Search results

This study analyses the impact of the Covid-19 on stock market performance of BRICS nations together. BRICS countries comprise almost 30% of the global GDP and around 50% of the world’s economic growth. As BRICS nations have gained the attraction as financial investment destinations, their financial markets have apparently been as potential opportunities for foreign portfolio investors. While there is extensive research on the impact of the Covid-19 pandemic on individual economies and global financial markets, this paper is among the first to systematically investigate the dynamic connectedness of these emerging economies during the pandemic using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach.

We categorise our data into two distinct periods: the pre-Covid period spanning from January 1, 2018, to March 10, 2020, and the Covid crisis period extending from March 11, 2020, to June 4, 2021. To achieve our research objectives, we employ the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach to assess dynamic connectedness.

Our findings reveal that among the BRICS nations, Brazil and South Africa serve as net transmitters of shocks, while China and India act as net receivers of shocks during the Covid crisis. However, the total connectedness index (TCI) has exhibited a notable increase throughout this crisis period. This paper makes several notable contributions to the academic literature by offering a unique focus on BRICS economies during the Covid-19 pandemic, providing practical insights for stakeholders, emphasising the importance of risk management and investment strategy, exploring diversification implications and introducing advanced methodology for analysing interconnected financial markets.

The results have important implications for the investors, the hedge funds, portfolio managers and the policymakers in BRICS stock markets. The investors, investment houses, portfolio managers and policymakers can develop investment strategies and policies in the light of the findings of this study to cope up the future pandemic crisis.

This study is one of its kind that examines the dynamic connectedness of BRICS with recently developed TVP-VAR approach across pandemic crisis.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This paper aims to examine the return and volatility transmission among economic policy uncertainty (EPU), geopolitical risk (GPR), their interaction (EPGR) and five tradable precious metals: gold, silver, platinum, palladium and rhodium.

Applying time-varying parameter vector autoregression (TVP-VAR) frequency-based connectedness approach to a data set spanning from January 1997 to February 2023, the study analyzes return and volatility connectedness separately, providing insights into how the data, in return and volatility forms, differ across time and frequency.

The results of the return connectedness show that gold, palladium and silver are affected more by EPU in the short term, while all precious metals are influenced by GPR in the short term. EPGR exhibits strong contributions to the system due to its elevated levels of policy uncertainty and extreme global risks. Palladium shows the highest reaction to EPGR, while silver shows the lowest. Return spillovers are generally time-varying and spike during critical global events. The volatility connectedness is long-term driven, suggesting that uncertainty and risk factors influence market participants’ long-term expectations. Notable peaks in total connectedness occurred during the Global Financial Crisis and the COVID-19 pandemic, with the latter being the highest.

Using the recently updated news-based uncertainty indicators, the study examines the time and frequency connectedness between key uncertainty measures and precious metals in their returns and volatility forms using the TVP-VAR frequency-based connectedness approach.

The main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2

The main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions. The period is divided into sanctioned and non-sanctioned periods by Markov switching model.

The results show that the pass-through of oil price shocks into Iran’s inflation are time-varying, and there are significant differences at sanction period from other time horizons. An increase in oil price has a positive effect on inflation and its effects are stronger during the sanctions period. It is also observed that the producer price index is more sensitive to changes in the oil price than the consumer price index. The necessity of the government’s earnest efforts to improve international relations to lift the sanctions, along with diversification of exports, and making the economy of Iran independent of oil revenues is obvious.

In addition to the exogenous oil price shocks, Iran’s economy faces numerous restrictions for its oil exports due to the sanctions. The main purpose of this paper is to investigate the dynamics effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions.

The real estate markets may be significantly influenced by the uncertainty in global economic policy. This paper aims to evaluate the time-varying connectedness between global economic policy uncertainty and regional real estate markets to understand how regional real estate markets and uncertainty in global economic policy are related throughout time.

The current study includes the monthly data from April 2007 to August 2022 of major regions (i.e. Asia Pacific, Europe, Africa, North America and Latin America). Moreover, the authors use the time-varying parameter vector auto-regression (TVP-VAR) approach for the analysis.

The finding revealed a significant level of connectedness among global economic policy uncertainty and selected regional real estate markets. The result highlights more than 80% connectivity between the two variables, which makes the current study valuable. Furthermore, results determine Africa and North America are the shock transmitters; thus, they are considered safe-haven for investors to invest in these markets.

The main novelty is that this research highlights the time-varying connectedness between global economic policy uncertainty and five regional real estate markets (Africa, Asian Pacific, Europe, Latin America and North America) using TVP-VAR. Furthermore, the authors used the standard and poor daily real estate investment trust (REIT) indices for the selected REIT markets. Finally, this research suggests practical implications for real estate investors, property developers, stakeholders, policymakers and managers to revise their current policies to maintain the real estate market stability during economic and political uncertainty or in other uncertain situations.

This study aims to investigate the dynamic interconnectedness of economic policy uncertainty (EPU), fiscal policy uncertainty (FPU) and monetary policy uncertainty (MPU) in four nations, the USA, Japan, Greece and South Korea, between 1998 and 2021.

To comprehend the cross-category/cross-country evolution of uncertainty connectedness, the authors use the conditional connectedness approach. By using an inclusive network, this strategy lessens the bias caused by omitted variables. The TVP-VAR method is advantageous as it eliminates outliers that may potentially skew the results and reduces the bias caused by picking arbitrary rolling windows.

Based on the findings, aggregate EPU is a net transmitter of policy uncertainties across all countries when conditional-country connectedness is used. MPU receives significantly more spillovers than FPU does across all countries, even though both are primarily recipients of uncertainties. The USA appears to be a transmitter of categorical spillovers before COVID-19, while Greece appears to be a net receiver of all category spillovers in terms of category-specific connectedness. The existence of extreme global events is also seen to cause an increase in category-specific and country-specific connectedness. Additionally, the authors report that conditional country-specific connectedness is greater than conditional category-specific connectedness.

This study expands existing literature in several ways. Firstly, the authors use a novel conditional connectedness approach, which has not been used to untangle cross-category/cross-country policy uncertainty connectedness. Secondly, they use the TVP-VAR approach which does not depend on rolling windows to understand dynamic connectedness. Thirdly, they use an expanded number of countries in their analysis, a departure from existing studies that have in most cases used two countries to understand categorical EPU connectedness.

This paper aims to examine the dynamic return and volatility connectedness for six major industrial metals (tin, lead, nickel, zinc, copper and aluminium) and the coronavirus media coverage index (MCI).

To that purpose, this study applies the fresh time-varying parameter vector autoregression methodology (TVP–VAR model) during the sample period between 2 January, 2020, and 16 April, 2021, that is, covering the three waves of the COVID-19 pandemic crisis.

This study’s results show interesting findings. First, dynamic total return and volatility connectedness changes over time, highlighting a significant increase during the third wave of the pandemic. Second, the MCI index is a leading net transmitter in terms of return and volatility at the introduction of the SARS-CoV-2 coronavirus crisis. Third, this study clearly distinguishes two profiles among industrial metals: copper and tin/zinc as net transmitters and lead and aluminium as net receivers. Finally, the most relevant differences between them are concentrated not only at the beginning of the COVID-19 pandemic (first wave) but also during the second and third waves of the coronavirus outbreak.

To the best of the authors’ knowledge, this is the first research that explores the dynamic return and volatility connectedness in the industrial metal market, applying the TVP–VAR methodology during the first waves of the COVID-19 pandemic crisis.

The purpose of this study is to investigate the effects of uncertainty, namely, macroeconomic uncertainty (MU) and financial uncertainty (FU) on foreign exchange market stability, specifically on foreign exchange market pressure (EMP) and jump risk (RJV).

The latent threshold time-varying parameter VAR (LT-TVP-VAR) econometric approach is used in estimations to solve structural breaks.

The relationship of uncertainties and China's foreign exchange market stability is latent threshold nonlinear dynamic time-varying. In China's renminbi (RMB) appreciation stage, both MU and FU weaken the appreciation pressure of RMB. Moreover, MU and FU significantly increase the RJV, while MU significantly affects the RJV of the foreign exchange market. In the RMB depreciation stage, both MU and FU strengthen the EMP.

Findings based on data in China's foreign exchange market can be considered for other global markets in future research.

An increase in MU and FU has a negative effect on foreign exchange stability. Regulators can prevent the economic system uncertainty shocks on foreign exchange market stability through observation and judgment of MU and FU, which helps prevent and relieve financial risks. Investors can reduce foreign exchange risk as the exchange rate rebounds after hedging behavior during high uncertainty periods.

The effect of MU on the foreign exchange market stability is greater than that of FU, regardless of whether EMP or RJV occurs in the foreign exchange market.

This paper aims to study the interplay of fiscal policy and asset prices in a time-varying fashion.

Using South African data since 1966, the authors are able to study the dynamic shocks of both fiscal policy and asset prices on asset prices and fiscal policy based on a time-varying parameter vector autoregressive (TVP-VAR) model. This enables the authors to isolate specific periods in time to understand the size and sign of the shocks.

The results seem to suggest that at least two regimes exist in which expansionary fiscal policy affected asset prices. From the 1970s until 1990, fiscal expansions were associated with declining house and slightly increased stock prices. The majority of the first decade of 2000 had asset prices increasing when fiscal policy expanded. On the other hand, increasing asset prices reduced deficits for the majority of the sample period, while the recent financial crises had a marked change on the way asset prices affect fiscal policy.

This is the first attempt in the literature of fiscal policy and asset prices to use a TVP-VAR model to not only analyse the impact of fiscal policy on asset prices, but also the feedback from asset prices to fiscal policy over time.

The purpose of this paper is to study the interlinkages between the cryptocurrency and stock market by characterizing their connectedness starting from January 1, 2018 to December 31, 2021.

The author employs a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach.

The pandemic shocks appear to have influences on the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that each cryptocurrency and stock have a heterogeneous role, conditional on their internal characteristics and external shocks. In particular, Bitcoin and Binance Coin are reported as the net receiver of shocks, while the role of Ethereum shifts from receivers to transmitters. As for the stock market, the US stock market stays persistent as net transmitters of shocks, while the Asian stock market (including Hong Kong and Shanghai) are the two consistent net receivers. During the COVID-19 pandemic shock, pairwise connectedness reveals that cryptocurrencies can explain the volatility of the stock markets with the impact most severe at the beginning of 2020.

Insightful knowledge about key antecedents of contagion among these markets also help policymakers design adequate policies to reduce these markets' vulnerabilities and minimize the spread of risk or uncertainty across these markets.

The author is the first to investigate the interlinkages between the cryptocurrency and the stock market and assess the influences of uncertain events like the COVID-19 health crisis on the dynamic interlinkages among these two markets. The author employs the TVP-VAR combined with an extended joint connectedness approach.