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The scientific community still struggles to understand the magnitude of the worldwide infections and deaths induced by COVID-19, partly ignoring the financial consequences. In this paper, using the autoregressive fractionally integrated moving average (ARFIMA)—general autoregressive conditional heteroskedasticity (GARCH) model, we quantify and show the impact of the COVID-19 spread in Italy, utilizing data for the stock market. Using information criteria and forecasting accuracy measures, we show that the COVID-19 confirmed cases contribute with statistically significant information to the modeling of volatility, and also increase the forecasting ability of the volatility of the Italian stock market index, leading to a decrease in the mean stock index.

In the recent era, gold is considered an essential investment source, a source of hedging inflation, and a medium of monetary exchange. The gold and exchange rate nexus become prominent after events like sovereign debt crisis, subprime mortgage crisis, low-interest rate problem, and global financial market solvency. These events attract the attention of researchers and academician for investigating the dynamics of the relationship between gold and exchange rates, and the majority of the studies discusses the linear dynamics, but the non-linear dynamics are ignored. Therefore, the current research investigates the non-linear dynamics of gold price and exchange rate relationship in G7 countries using the new technique named the nonparametric causality approach. This study uses monthly data from the years 1995(January)-2017 (March). The empirical results show that exchange rate return causes gold prices in four out of G7, especially at the low tails. This study also gives valuable insights for monetary policymakers, gold exporter’s international portfolio managers, and hedge fund managers.

This study aims to investigate the relationship between the spot and futures commodity markets. Considering the complexity of the relationship, we use a nonlinear autoregressive distributed lag (NARDL) framework that considers the asymmetry and nonlinearity in both the long and short run. Based on the daily returns of six commodity indices reaggregated on three commodity types, our study reaches some interesting findings. Our analysis highlights a bidirectional relationship between both markets over the short and long run, with a greater lead for the futures market. This result confirms the future market’s dominant contribution to price discovery in commodities. Changes in commodity prices appear first in the futures market, as informed investors and speculators prefer trading on this market that is characterized by low costs and a high-leverage effect. Then, the information is transmitted from the futures to the spot market through arbitrageurs’ activity, which explains the nonlinearity of the relationship. These results are helpful to scholars, investors and policymakers.

Given that there is no consensus on the fact that ESG portfolios are characterized by very high returns and very low risks compared to conventional portfolios, this study aims to empirically verify whether the series of returns of an ESG portfolio is less volatile than the returns of a benchmark market portfolio. To verify this hypothesis, we used the Markov-switching GARCH models in order to model the process of the series of daily returns of the ESG portfolio “MSCI USA ESG Select,” as well as those of the market benchmark portfolio daily returns series “S&P 500,” during the period June 01, 2005 to December 31, 2020 as well as that excluding the COVID19 crisis and from June 1, 2005 to October 29, 2019. It can be concluded that the ESG portfolio “MSCI USA ESG Select” is relatively less turbulentcompared to the market benchmark portfolio “S&P 500.”

This study is designed to model and forecast Nigeria’s stock market using the All-Share Index (ASI) as a proxy. By employing the Markov regime-switching autoregressive (MS-AR) model with data from April 2005 to September 2019, the study analyzes the stock market volatility in three distinct regimes (accumulation or distribution – regime 1; big-move – regime 2; and excess or panic phases – regime 3) of the bull and bear periods. Six MS-AR candidate models are estimated and based on the minimum AIC value, MS(3)-AR(2) is returned as the optimal model among the six candidate models. The MS(3)-AR(2) analysis provides evidence of regime-switching behaviour in the stock market. The study also shows that only extreme events can switch the ASI returns from regime 1 to regime 2 and to regime 3, or vice versa. It further specifies an average duration period of 9, 3 and 4 weeks for the accumulation/distribution, big-move and excess/panic regimes respectively which is an evidence of favorable market for investors to trade. Based on Root Mean Square Error and Mean Absolute Error, the fitted MS-AR model is adjudged the most appropriate ASI returns forecasting model. The study recommends investments in stock across the regimes that are switching between accumulation/distribution and big-move phases for promising returns.

Bitcoin is designed as a peer-to-peer cash system. To work as a currency, it must be stable or be backed by a government. In this paper, we show that the volatility of Bitcoin prices is extreme and almost 10 times higher than the volatility of major exchange rates (US dollar against the euro and the yen). The excess volatility even adversely affects its potential role in portfolios. Our analysis implies that Bitcoin cannot function as a medium of exchange and has only limited use as a risk-diversifier. In contrast, we use the deflationary design of Bitcoin as a theoretical basis and demonstrate that Bitcoin displays store of value characteristics over long horizons.

This paper aims to contribute to the growing pool of literature on the spillover effects of the European Central Bank’s (un)conventional monetary policies on the exchange rate, sovereign bond and equity markets of the Czech Republic, Hungary and Poland (CEE-3 countries), which are collectively known as the CEE-3 countries. The study is conducted using daily data from January 2010 to September 2019. Our results indicate that the financial markets of the CEE-3 countries have been strongly influenced by the nonstandard measures enacted by the European Central Bank, particularly those involving purchases of euro-area sovereign debt. The strongest spillover effects were identified for the Securities Markets Program, while the effects from the Outright Monetary Transactions program turned out to be the most durable. At the same time, the financial markets of the CEE-3 countries were found to have been largely unaffected by interest rate changes enacted by the European Central Bank.

Real estate development investments are characterized by a high value of projects, which in the event of irregularities in their implementation may result in significant losses for the economy. The lack of tools enabling ongoing control of real estate developers may result in disruptions in the operation of business entities on the real estate market, affecting the proper functioning of many stakeholders. The article proposes a method of measuring value for real estate companies. Accounting principles that regulate financial statements specify that they cannot be used directly to measure the value of a developer. The article proposes examples of corrections to financial statements supporting value measurement. When calculating value management measures, (i) adjustments excluding the impact of asset valuations, (ii) adjustments of settlement negative EVA of the investment phase, (iii) adjustments of advance payment of NPV of the project, and (iv) adjustments of excluding the impact of interest on foreign capital should be made. Examples of using these adjustments in a short-term housing project and a long-term commercial project were presented. The impact of the proposed adjustments on the comparison of formal financial statements and value measures for a large developer listed on the WSE was also discussed.

Using the nearest neighbor truncation (NNT) approach, this study investigates the realized volatility transmission between the Malaysian Islamic market with various global sectoral Islamic stock markets by extending the heterogeneous autoregressive (HAR) with GARCH, asymmetric effects and jump-robust volatility estimator established in a multivariate setting. The multivariate HAR-GARCH model is capable to capture the persistence and time-varying volatility of realized volatility while NNT approach allows for an asymptotic limit theory in the presence of jumps. Using intraday data, the findings suggest the daily realized volatilities of the Islamic equities rely significantly on their own short-, mid- and long-term volatility components. Besides, there is an evidence of volatility spillover in majority of the pairwise but with low magnitude. The findings also confirm the vital role of conditional heteroscedasticity in the volatility series in explaining the measured volatility transmission of the Islamic stock indices. Hence, this study provides useful insight to understand the portfolio risk of Shariah-compliant equities in making better-informed portfolio allocation.

This study examines nonlinear effects of global and country-specific geopolitical risk uncertainty on stock returns of Brazil, India, Indonesia, South Africa, and Turkey, employing a three-regime Markov-switching approach. This study discovers that the Markov-switching model captured the impacts of global and country-specific geopolitical risk uncertainty on stock returns of all fragile emerging economies, while the linear framework does not. Hence, the effects of both risk factors are nonlinear and asymmetric on stock market returns. The empirical results uncover that global geopolitical risk uncertainty can affect stock market performance both positively and negatively, which depend on contemporaneous time, lag time, volatility regimes, and stock market. The results also reveal that the country-specific political unrest adversely influences the stock market performance of four fragile emerging economies throughout the volatility regimes, with exception of the Indian stock market performance. Therefore, investors should follow market volatility behavior before taking risk of global and domestic geopolitical uncertainty.

This research aims to analyse the response of the Bank Indonesia (BI rate) to the Indonesian economic stability. The data analysis is stationarity test, model stability test, lag determination, Structural Vector Autoregression (SVAR), Impulse Response Function (IRF), and Variance Decomposition (VD). The research data is obtained from the publication provided by the Federal Reserve Data (FRED), the Bank Indonesia, and the Central Bureau of Statistics. The data used is since the third quarter of 2005 to the first quarter of 2017. The research results showed that the variable of the federal funds rate (FFR) significantly influences the exchange rate and the Consumer Price Index (CPI), but it does not significantly affect the BI rate, the amount of the money supply (M2), and Gross Domestic Product (GDP). The result of the IRF test showed that the BI rate, the amount of money supply, exchange rate (IDR/USD), GDP, and CPI positively and negatively respond the FFR change. The result of VD test indicated that the variation of the BI rate, the currency exchange rate, and CPI are mostly caused by the FFR variation.

Exposure to market risk is a core objective of the Capital Asset Pricing Model (CAPM) with a focus on systematic risk. However, traditional OLS Beta model estimations (Ordinary Least Squares) are plagued with several statistical issues. Moreover, the CAPM considers only one source of risk and supposes that investors only engage in similar behaviors. In order to analyze short and long exposures to different sources of risk, we developed a Time–Frequency Multi-Betas Model with ARMA-EGARCH errors (Auto Regressive Moving Average Exponential AutoRegressive Conditional Heteroskedasticity). Our model considers gold, oil, and Fama–French factors as supplementary sources of risk and wavelets decompositions. We used 30 French stocks listed on the CAC40 (Cotations Assistées Continues 40) within a daily period from 2005 to 2015. The conjugation of the wavelet decompositions and the parameters estimates constitutes decision-making support for managers by multiplying the interpretive possibilities. In the short-run, (“Noise Trader” and “High-Frequency Trader”) only a few equities are insensitive to Oil and Gold fluctuations, and the estimated Market Betas parameters are scant different compared to the Model without wavelets. Oppositely, in the long-run, (fundamentalists investors), Oil and Gold affect all stocks but their impact varies according to the Beta (sensitivity to the market). We also observed significant differences between parameters estimated with and without wavelets.

Investment in Green energy is becoming a popular alternative asset class for investors, primarily due to its environment-friendly attributes. However, there is a dire need for subjective evaluation of this emerging asset class based on the risk-return dynamics to which investors are exposed. To respond to this call, in this study, we conduct this evaluation utilizing a unique and rich data set consisting of daily prices of exchange-traded funds (ETFs) established on different asset classes. We use Vector autoregression and Baba-Engle-Kraft-Kroner parameterization of multivariate GARCH models and assess the relative strength of return and volatility spillovers from the Green and Grey energy markets. Our results reveal the return shocks originated in the Green energy market and transmitted to other markets are more pronounced. It is also observed that the potential to earn high returns and the weak correlation of Green energy ETFs with the traditional asset classes are the crucial factors helpful in inviting attention and investment of investors after 2015. Although our results further suggest that the role of Grey energy is diminishing, as shown by the Impulse response functions and the coefficients of multivariate ARCH and GARCH. Nonetheless, for some asset classes, e.g., Bonds, the volatility spillovers that originated in the Grey energy market are still prominent and robust.

In this paper, we examine whether jumps matter in both equity market returns and integrated volatility. For this purpose, we use the swap variance (SwV) approach to identify monthly jumps and estimated realized volatility in prices for both developed and emerging markets from February 2001 to February 2020. We find that jumps arise in all equity markets, however, emerging markets have more jumps relative to developed markets, and positive jumps are more frequent than negative jumps. In emerging markets, the markets with average volatility earn higher returns during jump periods, however, highly volatile markets earn higher returns during jump periods in developed markets. Furthermore, markets with low continuous returns and high volatility are more adversely affected during periods of negative jumps. The average ratio of jump variations to total variation shows considerable variations due to jumps. Integrated volatility is high during periods of negative jumps, and this pattern is consistent in both developed and emerging markets. Moreover, the peak volatility of stock markets is observed during periods of crises. The implication of this study is useful in the asset pricing model, risk management, and for individual investors and portfolio managers for both developed and emerging markets.

This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The study employs different constraining optimization frameworks that seek to maximize risk-adjusted returns (Sharpe ratio) of the portfolio by optimizing allocations to each asset class (asset allocation). The performance attributes are evaluated by comparing the portfolios both with and without Bitcoin under frameworks ranging from equal-weighted, risk-parity, and semi-constrained to unconstrained. This study suggests that Bitcoin, due to its exotic nature, unwavering appeal, and unknown set of drivers, could act as a diversifier in normal market conditions, and it might also have some borderline hedge to safe haven properties. The results further suggest that while Bitcoin may be a potential diversifier for a risk-seeking investor, the risk-averse investor must exercise caution by limiting their exposure to Bitcoin in their portfolios, as unnecessary exposure may increase the probability of losses in extreme market conditions.

Economic policy uncertainty (EPU) has important implications for crude oil market. To explore the implications, this paper investigates the impact of EPU on the crude oil return volatility and which EPU index has the most forecasting power in crude oil market. To this end, we employ the GARCH-MIDAS model which can incorporate lower frequency EPU index variable with higher frequency crude oil return variable effectively. We find that EPU has a positive and significant impact on the crude oil return volatility, but the effect is short-lived and the decay period is about one year. Particularly, our results show that the US EPU index has the best forecasting power for crude oil return volatility over the long-term, whereas China EPU index has the best forecasting performance in the past one year. Our findings have important implications on risk management for investors in crude oil market. JEL classification: C32, C58, E32, Q41, Q47, Keywords: Economic policy uncertainty, EPU index, Crude-oil return volatility, GARCH-MIDAS