Asset Classes Research Articles

Beyond the glitter: An empirical assessment of the true risk and hedging role of precious metals

Precious metals are an essential component of asset class within the global financial market offering diverse benefits. Being significant store of value, they are immune to default risk, provide hedge against inflation and are deemed to be safe haven. This highlights the importance of precious metals in the broader context of financial and economic stability. This paper focuses on the S&P Precious Metal Index (PMI) to explore its resilience towards financial market risks and examines if it could offer effective hedge during crises. Using Markov Regime Switching model, we find significant volatility persistence in PMI returns undermining its suitability for dynamic hedging. However, absence of speculative trends makes the PMI a safe asset. Further, financial market stress, as revealed by estimated Component GARCH and Discrete Threshold Models, does not affect the unique and market risk of investing in PMI. Although we could not establish PMI as a strong safe haven or a zero-beta asset, its potential to be included in a risk-avert investor's portfolio cannot be negated. Its consistently low and stress-invariant time-varying beta safeguards wealth against losses. The role of PMI becomes particularly salient while considering portfolio optimization during crises. In recent years, particularly during and after the pandemic the hedging effectiveness of PMI has increased. There is, however, a point of concern. The market risk of PMI is explained mostly by its lagged values rather than exogenous factors like market stress. Given the endogenous nature of market risks, chaotic dynamics might be in operation making forecasting and regulating PMI prices difficult.

Do capital controls absorb global financial shocks? Evidence from emerging market economies

ABSTRACT This paper examines the role of capital controls as an effective policy instrument to insulate Emerging Market Economies (EMEs) from global financial shocks. Using state-dependent Local Projection method on a sample of emerging market economies, we find that economies with strict inflow controls are able to moderate the effects of global financial shock on GDP, real housing prices and private credit. We also find that monetary policy responds countercyclically to global financial shock in strict capital inflow controls state, which provides support for the use of inflow controls as a macroeconomic stabilising policy tool. Further, the results suggest that individual asset category inflow controls significantly contribute to overall effectiveness of capital inflow controls to act as shock absorbers. The findings suggests that the policy makers in EMEs may consider capital controls as an effective policy tool to increase the resilience of their economies against global financial shocks.

Community assets in the context of COVID-19: Changes in valuation and contributions to place attachment and subjective wellbeing

ABSTRACT The COVID-19 pandemic disrupted patterns of daily life and use of community spaces. This study was completed in a municipality in the Greater Niagara Region, Ontario, to explore people-place relationships after the onset of the COVID-19 pandemic. A greater proportion of individuals perceived the way in which they value community assets had changed since the onset of the pandemic. This was supported by pre- and post-pandemic data, which found that indoor community assets were valued less in 2021 than they were in 2019, whereas outdoor community assets were valued comparably. Two categories of community assets were found to contribute most to both place attachment and wellbeing during the COVID-19 pandemic: Waterfront/beaches and recreational trails/pathways. These findings emphasize the integral role of outdoor community assets in community member’s place attachment and subjective wellbeing, which could guide future investment and community development in the municipality.

Social assets and the career intent of teachers serving students with extensive support needs.

Decades of research indicate social support is vital for retaining teachers. However, little is known about social support for teachers serving students with extensive support needs. The purpose of this study was to explore whether collective social assets (administrative support, colleague support, paraeducator support, school culture) were associated with retaining special education teachers (SETs) who serve students with extensive support needs (e.g., intellectual disability, autism). Stepwise regression analyses and Analysis of Variance (ANOVA) were used to analyze survey data from SETs across the United States. Results showed that positive social supports are critical, and teachers serving students with autism reported the highest levels of social support in three of the four social assets categories. Implications for future research and practice are discussed, as well as a need to promote and better understand positive school culture as this variable weighed heavily across SETs.

Cryptocurrency: A new player or a new crisis in financial markets? —— Evolutionary analysis of association and risk spillover based on network science

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets.

The role of uncertainty and sentiment for intraday volatility connectedness between oil and financial markets

We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 min quotations for most heavily traded financial assets: crude oil, the US dollar, S&P 500 index, gold and US treasury bonds. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents a different picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 min frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market-specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand financial and commodity markets dynamics.

Fair value estimates for illiquid cryptocurrency

To address the need for reporting and disclosure of cryptocurrency holdings in compliance with the FASB guidance for the use of fair value measurements for cryptocurrency (FASB, 2023), this paper develops a modeling process for reporting entities to measure the market value of cryptocurrencies with limited or no observable transactions. In this valuation model, we consider the last observable market information with time decay, its comparable assets market index, and dynamic real-time market participants’ sentiment and attention. Notably, the application of exogenous variables allows us to maximize the observable inputs in measuring fair value, such as asset classification based on economic traits and market participants’ attention and sentiment measurement with online media textual analytics. We propose a valuation framework and construct a prediction model that can achieve a prediction accuracy of 87 % on target asset resurging prices.

Security tokens, ecosystems and financial inclusion: Islamic perspectives

PurposeShariah-compliant security tokens can play an important role in developing innovative solutions to resolve voluntary and involuntary financial exclusion in Muslim societies. This paper aims to present features of Shariah-compliant security tokens and supporting ecosystems that can provide additional sources of financing for small and medium enterprises (SMEs) and create alternative investment opportunities for retail investors.Design/methodology/approachThis conceptual paper presents the building blocks of security tokens, their ecosystem and key functions and activities and then examines these features from Islamic perspectives. This is done by reviewing the contemporary literature on cryptoassets and their ecosystems and analysing these in light of Islamic legal and ethical values and principles.FindingsThe paper provides a framework of how Shariah-compliant asset- and equity-based security tokens can be used by SMEs to raise funds quickly and efficiently on crypto exchanges. Given the novelty and complexity of the technology involved and the lack of understanding and skills to develop blockchain-based systems among SMEs, this paper suggests developing security tokens and exchanges in a controlled manner under the supervision of a nation’s stock markets.Originality/valueAlthough several studies examine cryptocurrencies from Islamic perspectives, literature on other cryptoassets and their role in financial inclusion is scant. This paper identifies Shariah-compliant asset- and equity-based security tokens and supporting ecosystems that can contribute to the development of digital capital markets where SMEs can raise funds efficiently and retail investors can invest in alternative asset classes.

Assessing the environmental performance of green mortgage‐backed securities

AbstractThe green bond market is growing substantially, bringing with it a focus on economic and environmental performance. Yet while extensive work exists examining the former, there is little concrete evidence regarding the efficacy of green bond use‐of‐proceeds. Concurrently, the demand for ESG‐compliant investments provides an opportunity to direct capital toward the rehabilitation of one of the most energy‐intensive asset classes: real estate. One program in this space, the Fannie Mae Green Rewards green bond program, offers incentives to borrowers to increase multifamily building energy and water efficiency. Although all program participants must complete a set of preapproved projects targeting energy and water efficiency within 12 months of loan origination, there exists substantial variation in the realization of postorigination efficiency outcomes, and in the variation between projected and actual efficiency improvements. We find that fixed interest rates and supplemental financing loan structures are associated with postorigination energy efficiency improvements, as are newer, larger, and high‐quality assets. However, the ex ante estimates of efficiency savings provided to prospective investors prove unrelated to the efficiency outcomes. These findings highlight opportunities to improve program transparency and calibration across the green bond universe.

Performance of Green vis-à-vis Red EU securities

ABSTRACT This research compares the performance of Green (eco-friendly) versus Red (eco-enemy) returns for EU securities. Green securities are the stocks of companies whose primary business activities focus on being relatively beneficial to the environment, while Red stocks are associated with business activities that are harmful to the environment. The study investigates their performance from January 2000 to December 2019, before and after the Global financial crisis in 2009. The methodology follows two parts in order to distinguish the financial performance between Green and Red stocks. The first part applies the asset pricing models from the Fama-French and the Carhart models and their extensions for every security and estimates the alpha performance; the second continues with a cross-sectional regression to compare the asset classes. The findings indicate that there is no statistically significant difference in financial performance between Red and Green securities.

Time-frequency co-movements between commodities and global economic policy uncertainty across different crises

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises.The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis.Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

Monetary Policy without Moving Interest Rates: The Fed Non-Yield Shock

Existing high-frequency monetary policy shocks explain surprisingly little variation in stock prices and exchange rates around FOMC announcements. Further, both of these asset classes display heightened volatility relative to non-announcement times. We use a heteroskedasticity-based procedure to estimate a “Fed non-yield shock”, which is orthogonal to yield changes and is identified from excess volatility in the S and P 500 and various dollar exchange rates. A positive non-yield shock raises stock prices in the U.S. and around the globe, and depreciates the dollar against all major currencies. The non-yield shock is essentially uncorrelated with previous monetary policy shocks and its effects are large in comparison. Its strong effects on the VIX and other risk-related measures point towards a dominant risk premium channel. We show that the non-yield shock can be related to Fed communications and that its existence has implications for the identification of structural monetary policy shocks.

Investment and financing maturity mismatch: Moderating role of financialization in regulatory context for A-share listed Chinese companies

As the financialization issue is getting more and more attention, the behavioral motives and effects behind this appearance should not be ignored, and it is of great practical significance for the high-quality development of China's real economy to explore the impact and mechanism of the financialization trend on the investment and financing maturity mismatch of China's real enterprises. Using sample data of Chinese A-share listed companies from 2013 to 2020, this article empirically examines the impact of financialization on the investment and financing maturity structure from a new perspective of asset classification by using a fixed-effect model, and explores the mechanism of the financial regulatory environment's moderating effect on the relationship between the two mentioned above. The study shows that: there is an inverted U-shaped nonlinear relationship between the financialization of investment income and fixed income and “maturity mismatch “. The term mismatch of investment and financing increases with the degree of financialization, after reaching the critical point, it eases with the deepening of financialization. However, the specific point of view is different. In the sample interval, the investment income financialization exacerbates the investment and financing maturity mismatch more obviously; the fixed income financialization inhibits the investment and financing maturity mismatch more obviously. Under the different perspectives of the firms' ownership nature, financing constraints, and principal-agent problems, there are differences in the impact of firms' allocation of different types of financial assets on the investment and financing term structure. In addition, the regulatory effect of financial supervision weakens the inverted U-shaped relationship of investment income financialization with investment and financing maturity mismatch; it enhances the inverted U-shaped relationship between fixed income financialization and investment and financing maturity mismatch. In general, financial supervision has had a significant positive effect on investment and financing maturity mismatches. The findings have important policy implications in terms of corporate real investment, financial market development, and financial regulation, which can help promote China's economic development and stability.

Comparative Analysis of Gold, Art, and Wheat as Inflation Hedges

This study confirms gold’s role as a reliable inflation hedge while introducing new insights into lesser-explored assets like art and wheat. Using advanced methodologies such as the ARDL framework and LSTM deep learning, it conducts a detailed analysis of inflation-hedging dynamics, exploring non-linear relationships and unexpected inflation impacts across various asset classes. The findings reveal complex dynamics. Gold demonstrates strong long-term inflation hedging potential. The negative coefficient for the US dollar index suggests that gold acts as a hedge against currency depreciation. Furthermore, a positive relationship between gold returns and inflation during high inflation periods highlights its effectiveness in protecting purchasing power. Art presents a more intricate picture. Long-term analysis suggests a weak mean-reverting tendency, but a negative relationship with inflation, potentially linked to economic downturns. Interestingly, unexpected inflation positively correlates with art returns in the long run, hinting at its potential inflation-hedging abilities. No statistically significant connection between wheat prices and overall inflation was observed; the short-run analysis reveals a dynamic interplay between inflation, real GDP growth, and wheat prices at different time points.

Mean Absolute Directional Loss as a new loss function for machine learning problems in algorithmic investment strategies

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. MADL places appropriate emphasis not only on the quality of the point forecast but also on its impact on the rate of achievement by the investment system based on it. The introduction and detailed description of the theoretical properties of this new MADL loss function are our main contributions to the literature. In the empirical part of the study, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that our new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.

Liquidity Connectedness Among Major Financial Asset Classes: Do Uncertainty Factors Matter?

Liquidity Connectedness Among Major Financial Asset Classes: Do Uncertainty Factors Matter?

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.

A comparison of cryptocurrency volatility-benchmarking new and mature asset classes

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.

Can green bonds be a safe haven for equity investors?

AbstractWe investigate if green bonds can act as a safe‐haven asset for equity investors by analysing their relationship with stocks and other alternative safe havens, namely sovereign bonds and gold. Safe havens are defined as assets that exhibit zero or negative comovement with equity during a stock market downturn. We analyse the interrelationships between the asset classes using the Marginal Expected Shortfall of Acharya et al. (The Review of Financial Studies, 30(1), pp. 2–47, 2017) and by comparing the regime‐dependent GIRFs from a Markov‐switching VAR model. Our results suggest that green bonds are not safe haven assets for equity investors but rather show positive comovement during periods of market stress. The sovereign bond is the most consistent in delivering diversification benefits across market conditions, while gold acts as a safe‐haven asset during all regimes except during rare periods of extreme turbulence.

Which migrant jobs are linked with the adoption of novel technologies, robotisation, and digitalisation?

This paper presents pioneering evidence of the effects of various novel technologies on migrant employment. By analysing data from 18 EU member states from 2005 to 2019, it sheds light on how innovations proxied by patents granted, robot adoption, three categories of digital assets, and total factor productivity, affect migrant employment. The key findings reveal that innovation leads to an increase in both the number and proportion of migrant workers relative to the overall workforce. While robots do displace some workers, their impact on native workers is stronger compared to migrant workers. Total factor productivity positively influences migrant workers, while the effects of digital assets are heterogeneous. Importantly, results also point towards significant variations concerning the impacts of these technologies on migrant workers across different occupations and educational levels.