The financial events occurrence including the crisis and pandemics increased the panic in the global financial markets. Investors are in trouble for investment due to the cointegration pattern between the global markets in the period of these events. Investors belongs to different regions want to minimize their risk at given expected return. Some previous research provides the solution of the investors troubles by investigating different factors that affect the investment risk and return in the portfolio. This study provides a wide range of investment strategy that is helpful for the investors to minimize the portfolio risk by taking the cointegration or co-movement pattern in mind while preparing the effective portfolio. We used the dynamic conditional variance, correlation, covariance, dynamic portfolio weight, hedge ratio and hedge effectiveness for all selected markets. The financial crisis in the global economy effects the volatility of the return. Based on the portfolio weight, investors should increase their investment any portfolio that is favourbale provided by the study during the period of crisis and pandemics. Moreover, our findings shows that all hedge ratios are higher at the period of different financial crisis as well as pandemic COVID-19 also. The results of the hedge effectiveness reveals that the risk-adjustment return can be favorable and improved through constructing the effective portfolio. Our findings shows that the most diversifying and hedging advantageous European/developed market pairs are France- Germany and UK -France, and with a high value of hedge effectiveness, these are the best to include in low-risk portfolios. Aspiring and Asian market pairs, such as Japan-China and Malaysia-Singapore, are a moderate diversification on the ground of hedge effectiveness, appropriate to either balanced or medium-risk strategies. Conversely, the correlations of Pakistan with major economies reflect very low or negative hedge effectiveness, which means that they are highly diversified with great portfolio risk. These findings advocated the significant implications for investors and the portfolio manager around the globe.
It is widely recognised that high-frequency traders (HFTs) rely on ultra-low-latency algorithmic systems that allow them to submit and withdraw orders at speeds far beyond human capability. The analysis presented in this study shows that a substantial share of HFT orders are cancelled within roughly 20 milliseconds of being placed. This behaviour enables traders to anticipate short-term price movements and exploit cross-market statistical arbitrage opportunities between the E-mini S P 500 futures contract and the SPDR S P 500 ETF. The evidence further indicates that these strategies generate economically meaningful profits even after accounting for transaction costs and that the resulting gains tend to persist over time. In contrast, traders without comparable low-latency infrastructure and advanced algorithmic systems are likely to face higher execution costs in such market environments. As a potential policy response, we advocate introducing batch auction mechanisms, which would introduce queuing risk for high-frequency traders and could ultimately improve overall market quality.
Capitalization rate is a crucial indicator in real estate valuation. This study proposes a novel method for estimating REIT market-implied capitalization rates using financial market information at high frequency and granularity to understand real-time perceptions of property values across various segments, reconciling timely financial market information with traditional appraisal data to capture market dynamics in its complexity. By linking REIT enterprise values to the market-implied values of their underlying properties, this paper introduces a segment-level market-implied capitalization rate which we call “property ICR” based on detailed underlying property attribute segmentation, applicable to real estate investment practice. Through state-space representation, a dynamic model of property ICR for Japanese REITs is estimated in nine segments based on property types, locations, and other attributes. The estimation results using data from 2007 to 2024 suggest that the property ICRs reflect major shocks in social and economic conditions, such as large-scale monetary easing policies after the Global Financial Crisis and the COVID-19 pandemic, distinctly for each segment. Furthermore, comparisons with appraisal valuations reveal that investors may accept cap rates lower than appraisal values for high-quality office buildings in central Tokyo and demand higher ones for suburban retail properties.
This study investigates the presence, timing, and economic significance of speculative bubbles in the rare whisky market from 2012 to 2025, employing the Generalized Supremum Augmented Dickey–Fuller (GSADF) test. Analysis of auction-based indices from RareWhisky101 reveals multiple episodes of explosive price behavior across nearly all whisky categories, including distillery-specific, collector, and market-performance segments. The results demonstrate the existence of both short-lived and persistent bubbles, which frequently coincide with periods of heightened investor attention, notable auctions, and structural shifts in market liquidity. These findings highlight the dual function of rare whisky as a collectible good and a speculative asset. Beyond documenting bubble dynamics, the study assesses their implications for market participants, emphasizing the need for informed decision-making amid scarcity, heterogeneous liquidity, and substantial transaction costs.
This study investigates the relationship between a sustainable-based extracted factor and US mutual fund returns using flexible quantile breakpoints. This was motivated by the lack of consensus on intrinsic factor construction and the limitations of traditional replication methods. Employing tail risk management in extreme quantile value-weighted sustainable portfolios based on long-only trading strategy, this study finds that moderate ESG risk exposure is associated with reduced tail risk, as evidenced by lower VaR values. To assess the factor’s quality, this research shows that it is nonredundant and contributes unique information to explaining US fund returns. Quantile-by-quantile regression analysis reveals a significant and positive impact of the sustainable factor in the middle quantiles, suggesting its positive contribution to fund returns within these ranges. However, its influence diminishes at the extreme highest quantiles, indicating that the factor’s effect is concentrated in the mid-range of the return distribution. This study empowers investors by explaining impact investing principles and highlighting how the constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed. The QR model findings underscore the importance of ESG factors in asset pricing, affirming the US market’s potential for sustainable investment. This research provides valuable guidance for both academics and practitioners. An ESG risk factor is constructed through flexible quantile breakpoints. Moderate ESG risk exposure is associated with reduced tail risk, as evidenced by lower VaR values. The ESG risk factor’s quality is validated by the Risk Factor Redundancy Test.
The often-observed ‘greenium’ poses a philosophical challenge for the proverbial homo economicus, who may deem it irrational and a failure of fiduciary responsibility, prima facie, to invest in green bonds with an inherently inferior to-maturity economic return versus a same-issuer conventional comparator. In pursuit of partially justifying the greenium, we assess the credit spread behaviour of EUR-denominated green bonds in search of additionality in portfolio construction. Our hypothesis posits that the stylised heterogeneous behaviour of green bond investors, per Flammer (2021), may contribute beneficially to volatility-attenuation during periods of underlying market stress. Our findings confirm this, highlighting the utility of green bonds in creating positive downside portfolio convexity versus comparators. We conjecture that an investment manager could, ex ante, determine the viability of “volatility laundering” through green bond substitution by invoking the Berk and Green (2004) model, given a view on the future path of conventional comparator spreads.
This paper examines how sample composition, the universe of stocks used to test anomalies, affects their significance. Prior studies bundle data-quality filters, investability screens, value-weighting, and factor adjustment, making the sample composition effect hard to isolate. A cumulative filter ladder addresses this by imposing stricter screens, from data-quality to investability and listing restrictions, while holding portfolio construction fixed. A large fraction of anomalies significant in the full sample lose significance by the end of the ladder. This attrition is driven by the investability and market-access screens, not routine data-quality filters, reflecting genuine alpha loss rather than weaker precision or shifting factor exposures. The most resilient anomalies are based on options, analyst, and price data, whereas many accounting and institutional-ownership signals are fragile, losing significance at different filter steps. The survivors tend to begin from stronger initial alphas, and those that drop out from weaker ones. The broader implication is that much of the factor zoo relies on microcap amplification, leaving the robust set that survives the full ladder substantially smaller than conventional full-sample evidence suggests.
This paper investigates whether a firm’s carbon risk exposure influences the relationship between dividend announcements and stock returns among EURO STOXX 600 non-financial companies during the period 2000–2024. Using comprehensive panel data derived from Thomson Reuters Eikon, we apply Ordinary Least Squares (OLS) and Generalized Method of Moments (GMM) estimations to assess how variations in greenhouse gas (GHG) emissions affect the market’s reaction to dividend changes. Our findings reveal that firms with higher carbon risk exposure experience a significantly weaker positive market reaction to dividend increases and a stronger negative reaction to dividend decreases. Moreover, when carbon risks are underpriced, the market exhibits asymmetric behavior toward dividend policies, reflecting an inefficient integration of environmental risk into asset pricing. We further show that both emission levels and emission growth significantly drive carbon premiums, while emission intensity plays a limited role. These results highlight the materiality of carbon risk for investors and regulators in shaping dividend decisions and stock performance across European markets.
This paper investigates the dynamic linkages among WTI, Brent, and Shanghai crude oil futures markets during two major global crises: the COVID-19 pandemic and the Russia–Ukraine conflict. Using daily data from March 2018 to June 2024, the study employs a suite of econometric techniques, including range-based volatility estimators, the rolling Information Leadership Share (ILS) measure, time-varying Granger causality (TVGC), and wavelet coherence analysis. The results show that WTI futures experienced higher volatility during episodes of severe global uncertainty, whereas Shanghai futures exhibited greater volatility during relatively stable periods. Despite its recent inception, the Shanghai market demonstrated a strong and growing role in price discovery, particularly during daytime trading. Causality analysis confirms WTI’s dominant predictive influence on both Brent and Shanghai. Wavelet coherence reveals a persistently strong alignment between WTI and Brent, alongside Shanghai’s gradual, long-term integration into global pricing dynamics. These findings contribute to the literature on international financial markets and energy economics, offering new insights into market efficiency during extreme shocks within the global crude oil benchmarks.
This paper investigates the cross-sectional relation between US-China tension relationship and expected stock returns in China. US-China tension relationship is measured by US-China tension index. Using a sample of Chinese A-share companies from January 2008 to December 2023, we find robust evidence that stocks with the lowest US-China tension index beta generate about 6.8
This study investigates whether U.S. investors can achieve international diversification benefits by investing in domestically traded assets. We use monthly data of six developed markets from G7 countries (excluding the U.S.), and five emerging markets from BRICS countries from 1997 to 2021. We first create three mimicking portfolios to replicate the foreign market index of each of the 11 countries and identify the best mimicking portfolio for each country. We then use a robust mean–CVaR optimization technique on in-sample data to construct three rebalanced portfolios on monthly basis for the out-of-sample analysis. Finally, we evaluate the risk-adjusted statistical and economic performances of these three portfolios to examine whether international diversification benefits can be fully achieved by investing in domestically traded assets in the U.S. For robustness, we also evaluate the performance of these portfolios optimized and rebalanced on quarterly and semi-annual basis. It is observed that U.S. investors can achieve the benefits of international diversification from homemade diversified portfolios and hence they do not require to diversify their portfolios internationally.
Our research employed Deep learning for a time-varying VAR model with extension to the integrated VAR method (DeepTVAR) to explore the relationship between green assets and equity market volatility from September 2020 to September 2025. The results show that TCI decreased from late 2020 to mid-2021 and reached its peak at around 80
I use an event study approach to examine the impact of the 2024 Nikkei 225 crash (05 August 2024) on 72 global stock market indices. Further, I also examine whether the macroeconomic characteristics of the sample nations control the cumulative impact of the exogenous shock. I apply the market model estimation with a 252-day estimation window and an 11-day event window. I find that the Nikkei 225 crash 2024 led to a significant − 1.93
This study examines price jumps in foreign exchange (FX) markets using daily data spanning over 20 years, focusing on the following currency pairs: EUR/USD, GBP/USD, USD/CAD, and USD/JPY. We employ a quantile vector autoregressive (QVAR) model and a frequency connectedness approach to analyze the dynamics of price jumps. Our results reveal increased interconnectedness of price jumps during periods of turmoil, manifested mainly in the short term and at the extreme ends of the distribution. We also observe heterogeneity in the total connectedness indices, with more pronounced spillover effects in the upper end quantile.
Accurate volatility forecasting is essential for asset management, risk control, and regulatory supervision. Traditional GARCH models rely on lagged information, which often reduces their responsiveness to rapidly changing market conditions. This study extends three Realized GARCH-type models—the Log-linear Realized GARCH (LRG), Realized Exponential GARCH (REG), and GARCH@CARR (G@C)—by incorporating real-time returns to improve risk forecasting. The models are estimated using a Bayesian Markov Chain Monte Carlo approach with an adaptive Random Walk Metropolis algorithm. Their performance is evaluated using in-sample fit, out-of-sample volatility forecasts, and one-day-ahead Value-at-Risk (VaR) predictions, applied to stock market indices from the world’s ten largest economies. The empirical results show that the real-time extensions consistently improve model fit and forecasting accuracy. In particular, the extended G@C model achieves the best performance in two markets, while the extended LRG and REG models dominate in seven indices. The proposed models also substantially reduce VaR violations, with the G@C-RR model for the SPX delivering violation rates that align precisely with the 99
Robust regression, which has been very rarely used to determine factor significance and premia, is used to revisit the Fama and French 1992 least squares analysis of the Size, BM, Beta and EP factors. We do so using an optimal bias robust regression estimator that is not much influenced by outliers. The robust Fama-MacBeth regressions for these factors show that small fractions of outliers in the range 2
This study examines the influence of the network centrality of Indian mutual fund managers within their alumni network of peers and corporate directors on their funds’ performance. It also explores whether the gender of fund managers moderates the impact of centrality on performance. Utilising System-GMM, the study shows that more central fund managers (more connected and having better access to information) leverage informational advantages to manage their investment styles (market, size, value, and momentum), resulting in superior risk-adjusted fund performance (measured by Alpha). Although female fund managers tend to achieve higher risk-adjusted fund performance (measured by Sharpe and Sortino ratios), they appear less exploitative and more risk-averse than their male counterparts. The findings suggest that investors are generally unaware of these advantages, since the fund flows are not notably affected by fund managers’ centrality. It implies that fund managers do not significantly profit from their network connectedness in terms of more subscriptions to their funds.
We reconstruct the profitability factor in the Chinese sample using the Gross Profitability to Assets (GPA) ratio. Given the widespread presence of earnings management and policy distortions in Chinese financial statements, the profitability factor based on GPA is able to capture and mitigate these distortions. Compared to ROE, GPA provides a more comprehensive measure of profitability and more effectively captures the profitability effects in the Chinese market. Our findings show that both the gross profitability anomaly and the accrual anomaly are significantly present in China, exhibiting distinct characteristics before and after the split-share structure reform. The newly developed CH-5 factor model, constructed using GPA, successfully explains these two anomalies, captures the information contained in the earlier CH-3 model, and outperforms the traditional Fama-French five-factor (FF-5) model. Furthermore, during the anomaly explanation process, the GPA factor exhibits a “crowding-out effect” on the value factor, offering a novel framework for asset pricing research within the Chinese context.
This study investigates the impact of Bitcoin-related announcements on cumulative abnormal returns (CAR) during the transformative period from 2021 to 2024. Using a dataset of 15,662 announcements sourced from Crunchbase, the analysis explores how external events and narratives shaped Bitcoin’s market behavior. Bitcoin’s performance is evaluated against two benchmarks: the Global Cryptocurrency Market Cap (GCMC), capturing cryptocurrency-specific dynamics, and the MSCI World Index (MSCIW), reflecting broader macroeconomic trends. The results reveal significant variations across the 4 years. In 2021 and 2022, regulatory uncertainty and fragmented messaging drove strong negative CAR trends, highlighting heightened volatility and investor caution. In contrast, 2023 exhibited a sharp positive CAR trend, fueled by optimism surrounding Bitcoin Exchange-Traded Funds (ETFs) and favorable regulatory developments. By 2024, market responses were subdued, reflecting recalibrated expectations following the January 2024 approval of spot Bitcoin ETFs. This study underscores the critical role of external conditions, regulatory clarity, and strategic narratives in shaping Bitcoin’s market dynamics. By highlighting Bitcoin’s evolution as both a speculative and maturing financial asset, the findings offer valuable insights for investors, policymakers, and researchers aiming to understand the cryptocurrency market’s response to external stimuli.
