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This study aims to evaluate the market risk exposure of three international equity portfolios using value‐at‐risk (VaR). This risk metric calculates the worst case loss for a business in the course of its daily transactions. To ensure that the calculated VaR reflects emerging risk characteristics, this paper introduces an approach that incorporates time‐varying volatility.

This study uses the GARCH technique to calculate the volatility metric with which VaR estimates are obtained. The out‐of‐sample performance of the VaRs is then assessed by comparing them to the actual market risk losses in that period.

Empirical results show that regardless of market conditions, the VaR calculated with this (GARCH) approach is more robust and more reliable than the traditional methods. Pursuant to the banking regulation on market risk capital stipulated by the Basel Committee on Banking Supervision, the out‐of‐sample VaRs are at least equal to actual daily market risk losses at the 99 percent confidence level.

The key goal of banking regulation is to ensure that financial firms have sufficient capital for the types of risks they take. Determining the right amount of capital requires these firms to first estimate their worst case loss, which is the value‐at‐risk. The approach to the calculation of VaR introduced in this paper enhances the accuracy in the measurement of market risk capital for financial institutions.

This paper recognizes that for VaR to fully account for market risk losses, the risk metric must be correctly measured. The unparalleled approach in this paper of incorporating time‐varying volatility in VaR calculations offers banking institutions a more reliable means of determining their capital adequacy.

The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

Existing empirical evidence suggests that episodes of financial stress (crises) can act as driver of growth of inequality. Consequently, in this study, the authors explore the time-varying predictive power of an index of financial stress for growth in income (and consumption) inequality in the UK. The authors focus on the UK since income (and consumption) inequality data are available at a high frequency, i.e. on a quarterly basis for over 40 years (June, 1975 to March, 2016).

The authors use Wang and Rossi's approach to analyze the time-varying impact of financial stress on inequality. Hence, the method provides a more appropriate inference of the effect rather than a constant parameter Granger causality method. Besides, understandably, the time-varying approach helps to depict the time-variation in the strength of predictability of financial stress on inequality.

This study’s findings point that financial distress correspond to subsequent increases in inequality, with the index of financial stress containing important information in predicting growth in income inequality for both in and out-of-sample periods. Interestingly, the strength of the in-sample predictive power is high post the period of the global financial crisis, as was observed in the early part of the sample. The authors believe these findings highlight an important role of financial stress for inequality – an area of investigation that has in general remained untouched.

Accurate prediction of inequality at a higher frequency should be more relevant to policymakers in designing appropriate policies to circumvent the wide-ranging negative impacts of inequality, compared to when predictions are only available at the lower annual frequency.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

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.

The study investigates the effectiveness of government policies to mitigate the impact of a pandemic. The study adopts the small open economy of Nigeria for the following reasons. First, Nigeria is the largest economy in SSA. Second, Nigeria was also significantly impacted by the COVID-19 pandemic.

The study employed the time-varying structural autoregressive (TVSVAR) model to control for the potential asymmetry in fiscal variables and to control for the shift in the structural shift, following a macroeconomic shock. As a form of robustness, the study also implements the time-varying Granger causality to formally assess the temporal instability of the variable of interest.

The results show that an oil price shock is an important source of macroeconomic instability in Nigeria. Importantly, the results indicate that the effects of fiscal policy are strongly time varying. Specifically, the results show that fiscal policy helps to stabilize the economy, (i.e. they help to reduce inflation and spur output growth) following macroeconomic shock. Further, the Granger test shows that fiscal policy helped to spur growth in Nigeria. The research and policy implications are discussed.

The study accounts for the time-varying effects of fiscal policy.

The purpose of this study is to investigate the interplay between fiscal dominance and monetary policy in South Africa from 1960 to 2023.

The study employs a structural vector autoregression (SVAR) medel to analyze the relationship between fiscal dominance and monetary policy. Short-term and long-term shocks of government borrowing and deficits are examined to understand their impact on inflation dynamics.

Fiscal dominance has a significant effect both in the short and long run. There is evidence that government debt and deficits increase inflation, overriding the effects of monetary policy aimed at maintaining price stability. On the other hand, the study reveals that money supply shocks have a greater effect in reducing fiscal dominance compared to interest rate shocks. The variance movement on inflation is significantly explained by government debt and deficits. This emphasizes the persistence of inflationary pressures associated with fiscal dominance, highlighting the importance of effective policy interventions to mitigate inflationary risks.

This study contributes to the existing literature by providing insights into the dynamics of fiscal dominance in South Africa. Moreover, this study extends the theoretical framework of the fiscal theory of the price level (FTPL) and the government budget constraint. This study contributes valuable insights into the dynamics of fiscal dominance in South Africa and offers guidance for policymakers in formulating strategies to safeguard economic stability.

In this chapter, the authors ask two questions: (i) Is the conduct of monetary policy stable across time and similar across major economies? and (ii) Do policy decisions of major central banks have international spillover effects? To address these questions, the authors build on recent semi-parametric advances in time-varying parameter models that allow us to increase the vector autoregressive () dimension and to jointly model three advanced economies (USA, UK and the Euro Area). The main reduced-form finding of this chapter is an increased connectedness between and within countries during the recent financial crisis. In order to study policy spillovers, we jointly identify three economy-specific monetary policy shocks using a combination of sign and magnitude restrictions. The authors find that monetary policy shocks were larger in magnitude and more persistent in the early 1980s than in subsequent periods. The authors also uncover positive spillover effects of policy between countries in the 1980s and diminished, and sometimes negative ‘beggar-thy-neighbour’ effects in the second half of the sample. Moreover, during the 1980s, the authors find evidence for policy coordination between the Federal Reserve, the Bank of England and the European Central Bank.