Purpose This study examines the diversification and hedging potential of cryptocurrencies and traditional financial assets during periods of varying economic policy uncertainty (EPU). In the identified periods, we analyze return and risk characteristics of six well-known cryptocurrencies (i.e. Bitcoin, Ethereum, Solana, Cardano, Ripple, Binance coin) alongside with traditional assets (S&P 500, gold and T-bills) and assess their effectiveness in investment portfolios. Design/methodology/approach The methodology is separated into two sections. First, we objectively identify periods of economic distress by employing the Bai-Perron structural breaks test on the EPU time series. Then, in each subperiod, we employ a set of return and risk metrics using multiple rolling window specifications, as well as measure the correlation coefficients in order to estimate how useful the selected cryptocurrencies as well as TradFi assets (Gold, T-bills and S&P500 index) are in investment portfolios. Findings Gold maintains superior safe-haven characteristics compared to all cryptocurrencies during high EPU periods, exhibiting lower volatility, reduced correlation with equity markets and more stable positive returns. Among cryptocurrencies, Bitcoin demonstrates the strongest safe-haven properties during periods of elevated economic policy uncertainty, maintaining lower correlation with traditional financial markets compared to other digital assets. The decentralized nature of cryptocurrencies provides limited hedging benefits during policy-driven uncertainty episodes, as their risk-return profiles remain inferior to gold's established safe-haven characteristics. Cryptocurrency performance exhibits significant heterogeneity across different EPU regimes, with certain digital assets showing strong performance in specific episodes but lacking consistency over time. Originality/value We have two elements of originality. First, we objectively identify periods of low versus high economic uncertainty via the Bai-Perron structural breaks test. This eliminates subjectivity in the period selection, which is a problem in other research. Second, we identify a high-EPU period characterized first by the trade policy measures taken by President Trump but also by a pro-crypto regulatory agenda. We analyze the behavior of cryptocurrencies in this period and compare their performance with Gold and the S&P 500 index.
Purpose The study aims to address the limitations of modern portfolio theory (MPT) in asset selection by developing a risk-centric, multi-criteria decision-making approach. By incorporating additional risks such as fat-tail risk and skewness, as well as investor preferences and macroeconomic factors, the study offers an improved and pragmatic framework for asset selection in real-world financial markets. Design/methodology/approach The study employs a fuzzy multi-criteria decision-making methodology, using the Choquet integral to evaluate assets across eight global markets. It combines quantitative criteria (return, risk) with qualitative factors (investor preferences, macroeconomic conditions). Investor profiles are developed, and fuzzy trapezoidal numbers are used to quantify linguistic evaluations. The study tests six different investor profiles, ranking assets based on each profile's unique risk-return preferences, demonstrating the method's adaptability to varied investment strategies. Findings We find that the proposed approach augments MPT by accommodating heterogeneous investor preferences and incorporating non-conventional risks. Different investor profiles result in varied optimal asset allocations, suggesting that a sole focus on risk-return measures may not be sufficient and that integrating behavioral and macroeconomic factors leads to more personalized and effective investment strategies. Practical implications The study offers practical implications for financial institutions, particularly in improving investor profiling and aligning asset recommendations with individual preferences. This approach enhances personalized wealth management and aids in regulatory compliance, offering a robust framework for portfolio strategies that can adapt to market changes and diverse investor behaviors. Originality/value This study offers a novel contribution to asset selection by integrating a fuzzy multi-criteria decision-making model that accounts for investor heterogeneity, nonconventional risks (skewness and kurtosis) and macroeconomic factors. Unlike traditional models that rely solely on risk-return trade-offs, this approach provides a more holistic and practical solution, aligning investment strategies with real-world investor preferences. Highlights
Purpose This study quantifies the material distortions in empirical return distributions caused by finite samples and overlapping returns, raising caution about the reliability of raw historical simulation for risk management. We aim to provide actionable guidance on when historical data can be informative and when it may be misleading. Design/methodology/approach Using controlled simulations of a Geometric Brownian Motion – a best-case scenario of stationarity and ergodicity – we isolate the distorting effects of sample size (1–100 years) and overlapping windows on distributional accuracy, employing Kolmogorov-Smirnov, Anderson-Darling and other distance metrics. Findings Even a century of daily data leaves the mean estimated with substantial relative uncertainty and tail estimates highly variable. Overlapping returns improve nominal sample size and estimates of central tendency but introduce dependencies that materially distort tail risk, inflating Anderson-Darling statistics by a factor of 14 in our simulations. The trade-off is purpose-dependent: overlapping windows help describe the distribution body but hinder tail-risk inference. Research limitations/implications The analysis relies on ideal conditions rarely met in real markets. This limitation is intentional: documenting distortions under these favorable conditions establishes a conservative lower bound. In practice, with non-stationary, path-dependent data, distortions are likely larger. Practical implications Risk measures calibrated from raw historical return distributions should generally avoid overlapping data for tail-risk applications. Overlapping windows may be acceptable for descriptive visualization or central-shape estimation, provided that effective sample size is reported. Even the finite-sample results alone call for a move from naive historical extrapolation to prudence, combining historical estimates with scenario analysis, robustness checks and forward-looking distributions. Originality/value This controlled lower-bound study shows that even under ideal conditions, finite samples and overlapping windows materially affect distributional estimation, especially in the tails, with clear implications for risk measurement. To our knowledge, it is the first to systematically quantify these distributional biases.
Purpose This research examines whether environmental, social, and governance (ESG) integration comes at a cost financially through an analysis of the performance of global ESG and traditional funds. Design/methodology/approach Using a global dataset of funds (2018–2024), fixed-effects regressions, and risk-adjusted measures such as the Sharpe ratio, Jensen's alpha, and Drawdown, this paper examines performance differences. Findings Returns from ESG funds are similar to those of regular funds after risk adjustment. More specifically, ESG funds perform better in terms of risk management, with lower volatility (12.15 vs. 13.02%) and maximum drawdowns (−18.4% vs. −21.3%). Such an advantage is especially noticeable in equity funds, which demonstrate not only better performance but also lower volatility. Besides, ESG funds also proved more robust amid COVID-19-related risks, recording losses 1 point lower. Practical implications Integration of ESG provides a “haven” in times of market stress without affecting future gains. The results help investors manage risks and help policymakers justify sustainability finance models. Originality/value This study covers both the coronavirus outbreak and the 2022 energy crisis and offers crucial empirical evidence on the downside protection provided by ESG.
Purpose This study investigates whether real house prices in Greece respond asymmetrically to key macroeconomic fundamentals and whether such asymmetries are consistent with downward price rigidity. Motivated by the persistent rise in property prices and its implications for affordability and macro-financial stability, we examine differential effects across expansionary versus contractionary phases and assess the role of major structural disruptions. Design/methodology/approach We model real house prices in Greece as the dependent variable and apply the nonlinear autoregressive distributed lag (NARDL) methodology, decomposing GDP per capita, inflation (CPI) and mortgage interest rates into positive and negative partial sums to estimate asymmetric short- and long-run effects. Regime shifts are explicitly accounted for through an endogenously identified structural break at 2012Q2, aligned with the March 2012 PSI, strengthening inference on cointegration and long-run relationships over an extended, crisis-prone sample. Findings The results confirm cointegration with a significant speed of adjustment and pronounced asymmetries in the transmission of shocks to real house prices. In the long run, lagged GDP per capita is significant. Positive CPI shocks with a one-quarter lag raise prices, while negative shocks are insignificant, consistent with downward price rigidity. Interest-rate hikes depress prices more than cuts support them, and Wald tests indicate that long-run asymmetry operates mainly through the CPI and interest-rate channels. In the short run, positive GDP-per-capita shocks are significant, while negative changes are negligible. Lagged positive CPI changes reduce prices, while negative CPI changes remain insignificant. Interest-rate hikes have more persistent negative effects than the less durable and partly offset response to cuts, with Wald tests confirming short-run asymmetry across all three channels. Research limitations/implications In the Greek housing market, the long-run equilibrium appears stable; however, the transmission of macroeconomic shocks to real house prices is nonlinear and asymmetric, particularly in the short run. The findings highlight the importance of nonlinear approaches for both economic analysis and policy design. Although the empirical estimates are specific to Greece, the NARDL framework with structural breaks and formal asymmetry testing may also prove useful for analyzing similar housing-market adjustment mechanisms in other euro-area peripheral economies that experienced sovereign-debt, housing-market and banking-system stress, such as Portugal, Spain and Ireland. Practical implications Results imply that macroprudential policy should be calibrated to asymmetric risks. Authorities can front-load risk containment during boom phases through Loan-to-Value and Debt-to-Income limits, while supervisors can incorporate asymmetric scenarios into stress tests. In Greece, demand-support schemes such as the Golden Visa and “My Home” should be linked to local housing availability and complemented by measures that expand effective supply, including easing permitting delays, mobilizing vacant housing stock, and supporting new housing provision in high-demand areas. Social implications Asymmetric house price responses can exacerbate affordability pressures because prices rise quickly in upswings but adjust slowly in downturns. This pattern can widen wealth inequality between owners and non-owners, raise barriers for first-time buyers, and increase household vulnerability when interest rates rise. Recognizing these dynamics supports policies that protect access to housing and reduce boom-driven displacement, especially in urban areas where demand shocks are strongest. Originality/value This study addresses a specific gap in the Greek housing-price literature by examining whether key macroeconomic determinants affect real house prices asymmetrically in both the short and long run once major structural disruptions are taken into account. It is among the few studies to apply a unified nonlinear NARDL framework with asymmetric effects, an endogenously identified structural break and a long sample covering major economic episodes. To the best of our knowledge, no previous study on Greece combines these features. The findings offer policy-relevant implications for housing policy, monetary policy and macroprudential design under heightened macro-financial risk.
