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This paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.

This paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.

Wavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.

A comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.

This study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020. The authors also compare the hedging performance of in-sample and out-of-sample analyses.

For the modeling purpose, the authors combine the GARCH-BEKK model with the machine learning approach to predict the transmission of shocks between the financial markets and the oil market. The authors also examine the hedging performance in order to obtain well-diversified portfolios under both Financial Stress cases, using a One-Dimensional Convolutional Neural Network (1D-CNN) model.

According to the results, the in-sample analysis shows that investors can use oil to hedge stock markets under positive Financial Stress. In addition, the authors prove that oil hedging is ineffective in reducing market risks for bond markets. The out-of-sample results demonstrate the ability of hedging effectiveness to minimize portfolio risk during the recent pandemic in both Financial Stress cases. Interestingly, hedgers will have a more efficient hedging performance in the stock and oil market in the case of positive (negative) Financial Stress. The findings seem to be confirmed by the Diebold-Mariano test, suggesting that including the negative (positive) Financial Stress in the hedging strategy displays better out-of-sample performance than the in-sample model.

This study improves the understanding of the whole sample and positive (negative) Financial Stress estimates and forecasts of hedge effectiveness for both the out-of-sample and in-sample estimates. A portfolio strategy based on transmission shock prediction provides diversification benefits.

Over a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. This study aims to demonstrate that residential assets in “Grand Paris” are a hedge against inflation and particularly against its unexpected component.

In this study, the physical residential markets in 127 communes in Paris and the Parisian first-ring suburbs are considered as potential asset classes. We simplified the analysis by clustering the 127 communes into five homogenous groups using ascending hierarchical classification (AHC). Then, we test the hedging ability of these groups within a mixed asset portfolios using both correlation and regression analysis.

This paper presents an analysis of the “Grand Paris” housing market and its inflation hedging ability with comparison to other financial asset classes. Results show that the five housing groups act as a highly positive hedge against unexpected inflation. Furthermore, cash and bonds seem to provide, respectively, a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant hedge against expected inflation. Also, indirect listed real estate demonstrates little correlation with inflation, which makes us reject its hedging ability contrary to physical residential real estate.

The inflation topic: although several researches exist that question the hedging property of real estate, very few concentrate on physical residential assets and to the best of the authors’ knowledge, this study is the only one that targets the “Grand Paris” area. Residential assets of the “Grand Paris” communes are confirmed to be a hedge against inflation and particularly against its unexpected component thanks to its capital appreciation rather than income one. Also, we show that the listed real estate in France (Sociétés d’Investissement Immobilier Cotée) does not provide the same hedging properties contrary to the US real estate investment trusts (REITs) who demonstrate this ability. Listed real estate could thus not be used interchangeably with housing to protect from inflation in the French market.

Protection of investors against inflation and in particular in the face of its return to France in 2022. Reassuring promoters and investors of the interest of residential investment projects in “Greater Paris” and of the potential that this holds.

Inflation takes a chunk out of the purchasing power of money and thereby erodes the real value of people’s finance. Investors and households who seek protection from inflation erosion should invest in direct housing, and in particular within areas that are experiencing an effective metropolization process.

The originality of the study is precisely relative to the geographical area studied. The latter has experienced favorable economic conditions for several years and offers interesting fundamentals to explore and exploit in investment strategies that prove capable of protecting against imminent inflation. The database is specific to this project and has been built through the compilation of several sources and with the support of BNP Paribas Real Estate.

This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.

The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.

Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.

The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

This study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.

This study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.

It is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.

To the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility.

This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area.

The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with “cryptocurrencies” and “bitcoin” during “COVID-19.” The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area.

This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results.

Over the past two decades, the topics of Environmental, Social and Corporate Governance (ESG) and Corporate Social Responsibility (CSR) have attracted an increasing amount of interest, reflecting a growing sensitivity of investors and corporations towards environmental, social and governance issues.

This survey offers an overview of the academic literature on ESG/CSR through the lens of investors, institutions and firms. We first discuss the definitions of ESG and CSR and their relationship to each other.

We next describe how ESG is measured and note problems with the measurement of and quality of ESG data and discrepancies between different measures of ESG. We then turn our attention to investors, examining what types of investors invest in ESG and the role of institutional investors in ESG. From the firm's perspective, we discuss why firms themselves conduct ESG. We also summarize the literature on the impact of ESG on firms: how ESG affects firms' financing, disclosure and reporting activities and firm performance. Finally, we describe other consequences of the focus of ESG and CSR on firms and investors.

This survey offers an overview of the academic literature on ESG/CSR through the lens of investors, institutions and firms.

This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market.

This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic.

EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened.

This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market.

This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.