Financial Stress Index Research Articles

Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework

This study explores the theoretical possibility of co-movement and causality between crude oil implied volatility (OVX) and financial stress in a wavelet framework. The paper contributes to the existing literature in at least three possible ways: (a) First, the study considers not only composite financial stress indicators but also uses the categorical stress components such as Credit, Equity Valuation, Funding, Safe Assets and Volatility. (b) Second, the study employs a wavelet-based approach in tracking the co-movement and causality between oil and financial stress in a continuous time-frequency space. Lastly, (c) while previous studies mainly use oil price changes to assess the relationship with financial stress, the present study evaluates the role of forward-looking (30-days ahead) oil price uncertainty (proxied by OVX). The findings indicate the existence of co-movement between oil volatility and financial stress, mainly around the phases of economic turbulence. The patterns and strength of such co-movements are time-variant. The direction of the relationship is mostly positive, and the lead-lag relationship reveals that OVX tends to drive the relationship. It is further observed that the causalities between the variables are mostly bi-directional. However, relatively stronger causalities are transmitted from OVX towards FSI. Furthermore, the association between OVX and stress indicators is assessed in two different states of the economy, i.e., state of distress and tranquillity. The findings suggest that the causal co-movement intensifies majorly during the state of distress. Overall, the outcome of this study could be useful to policymakers and investors to anticipate the impending changes in the relationship to mitigate its potential adverse impact.

Construction and prediction of China's financial stress index based on the A-E-L coupling perspective

As economic globalisation and financial liberalisation continue to develop, domestic and international shocks have increased the pressure on China's financial system. To this end, this paper constructs the China Financial Stress Index (CFSI) to analyse China's financial situation. This paper selects 10 representative indicators of the banking, bond, foreign exchange, real estate and insurance markets for the period 2011from 2021. Based on the dimensionality reduction of the indicators, the paper uses the hierarchical analysis method (AHP) and the entropy weighting method (EWM) to assign weights, and couples them with the Lagrange multiplier method to obtain the China Financial Stress Index, and finally uses the long and short-term memory network model (LSTM) to forecast. The results show that the CFSI constructed in this paper can better portray the dynamic financial stress situation in China, and the overall financial stress in China can be divided into three trends.

Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks

This study aims to predict GCC financial stress on oil market, and GCC Stock and bond markets while considering the effect of the 2008 financial crisis, 2014 oil drop price and the 2019 novel COVID-19 outbreak. For this purpose, we use a new approach for predicting the financial stress, based on the One-Dimensional Convolutional Neural Network (1D-CNN). This article introduces a parameters optimization method, which provides the best parameters for 1D-CNN to improve the prediction performance of the financial stress indices. The results suggest that indexes of financial stress help to improve forecasting performance. It implies that the 1D-CNN model shows a better predictive performance in the out-of-sample findings.Regarding the influence of financial stress on hedging between Brent, and financial markets, the outcomes emphasize the role of oil in hedging stock market risks in positive market stress case. Another interesting result is that the out-of-sample estimates for stock–bond markets, hedging with oil have higher variability for negative (positive) financial stress. The findings highlight the predictive information captured by financial stress in accurately forecasting oil market volatility and financial markets, offering a valuable opening for investors to monitor oil market volatility using information on traded assets.

Financial Stability and Economic Activity in China: Based on Mixed-Frequency Spillover Method

To improve financial sustainability and promote economic stability, it is important to understand the intricate relationship between finance and macroeconomy. Thus, focusing on financial stress and macroeconomic sectors, this paper investigates macro-financial spillovers in China. First, we develop a high-frequency financial stress index based on eight daily financial indicators to measure the stability of China’s financial markets. Through event identification, we find that China’s Financial Stress Index can effectively reflect the stress situation of China’s financial market. Then, given that the traditional co-frequency method fails to deal with financial stress index and macroeconomic data with different frequencies, we employ the mixed-frequency spillover method to evaluate macro-financial spillovers to examine the connectedness between China’s financial market and the real side of the economy. We find that financial stress is the leading net risk output and primarily affects the loan sector; deterioration of economic conditions can lead to more apparent fluctuations in spillover effects, with spillovers from financial stress to others being the most susceptible; within the sample, the 2015 stock crash, U.S.–China trade friction, and COVID-19 have the most impact on macro-financial spillover effects. In addition, we track the results of different risk events on spillover effects across sectors.

A time-varying copula approach for constructing a daily financial systemic stress index

This paper develops a financial systemic stress index (FSSI) for the US financial market. We propose a time-varying copula method to model the dependence structure among financial sectors in order to build a correlated financial stress model that can signal systemic financial risks. The copula method is preferable to the traditional approach, enabling the modeling of non-linear correlations. Our analyses show that the dependencies across banking, security, and forex markets are best modeled by Archimedian copulas. Finally, we conduct a Markov Switching Autoregressive (MS-AR) model for FSSI and identify high financial stress episodes taking place in 2008–2009, 2011 and 2020.

Financial connectedness and risk transmission among MENA countries: Evidence from connectedness network and clustering analysis

This study examines the financial connectedness and risk transmission among MENA economies by accounting for financial connectedness in the short and long run as well dependency under extreme market conditions and network graph analysis. To this end, Composite Financial Stress Indices are constructed for 11 MENA countries. In addition, a battery of econometric models is applied including the standard spillover approach, the frequency domain method, the quantile connectedness technique, and connectedness networks analysis. Using daily data over the period from June 30, 2006 to June 30, 2021, the empirical results show a positive and strong association between financial stress co-movements and spillovers in those MENA countries, particularly during the long run and high extreme stress periods. Furthermore, the five Gulf countries are strongly financially connected among themselves than with the other countries. Contrary, to Tunisia, Saudi Arabia is the main financial stress and risk transmitter to other MENA economies whereas, the North African countries are relatively mild receivers of risk. Finally, the more open countries in terms of capital controls, particularly Kuwait, Oman, Qatar, and UAE seem to play a more central role in financial connectedness and risk spillovers.

The effect of global volatility, uncertainty and geopolitical risk factors on international tourist arrivals in Asia

AbstractThe major tourist destinations in the Asian region are promoting and maintaining a conducive tourism environment. The susceptibility of the major tourist destinations in Asia to the uncertainties may encumber the arrival of international tourists. However, to the best of our understanding the research in this area is scant. Further though the tourism‐uncertainty nexus has been widely explored in the literature, the deliberations in a time‐varying and frequency‐domain framework continue to be ignored. Thus, building upon the earlier deficiencies this study, investigates in a time and scale varying framework the tourism uncertainty/risk nexus for Singapore and Malaysia, two major tourist destinations in the Asian region. This article endeavors to investigate the role of world policy uncertainty, US Monetary Policy Uncertainty, geopolitical risk, Global Financial Stress Index and market volatility on international tourist arrivals in Malaysia and Singapore. To the best of our understanding, this study is the foremost to scrutinize the interrelatedness between these variables in a multivariate wavelet causality framework, from January 2000 to April 2020. Our results show that the global uncertainties manifested through different macro‐based indicators affect international tourism demand for the destination countries. During the subprime crisis, Euro‐crisis and the pandemics significant coherencies are perceptible. The major empirical findings of the impact of uncertainty indicators and geopolitical risk on international tourist arrivals over the recent period have important implications for policy makers and practitioners.

Volatility Spillover Between Bitcoin And Financial Stress Index

Purpose: This paper aims to test the volatility models for Bitcoin (BTC) and the financial stress index (FSI) and examine the volatility spillover among them. This aim was reached by obtaining weekly data from the 7th of January 2011 and the 24th of December 2021. Methodology: First, volatility modelling for the series is provided, and GARCH (1,1) for the BTC series and IGARC (1,2) for the FSI series are determined as the most appropriate volatility models. Then, residual volatility series are created for each variable over the IGARCH (1,2) and GARCH (1,1) models for the volatility spread between the series. The volatility spread between the series is examined with the diagonal VECH GARCH method. It is concluded that there is a positive volatility spillover effect from the FSI variable to the BTC variable. Then, impulse-response analysis is performed on the volatility residual series created for each variable. The empirical findings from impulse response analysis support a risk transfer between BTC and FSI series. Results and Findings: Changes in the BTC return series and FSI series are caused mainly by themselves, and the series are most affected by their shocks. By comparing the variance decomposition of the volatility series with the analysis results, it can be said that the changes in the volatility series are caused mainly by each other.

What can we learn from financial stress indicator?

This paper investigates whether the information of the financial stress index has predictive power for stock returns. The empirical results show that the financial stress index is efficient in predicting stock returns. In addition, the financial stress index can provide incremental information based on 14 traditional macroeconomic variables. Considering different investor risk aversion coefficients, the financial stress index has the highest CER and SR gains among the predictors. Our paper tries to provide new evidence for stock return predictability from the perspective of financial stress.

Time-frequency analysis of financial stress and global commodities prices: Insights from wavelet-based approaches

We examine the time-frequency lead–lag relationships and the degree of integration between the US financial stress index and global commodity prices (i.e., oil, gold, silver, and cocoa) with data covering over 47 decades (January 1975 to December 2021). For this purpose, we resort to the bi- and multiple wavelet econometric approaches. Findings from the bivariate wavelet analysis evidence the significant influence of the US financial stress in driving the price-generating process in commodities markets. Our findings support the hedging abilities of commodities across the time-frequency space. Findings from the multiple correlations explicate that the interrelation between the commodities and financial stress is attributable to their interdependence in the long term during financial market meltdowns. The dynamic and nonhomogeneous lead/lag relations underscored by our findings highlight the importance of cross-commodity investments. As such, by acknowledging the response of different commodities to financial stress, asset allocation should factor in commodities that offer opposing responses to a financial stress to hedge downside risks associated with portfolios. Our findings are of interest to regulators, risk managers, investors, and commodities producers.

Impact of transition risks of climate change on Chinese financial market stability

Transition risks caused by climate change are becoming an important issue in finance research. In this study, we first construct the transmission mechanism of “climate change–change in investor attention–financial stability” and analyze the impact of transition risks on Chinese financial market stability. Second, we construct a Climate Change Index (CCI). The CCI depicts changes in investors’ attention to the transition risks of climate change based on the Baidu Index. We also use the dynamic factor model (DFM) to construct a Financial Stress Index (FSI) that describes the Chinese financial market stability. The FSI can also effectively identify financial events within the sample interval. We then use the TVP-VAR model to empirically analyze the impact of the transition risks of climate change on Chinese financial market stability. We present the following results: in the short and medium terms, more financial market pressure will be caused by an increase in the CCI. In the long run, the impact of an increase in CCI on Chinese financial market pressure is uncertain. Finally, we present valuable countermeasures and suggestions from the different perspectives of investors, financial institutions, and regulators.

The Predictive Power of Financial Stress on the Financial Markets Dynamics: Hidden Markov Model

This study investigates the predictive power of the financial stress on the dynamic of the Middle East and North Africa (MENA) financial market returns from 2007 to 2021. Based on a Quantile Regression, we show that financial stress has highest predictive abilities at the lower quantiles when the market is bearish. Then, we propose a Hidden Markov Model (HMM) based on the transition matrix to understand the relationship between financial stress index and the MENA stock market dynamics. We find that the effect of financial stress on stock market return reveals the persistence of regimes: Bullish state exists and persists, and has the longest conditional expected duration for the majority of MENA markets, except Bahrain, Qatar and Jordan. However, the transition probability from the bullish to the calm regime is too low for the financial market of Bahrain, United Arab Emirates and Egypt. Besides, the estimated mean returns for each regime divulge that the bearish and calm states are more attractive destination for both portfolio managers and investors.

Time-varying predictability of financial stress on inequality in United Kingdom

PurposeExisting 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).Design/methodology/approachThe 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.FindingsThis 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.Originality/valueAccurate 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.

Measurement and Forecasting of China's Financial Stress Index in the A-E-G Coupling Perspective

After the outbreak of the global financial crisis in 2008, many scholars began to quantify financial risks and find alternative variables to reflect financial risks better. The financial stress index is a good alternative. In this paper, nine important factors of economic aggregate, economic benefit, and economic structure are selected to construct China's Financial Stress Index (CFSI). Then, the coupling identification of AHP and entropy weight method (A-E) is carried out, and the CFSI and its fluctuation are predicted by the grey dynamic prediction model GM(1, N). The results show that the CFSI constructed by a-E coupling weight can measure the pressure of China's financial system more accurately, and the CFSI is predicted to show A gentle decline trend from 2022 to 2024. Inspired by previous studies by scholars, this paper tries to construct and predict CFSI from the perspective of A-E-G coupling, combining subjective and objective, and hoping to open new ideas on CFSI construction and prediction.

Asymmetric economic effects via the dependence structure of green bonds and financial stress index

In this study we examine the connectedness and spillovers of financial stress with corporate green bonds that has hardly attracted any attention in the relevant literature. To the best of our knowledge this is the first research attempt to connect and locate any impact of the new financial and environmental tool of green bonds in financial stress which implies respective social impacts. Significant results are extracted by our analysis. The most important is that in contrast to what financial stress theory holds we locate a causal relationship from green bonds to financial stress. The transmission from green bonds to financial stress becomes clearer by using the Bayesian VAR model. We confirm the robustness of our results by using other valid indices for our main variables. Furthermore, our analysis provides important implications for global economic growth, for investors aiming to achieve better risk-adjusted performance but also decarbonization and sustainability of their portfolios and for policymakers in designing and implementing policies aiming to the mitigation of the climate change risk and the transition to a more sustainably economy.

Gold, silver, and the US dollar as harbingers of financial calm and distress

In this paper, we investigate the relationship between gold, silver, and the US dollar returns and financial stress to shed light on the circumstances where these assets serve as attractive investment vehicles and whether the assets signal financial conditions ahead. Using weekly data from 1994 to 2020 and predictability-in-mean, predictability-in-variance, and predictability-in-distribution, we examine the relationship between returns on gold, silver, and the US dollar and the St Louis Financial Stress Index (STLFSI). While we find no Granger predictability in the mean between gold returns and the aggregate STLFSI, there is some evidence of Granger predictability between silver and US dollar returns and financial stress. However, test results show significant bidirectional Granger predictability in variance between STLFSI and gold, silver, and US dollar returns. Predictability-in-distribution tests generally show significant bidirectional relationships between financial stress and gold, silver, and US dollar returns at the left and right tail of the distribution. We confirm the safe-haven properties of gold, silver, and the US dollar and find novel evidence that very low returns on these assets signal financial calm, and unusually high returns signal high financial stress ahead. In this sense, extreme gold, silver, and US dollar returns are harbingers of calm times or financial distress to come, acting as early financial market news providing risk guideposts for safety.

The role of financial stress, oil, gold and natural gas prices on clean energy stocks: Global evidence from extreme quantile approach

Sustainable economic growth is the prime concern of each country without compromising environmental sustainability. Therefore investors seek the opportunities such as investment in clean energy stocks having significant social, economic, and environmental benefits for the society. Existing studies have explored various macroeconomic factors that deeply influenced the performance of clean energy stock. However, none of the studies has examined the nexus of financial stress, commodity price volatility (Oil, natural gas, gold), and clean energy stock from a global perspective. The study fills the gap by exploring the dynamic relationship among the study variables and employs the advanced methodology of “The quantile autoregressive distributed lag "(QARDL) approach from January 1, 2008, to April 30, 2021. The empirical findings of the study suggest that increased financial stress index and oil & gold prices significantly depress the performance of clean energy stocks in the long-run and short-run, whereas the natural gas has a positive influence on the clean energy stock only in the long run while in the short-run it has no significant influence. The study significantly provides a clear picture of these macroeconomic variables' interdependencies in all market conditions based on the empirical outcomes. This will provide greater investment opportunities for investors and helps the policymakers to establish better policies and strategies to boost global clean energy stock performance.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

Financial Stress Index as an Indicator of Financial Stability of Ukraine in War Conditions

The importance of proper statistical support of the financial and banking system of the country is substantiated. It was noted that the statistics of the financial sector forms indicators of monetary and credit statistics, statistics of financial markets, statistics of financial accounts and indicators of financial stability. The criteria of financial stability were described: the financial system is liquidated and capitalized; payments and settlements are studied on time; the financial system effectively transforms free funds of citizens and businesses into loans and investments, etc.
 The goal of macroprudential policy is given and the system of indicators that characterizes the level of financial stability of the country is described. It was defined the key groups of indicators, on the basis of which the models of stress testing of banking systems by the National Bank of Ukraine are implemented. Particular attention was focused on the financial stress index, which covers five sub-indices (banking sector, corporate securities, government securities, foreign exchange market, household behavior) and takes into account the effect of their correlation.The dynamics of the IFS for January - May 2022 were analyzed. It was noted that at the beginning of January, the financial stress index was 0.01, and as of February 24, 2022, it has grown rapidly to 0.466 for all components, which indicates the systemic nature of stress for the financial sector in general. It was noted that the level of the sub-index of household behavior was relatively stable, since there was no significant outflow of deposits due to the preservation of public trust in the banking system. The value of the banking sector sub-index (0.075) improved by the end of May thanks to the improvement in the level of liquidity, while the currency sub-index (0.113) maintains quite high values. In May, the IFS fell to 0.265, but the level of stress in the financial market remains quite high by historical standards.The structure of the money supply was analyzed and it was determined that its share of cash was a third of the entire money supply in circulation. The share of cash in February and March 2022 significantly increased compared to the corresponding months of 2021 - by 3.2 percentage points and by 2.9 percentage points, respectively. The share of deposits in the national currency in March 2022 amounted to 34.7%, which is 6.0 percentage points more than compared to March of the previous year. It was determined that the credit demand of the population decreased significantly during the war, in particular, the attractiveness of long-term deposits decreased. Based on the oscillation coefficient, it was determined that in the first half of 2022, the official exchange rate hryvnia to dollar was more stable than the hryvnia to euro exchange rate.The financial losses of Ukraine as a result of the war were characterized. Thus, losses of physical capital from the destruction of enterprises, housing and infrastructure reached 100 billion dollars of the USA, which is equivalent to 50% of GDP in 2021. It is noted that currently the main source of covering the budget deficit is international aid.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.