This study examines the existence and nature of volatility spillovers between conventional and Islamic stocks indices in the context of the SARS-CoV-2 pandemic. By augmenting the Hafner and Herwartz methodology with the integration of Fourier terms in the test equations, we identify both unidirectional and bidirectional volatility spillovers, predominantly of a permanent nature, across these indices. The results suggest that the SARS-CoV-2 pandemic has challenged the traditional perception of Islamic stocks as safe havens. The robustness tests, incorporating the traditional Hafner and Herwartz and frequency domain causality tests, confirm the validity of our main findings by demonstrating that volatility spillovers between Islamic and conventional stock markets are persistent across 27 out of 38 countries, with the Fourier-augmented Hafner and Herwartz test providing superior detection of spillovers compared to traditional methods. The study has significant implications for individual investors, market professionals and policymakers, underscoring the need for caution when considering safe havens during periods of market instability.

This paper investigates the cross-sectional return predictability in the cryptocurrency market by systematically constructing and analyzing a comprehensive set of risk factors. Building on traditional asset pricing literature and the unique tokenomic characteristics of digital assets, we examine eleven key factors, including market, size, momentum, supply dynamics, network activity, computing power, technological attributes, governance decentralization, liquidity, volatility, and behavioral attention. Using quintile portfolio sorting, Fama-MacBeth regressions, and principal component analysis, we evaluate the pricing power and significance of each factor in explaining cryptocurrency returns. Our findings show that several token-specific factors are significantly priced in the cross section, indicating that crypto-assets reflect systematic risks and behavioral influences despite their decentralized nature.

Donald Trump's victory in the 2024 US presidential election presents a unique opportunity to empirically examine whether and to what extent equity markets responded to it, given the stark policy contrasts between Donald Trump and Joe Biden on several important domestic and international issues, alongside the election's narrow polling margins. We employ an event study approach, and returns on 27 equity indices from five geographical regions to gauge the impact of Trump's victory on the abnormal returns and cumulative abnormal returns. While we document negative abnormal returns in 21 of 27 markets on the event day (November 5, 2024), the postvictory market responses are considerably divergent, supporting the hope/fear hypothesis. That is, countries with strained ties to the Biden administration or more friendly relationships with the Trump administration exhibited positive and significant reactions to Trump's victory (e.g., Russia, Israel, T & uuml;rkiye, and Pakistan), whereas those susceptible to Trump's policies (e.g., Germany, France, Brazil, Mexico, and South Africa) exhibited negative and significant responses. The effect on the transitional and intermediate markets-including Canada, Japan, Poland, and Colombia-is mixed, with both positive and negative aspects, but lacks significance. We demonstrate that the US presidential elections have far-reaching economic implications that extend beyond the US market.

The global economic importance of green tech is rising. Yet the role of the green financial sector in the propagation of volatility is still unclear. Although the existing literature often characterizes green assets as stable, the new risks, particularly US–China trade tensions that target the green sector directly, may uncover potential vulnerabilities. As China’s green sector has attained global leadership, its interconnections with other major economies require a closer examination, especially within the BRICS block. Applying the Bayesian VAR with Minnesota Ridge prior and a TVP-VAR model-based connectedness approach on a dataset of 1880 observations spanning from 2016 to 2025, we identified that volatility in China’s green sector peaked during the COVID-19 pandemic and resurged in early 2025 amid trade tensions. Uniquely, this study also finds that, despite the intensification of political and economic relations between BRICS members, the interconnectedness of their financial markets has been weakening, suggesting their long-term decoupling and regionalization. From 2016 to 2024, green indices remained historically peripheral, with limited, stable ties to the Nasdaq and SSE. In 2025, short shock-driven transmitter episodes have emerged and indicate an incipient integration rather than a permanent regime change.

We employ the Bayesian Global Vector Autoregression (BGVAR) model to examine the transmission of adverse shocks originating in the cryptocurrency market to global financial markets. The analysis shows that these spillover effects are not limited to a specific group of countries but are instead global in nature. The results indicate that shocks originating in the cryptocurrency market adversely affect stock markets, bond indices, exchange rates, and volatility indices. These shocks, while typically moderate in magnitude and short in duration, suggest that cryptocurrencies act as mediators of short-term negative shocks. The study also underscores the heterogeneous nature of these impacts across different financial markets and countries, highlighting the varying sensitivities and responses to cryptocurrency market fluctuations. Importantly, this research represents the first application of the GVAR model in the context of the cryptocurrency market, to the best of our knowledge.

This paper examines the profitability of simple technical trading rules in bitcoin markets comprehensively, by taking into account realistic investor behavior and transaction costs, and data mining problems. Realistic investor behavior is replicated by first employing 75,360 simple technical trading rules, divided over 6 commonly used trading rule classes and daily and intraday frequencies. Next, we select the best performing rules after transaction costs using a multiple hypothesis procedure. Finally, we form portfolios combining the selected rules and analyse their out-of-sample performance. We find that, especially risk-return wise, simple technical trading rules can outperform a buy-and-hold strategy in the bitcoin market out-of-sample.

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

We investigate the performance of Socially Responsible Investments (SRI) in both developed and emerging countries. We do so by examining the performance of self-constructed equity portfolios based on SRI's mutual funds' current and historical holdings. Based on the current holdings, the SRI equity portfolios from emerging and developed countries significantly outperform their benchmarks, except for the Japanese stock portfolio. We mitigate the current holdings approach's potential look-ahead and survivorship bias by employing the historical holdings approach. The results change dramatically in developed markets: the outperformance of SRI is not significant in the U.S. – the dominant developed markets in the sample – and Japan. In emerging markets, the SRI's outperformance remains significant. It outperforms in both BRICS and Non-BRICS, and in terms of income, it consistently outperforms in the markets of middle-income countries.

This study examines the long-run relationships between Bitcoin and various financial and commodity markets. Utilizing a novel methodology termed the Implicit Asymmetric Combined Cointegration Test (IACC), an augmented variant of the Bayer Hanck combined cointegration method (BH), this research applies ten-minute frequency time series data to test asymmetric shocks associated with Bitcoin, stock markets, futures indices, sectoral stock indices, Islamic stocks, commodities, and foreign exchange markets. The principal finding reveals a hidden cointegration between negative Bitcoin shocks and both negative and positive shocks in almost all examined financial instruments, indicating an absence of decoupling in the connections between Bitcoin shocks and other financial instrument shocks. The study demonstrates Bitcoin's centrality in financial investments and establishes long-run relationships between Bitcoin price shocks and those of other financial instruments. The findings suggest caution for participants in both financial and commodity markets, as Bitcoin emerges as a major source of the recent volatility observed in these instruments' prices.

This study examines the correlation between former U.S. President Donald Trump's China-related tweets and the daily return and conditional volatility of onshore (CNY) and offshore (CNH) Renminbi exchange rates. Using sentiment analysis techniques to quantify political value judgment, we find that Trump's sentiment towards China has a significantly negative relationship with the CNH value but no significant linkage with the CNY price. During the trade tension period, both markets' conditional volatility responded to the tweets, with the CNH daily return displaying a heightened reaction to the sentiment. Our results demonstrate the importance of considering market conditions when analyzing the effects of political rhetoric on different segments of the Renminbi exchange rate market.

This study examines the causal connections between COVID-19-related restrictions, economic support measures, and the performance of fintech enterprises with combined Granger causality and Fractional Frequency Flexible Fourier-form Toda-Yamamoto (FFFFTY) causality tests. We find the evidence of unidirectional permanent causality running from Covid-19 related government response stringency and economic supports to the performance of the technology-driven financial services (TDFSC) in most of the countries in the sample, where traditional tests fail. Our findings indicate that these companies seem to be the primary beneficiaries of Covid-19related policies (restrictions and economic support) implemented by governments to mitigate the negative impacts of the pandemic.

Using transaction-level tick-by-tick data of same- and next-day settlement of the Russian Ruble versus the US Dollar exchange rate (RUB/USD) traded on the Moscow Exchange Market during the period 2005–2013, we analyze the impact of trading hours extensions on volatility. During the sample period, the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate. To analyze the effect of the implementations, various measures of historical and realized volatility are calculated for 5- and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions. Besides historical volatility measures, we also examine volume and spread. We apply an autoregressive moving average-autoregressive conditional heteroscedasticity (ARMA-GARCH) model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading. The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes. Volatility changes mostly occur after the opening of the market. The length of the extension has a significant positive effect on realized volatility. The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement, whereas this is not observed for next-day settlement. Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets, they may also lead to higher volatility in the market. Furthermore, this distortion is more significant at opening and midday. A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

The present study uses transfer entropy and effective transfer entropy to quantify the asymmetric information flow between monthly Bitcoin prices (Price) and total Bitcoin electricity consumption (Electricity). Both the Shannon and Re & PRIME;nyi estimates confirm a statistically significant information flow from Price to Electricity. However, Shannon and Re & PRIME;nyi methods yield mixed results in quantifying the information flow for Electricity to Price. That indicates a possible non homoge-neous and chaotic impact of total Bitcoin electricity consumption on Bitcoin prices. However, the Re & PRIME;nyi transfer entropy value converges with Shannon's as the value of q approaches to 1. The study findings are highly useful for managing the energy mix and carbon emissions associated with Bitcoin & other cryptocurrency mining.

We examine whether commodity trading advisors (CTAs), or managed futures, provide investors with diversification benefits in times of crises, along with looking into the source of such a "crisis alpha". We developed a systematic crisis identification methodology that takes into account both, the magnitude and the speed of price deterioration and apply it to a unique dataset, that includes realized returns and sector positions of CTAs on a daily basis. We find CTAs do acquire positive gains in most sectors during crises, which originate from two sources: Firstly, their diversification across multiple futures markets, i.e. positive yields in gaining markets can counterbalance low performance in the crisis market. Secondly, the fast reduction in CTAs' exposure to crisis markets (in less than 15 days for composite indices) allows them to stabilize their performance. Both factors together, quickly cutting losses in crisis sectors while staying profitable in the other ones, allow CTAs to generate crisis alpha.

AbstractFinancial markets have changed their face substantially with the digital era. Floor trading has to a large extent been replaced by electronic trading platforms. This led to a substantial increase in trading speed bringing the trade execution time down from minutes to milliseconds. At the same time, we observe an increase in liquidity and transparency, whereas the impact on price quality and financial stability is more ambiguous. Particularly high-frequency trading is suspected to have a negative effect in this respect. Recently we observed eroding profits from high-frequency trading and a declining market share.Turning to the foreign exchange market it has also undergone substantial changes due to digitalization. While it was dominated by bilateral telephone trades and voice brokers, it has to a large extent moved to the electronic trading platforms by Reuters and EBS. While this first applied to the interbank market, the customer market has more recently been affected and most of the enormous increase in trading volume during the last decades took place there. Particularly the introduction of electronic communication networks had a huge impact. Nevertheless, their share is currently declining due to increased demand by customers for a centralized marketplace.KeywordsElectronic tradingHigh-frequency tradingTrading speedForeign exchangeElectronic communication network

This study applies a Markov switching error correction model to describe the single most important real exchange rate (Deutsche mark versus US dollar) over the flexible exchange rates period from 1973 to 2004. We show an alternative way of modelling non-linear adjustment to the purchasing power parity (PPP) besides standard threshold models. The model merges the two possible sources of non-linearity by additionally allowing the probability of a mean-reverting regime to increase with the distance from PPP. The interest rate differential as an additional determinant of real exchange rate behaviour in a Markov switching framework is introduced in the model. The study finds that the real dollar exchange rate during the post-Bretton Woods era is well described by a Markov switching error correction model with (PPP) as long-run equilibrium. There is one mean reversion regime where PPP and the interest parity condition are valid. Contrary, the second regime is characterised by persistent mean aversion, where a regime switch does not become more likely with increasing distance from PPP. The unconditional half-life of shocks is about 1.5 years.

This paper focuses on testing the long memory in exchange rates series of 18 emerging and 10 developed foreign exchange markets against the USD, along with explaining the behavior of these markets by the implications of the adaptive market hypothesis. Our empirical approach is based on DFA methodology for computing Hurst exponent (a parameter for capturing long memory) along with the established bootstrapping procedure for improving the results obtained. We also analyzed the impact of the great financial crisis (2007–09) on the efficiency of these foreign exchange markets by dividing our sample period into three sub-periods; pre, during, and post-financial crisis. Finally, the rolling window approach is applied for capturing the time-varying dimension of market efficiency. All the aforementioned tests confirmed that the emerging and developed foreign exchange markets exhibit episodes of long memory (persistence long memory/anti-persistent long memory) and no long memory signaling their efficiency has been affected by the financial crisis due to the changes in the behavior of market participants, and they follow a time-varying market efficiency. Our results confirmed that the behavior of these markets is consistent with the implications of the Adaptive Market Hypothesis.

This study proposes and tests a portfolio selection model with inflation allocation lines (IAL) for corresponding capital allocation line (CAL) and utilities in several scenarios of crisis. The model is based on Markowitz's mean-variance (MV) theory, with modification of Tobin's portfolio utility function, and Sharpe's (1964) portfolio theory. The model introduces inflation as a significant factor. According to study results, empirically tested with least squares (OLS) and quantile regression models, the study verifies that under conditions of low and moderate inflation the investor chooses an optimal portfolio which generates the highest real returns (including borrowed funds). For the case of severe recession, the investor chooses a minimum variance portfolio.