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Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

Using a sample of oil and gas firms in the USA, the study examines the relation between the presence of hedging and annual report readability.

The authors use regression analysis to examine the relation between the presence of hedging and annual report readability.

The authors find that annual reports of firms with the use of hedging are less readable (i.e. difficult to read and understand). The authors also find that the primary results are more pronounced for firms with a higher level of business volatility.

The study contributes to the finance literature on the use and value of hedging and to the accounting literature on the determinants of annual report readability. The Securities and Exchange Commission (SEC) has persistently asked companies to improve the readability of their disclosures to stakeholders (SEC, 1998; 2013, 2014). Hence, the study not only identifies a potential determinant (i.e. hedging) that may influence the level of readability but also supports the current regulatory policy by the SEC, which is encouraging companies to improve readability.

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 adopts a new macro-perspective to explore the complex and dynamic links between financial instability and the Euro-American green equity market. Its primary focus and novelty is to shed light on the non-linear and asymmetric characteristics of dependence, causality, and contagion within various time and frequency domains. Specifically, the authors scrutinize how financial instability in the U.S. and EU interacts with their respective green stock markets, while also examining the cross-impact on each other's green equity markets. The analysis is carried out over short-, medium- and long-term horizons and under different market conditions, ranging from bearish and normal to bullish.

This study breaks new ground by employing a model-free and non-parametric approach to examine the relationship between the instability of the global financial system and the green equity market performance in the U.S. and EU. This study's methodology offers new insights into the time- and frequency-varying relationship, using wavelet coherence supplemented with quantile causality and quantile-on-quantile regression analyses. This advanced approach unveils non-linear and asymmetric causal links and characterizes their signs, effectively distinguishing between bearish, normal, and bullish market conditions, as well as short-, medium- and long-term horizons.

This study's findings reveal that financial instability has a strong negative impact on the green stock market over the medium to long term, in bullish market conditions and in times of economic and extra-economic turbulence. This implies that green stocks cannot be an effective hedge against systemic financial risk during periods of turbulence and euphoria. Moreover, the authors demonstrate that U.S. financial instability not only affects the U.S. green equity market, but also has significant spillover effects on the EU market and vice versa, indicating the existence of a Euro-American contagion mechanism. Interestingly, this study's results also reveal a positive correlation between financial instability and green equity market performance under normal market conditions, suggesting a possible feedback loop effect.

This study represents pioneering work in exploring the non-linear and asymmetric connections between financial instability and the Euro-American stock markets. Notably, it discerns how these interactions vary over the short, medium, and long term and under different market conditions, including bearish, normal, and bullish states. Understanding these characteristics is instrumental in shaping effective policies to achieve the Sustainable Development Goals (SDGs), including access to clean, affordable energy (SDG 7), and to preserve the stability of the international financial system.

This study investigates the impact of US dollar volatility and oil rents on the performance of small and medium-sized enterprises (SMEs) in the Gulf Cooperation Council (GCC) region, with an emphasis on understanding how these factors influence SME financing constraints in economies with fixed currency regimes.

Employing a random effects panel regression analysis, this research considers US dollar volatility and oil rents as independent variables, with SME performance, measured through the financing gap, as the dependent variable. Controls such as trade balance, inflation deltas and gross domestic product (GDP) growth are included to isolate their effects on SME financing constraints.

The study reveals a significant positive relationship between dollar volatility and the financing gap, suggesting that increased volatility can exacerbate SME financing constraints. Conversely, oil rents did not show a significant direct influence on SME performance. The trade balance and inflation deltas were found to have significant effects, highlighting the multifaceted nature of economic variables affecting SMEs.

The study acknowledges potential biases due to omitted variables and the limitations inherent in the use of secondary data.

Findings offer pertinent guidance for SMEs and policymakers in the GCC region seeking to develop strategies that mitigate the impact of currency volatility and support SME financing.

The research provides new insights into the dynamics of SME performance within fixed currency regimes, which significantly contributes to the limited literature in this area. The paper further underscores the complex connections between global economic factors and SME financial health.

This study aims to outline the research field of initial public offerings (IPOs) pricing and performance by combining bibliometric analysis with a systematic literature review process.

The study uses over three decades of IPO publication records (1989–2020) from Scopus and Web of Science databases. An analysis of keyword co-occurrence and bibliometric coupling was used to gain insights into the evolution of IPO literature.

The study categorized the IPO research field into four primary clusters: IPO pricing and short-run behaviour, IPO performance and influence of intermediaries, venture capital financing and top management and political affiliations and litigation risks. The results offer a framework for delineating research advancements at different stages of IPOs and illustrate the growing interest of researchers in IPOs in recent years. The study identified future research potential in the areas of corporate governance, earning management and investor sentiments related to IPO performance. Similarly, the study highlighted the opportunity to test multiple theoretical frameworks on alternative investment platforms (SME IPO platforms) operating under distinct regulatory environments.

To the best of the authors’ knowledge, this paper represents the first instance of using both bibliometric and systematic review to quantitatively and qualitatively review the articles published in the area of IPO pricing and performance from 1989 to 2020.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

Interest rate derivatives (IRDs) are the essential components of financial risk management and are used across various industry sectors. The objective is to analyze the differences in approach to managing interest rate risks between the Indian corporates that execute IRDs and the ones that do not.

Interest rate fluctuations require Indian corporates to hedge their exposures in foreign currency interest rates. This is all the more true for mid-sized corporates because of their balance sheet exposures. Additionally, they may not have the resources to formulate risk management policies. This paper analyzes data collected from financial statements of a diverse set of companies that use IRD and helps in formulating such a strategy.

The results indicate significant differences for some of the parameters like information asymmetry and agency cost between users and non-users of IRDs. The study further suggests causality between users of derivatives and parameters like size, growth and debt.

The study compares users and non-users of IRDs, thereby identifying factors unique to users of IRDs. It also studies causality relations which explain the motivation to do IRDs. Thus, it enables risk managers to use this as a reference point to decide on their strategies.

While there are multiple studies across geographies and sectors including commercial banks in India on the usage of interest rate swaps, this study focuses on Indian mid-tier corporates. Furthermore, the study distinguishes between users and non-users based on financial parameters, which in turn would provide a framework for decision-hedging strategies.