistan and Malaysia over a period of 2004–2021. The moderating role of bank loan concentration on the aforementioned relationship is also studied.

Regression estimation methods such as fixed effect, random effect and generalized least square are deployed for obtaining results. Liquidity creation Burger Bouwman measure (cat fat and noncat fat) and Basel-III liquidity risk measure (liquidity coverage ratio) are also used.

The results give us insight that liquidity creation is positively and significantly related to liquidity risk in both IBANs and CBANs of Pakistan and Malaysia. This relationship has been moderated negatively (reversed) and significantly by credit concentration showing the importance of risk management and loan portfolio concentration.

It is analyzed that during the process of liquidity creation, IBANs in Pakistan faced more liquidity risk for both on and off-balance sheet transactions in the presence of moderation of loan concentration than IBANs in Malaysia necessitating strategic policy-making for important aspects of liquidity risk management and loan concentration while creating liquidity.

Such studies comparing IBANs and CBANs comparison keeping in view liquidity creation, liquidity risk and loan concentration are either limited or nonexistent.

Although it is acknowledged that entrepreneurial networks play a crucial role in fostering business model innovation (BMI) for start-ups, it is unclear how and when these networks affect BMI. This research developed a moderated mediation model to explore the impact of entrepreneurial networks on BMI in start-ups and examined the dual mediating effects of causation and effectuation, as well as the moderation of environmental dynamism.

The proposed framework was tested by hierarchical regression analyses and bootstrapping using samples of 248 start-ups in China.

The results showed that entrepreneurial networks significantly positively impacted start-up BMI. Causation and effectuation played dual mediating roles between entrepreneurial networks and BMI. Furthermore, the entrepreneurial networks-effectuation-BMI association was more substantial in highly dynamic environments, whereas the entrepreneurial networks-causation-BMI relationship was unaffected.

There are several theoretical contributions resulting from this research. The findings offer new insights for understanding the antecedents of start-up BMI from the network perspective. This research adds to the growing literature on resource orchestration (RO) by exploring the dual mediating influences of causation and effectuation in resource management. This investigation revealed the boundary condition between entrepreneurial networks and BMI by testing the moderating influence of environmental dynamism.

Start-ups must effectively use external resources embedded within networks to advance BMI. Start-up entrepreneurs should apply causation and effectuation to transform entrepreneurial network resources into BMI. Start-up entrepreneurs must dynamically manage resources in response to ever-changing environmental conditions. Resource acquisition and management of entrepreneurial networks can vary significantly in their influence on start-up BMI under different environmental contexts.

Unlike previous BMI research focused on internal organizational factors, this study highlighted the critical importance of entrepreneurial networks as a prerequisite for achieving start-up BMI, contributing to the literature on open innovation and resource-based view. Examining the dual mediating roles of causation and effectuation illustrated the bridging role of strategic decision-making logic in connecting resources to value creation, contributing to the developing RO literature. The moderating influence of environmental dynamism was explored, clarifying how start-up BMI benefits from entrepreneurial networks in differing situations. A framework for reconciling contradictory findings concerning the association between entrepreneurial networks and innovation is provided.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.

The wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.

Results of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.

This study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

Biotech companies stand as key actors in pharmaceutical innovation. The high risk and long timelines inherent with their R&D investments might hinder their access to funding, potentially stifling innovation. This study aims to explore into the appeal of biotech companies to capital market investors, whose financial backing could bolster the growth of the biotechnology sector.

This paper uses a dataset of 774 US publicly listed biotech firms to investigate their risk and return characteristics by comparing them to pharmaceutical firms and a sample of matched non-biotech R&D-intensive firms over the sample period 1980–2021. Tests show that the conclusions remain consistent across diverse methodological approaches.

The paper shows that biotech companies are riskier than the average firm in the market index but outperform on a risk-adjusted basis both the market and a matched group of R&D-intensive firms. This is particularly true for large capitalization biotech, which is also shown to provide a diversification benefit by reducing the downside risk in past crisis periods.

This paper provides insight relevant to the current debate about the overall performance of the biotech industry in terms of policy changes and their impact on small, early-stage biotech firms. While small and early-stage biotech firms are playing an increasing role in scientific innovation, this study confirms their greater vulnerability to financial risks and the importance of access to capital markets in enabling those companies to survive and evolve into larger biotech.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.