Financial Stress Index Research Articles

Risk Prediction and Prevention Strategies Used by the Financial Industry Are Supported by Artificial Intelligence Technologies

With the advancement of artificial intelligence (AI) and big data technology, traditional financial management faces challenges in meeting modern needs. China's financial sector, although late to start, is embracing AI to mitigate risks like business and credit risks. This study explores risk prediction and avoidance methods in finance using AI. It employs elementary catastrophe theory to analyze dynamic system discontinuity, identifying high-risk periods when the calculated financial stress index (FSI) exceeds 1. Each unit increase in FSI affects its lag period by 0.7327 units. Macro-economic pressure, reflected in the macroeconomic pressure index (MSI), significantly impacts FSI, with the current period having a more pronounced effect than the lag period. Financial institutions must evolve talent into hybrid professionals and invest in AI to enhance risk management while pursuing profitability.

Dynamic spillovers of various uncertainties to Russian financial stress: Evidence from quantile dependency and frequency connectedness approaches

Being geopolitically exposed, the Russian financial sector is vulnerable to various ­uncertainties. The aim of the article is to examine the quantile movements and dynamic connectedness of uncertainty indices with the financial stress index of Russia employing the cross-quantilogram (CQ), recursive cross-quantilogram (R-CQ) and TVP‑VAR dynamic connectedness using monthly data from July 2011 to August 2023. It is found that for Tweeter-based Economic Uncertainty (TEU) and Global Economic Policy Uncertainty (GEPU), there is strong positive dependence on the Russian Financial Stress Index (RFSI) in the bearish states of market in the initial memory, and the strength of this positive spillover effect gradually wilts towards longer memory structures. Unlike the GEPU, Russian Economic Policy Uncertainty (REPU) has long-lasting heterogeneous spillover effects on RFSI. Though there are significant positive, as well as negative, spillover ­effects of Global Geopolitical Risk (GGPR) on RFSI in the initial memory, across the longer­ memory structures these entire heterogeneous effects wash out. However, Russian Geopolitical Risk (RGPR) have long-lasting heterogeneous spillover effects on RFSI, unlike GGPR. GEPU, GGPR and RGPR were the net transmitters while RFSI, TEU and REPU were net receivers of volatility shocks. Since RFSI shows resilience over the long-term horizon to global geopolitics and economic uncertainty, investors are advised to keep patience and hold their capital/investment up to a minimum of 1 year in Russian financial system in order to be rewarded with positive returns.

International financial integration and financial stress of emerging market economies: The role of institutional quality

In the wake of unprecedented financial liberalization policies adopted by Emerging Market Economies (EMEs) since the late 20th century, their integration into global financial markets has brought forth both opportunities and challenges. This paper endeavors to assess the intricate relationship between international financial integration (IFI) and financial stress in EMEs, focusing on the potential moderating effect of institutional quality. Addressing key questions, it examines how various IFI indicators impact financial stress and evaluates the role of different dimensions of institutional quality in influencing this relationship. Using a Financial Stress Index (FSI) and employing the panel threshold regression technique, the study unveils significant threshold effects of institutional quality on the IFI-FSI nexus, with variations across different institutional factors. This study underscores the vital need for policymakers to identify threshold levels in institutional quality indicators to strike a balance between attracting investments and preventing unwarranted financial distress.

Global uncertainty and exchange rate conditions: Assessing the impact of uncertainty shocks in emerging markets and advanced economies

Economic and financial conditions respond significantly to increased measures of uncertainty, particularly in economic policy, monetary policy, and financial markets. This research employs the global vector autoregression (global VAR) model to assess the response of bilateral exchange rates, exchange rate volatility, exchange market pressure, and nominal and real effective exchange rates to shocks in global economic and financial uncertainty, along with U.S. monetary policy uncertainty, U.S. equity market volatility, and U.S. financial stress indicators. Spikes in uncertainty correspond to immediate increases in exchange rate volatility, buildup of exchange market pressure, and depreciation for emerging markets. The results are compared to advanced economies, China, and Russia separately, identifying key differences across markets.

The spillover effects among financial stress, investor sentiment, and GCC stock markets: evidence under the bearish and bullish market states

ABSTRACT This study delves into an exploration of volatility spillover between financial stress, investor sentiment, and stock market index returns in the Gulf Cooperation Council (GCC) countries using the DCC-GARCH, wavelet coherence, and BEKK-GARCH models. A robustness check, applying Diebold and Yilmaz’s (2012, 2014) methodology, verifies network measures across markets. Significant patterns in financial stress index were observed during crises, with strong co-movement between financial stress, investor sentiment, and stock returns, especially during COVID-19 pandemic. Volatility transmission from stock returns to investor sentiment and the financial stress index was notable in some markets. Additionally, we found a significant volatility transmission from the financial stress index to market returns during periods of bearish stress in Bahrain and Kuwait and bullish stress across all countries. These findings offer insights for investors and fund managers optimizing portfolios and managing risk exposure.

Using machine learning to forecast clean energy, commodities, green bonds and ESG index prices: How important is financial stress?

PurposeThis study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.Design/methodology/approachWe utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.FindingsThe results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.Practical implicationsThis research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.Originality/valueTo the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

The time-varying impact of geopolitical risks on financial stress in China: A TVP-VAR analysis

This study employs the TVP-VAR model to investigate the dynamic impact of geopolitical risk (GPR) on China's Financial Stress Index (FSI). The results indicate that GPR is a significant source of financial stress in China. Geopolitical shocks during major geopolitical events or specific periods lead to sharp increases or decreases in China's FSI, demonstrating a complex time-varying impact mechanism. The effects of GPRT and GPRA on FSI show clear time-varying characteristics and structural changes. The impact of GPRC on China's FSI has shifted over time.

Exploring the influence of Silicon Valley Bank default and U.S. financial stress on sectoral interactions and effective hedging strategies

ABSTRACT The SVB implosion could trigger a domino effect, shaking confidence in U.S. banks. This research estimates the response of good and bad cumulative abnormal returns (G-CARs, B-CARs) on the event day, pre-event and post-event periods, using financial stress, oil, geopolitical and equity uncertainty as controls. We also examine the moderating impact of the SVB implosion and U.S. financial stress indices (US-FSI) on the good and bad dynamic conditional correlations between 132 U.S. sectoral pairings. Additionally, we assess the best portfolio distribution to determine if short positions in specific U.S. sectors offer better protection against SVB’s long-term volatility using the DCC-GARCH-t copulas. Overall findings suggest that B-CARs are more adversely affected than G-CARs. Therefore, investors need to consider bearish and bullish market conditions before devising hedging strategies. The Financials, Health Care, Industrial, Materials, Oil and Gas, Real Estate and Transportation sectors of U.S. experienced significant losses due to higher B-CARs around the SVB default event. Furthermore, the SVB default’s moderating impact on the U.S. sectoral stock returns is more pronounced during bearish market conditions compared to bullish ones following a Black Swan event. This suggests a differential moderating impact of SVB on good and bad conditional connectedness and a reduction in diversification benefits.

Financial Stress Amongst People Who Self-Harm in Sri Lanka

Objective Socioeconomic status deprivation is known to be associated with self-harm in Western countries but there is less information about this association in Low and Middle Income Countries (LMIC). One way of investigating this is to assess the prevalence of indicators of financial stress in people who self-harm. We have assessed the prevalence and correlates of day-to-day financial hardships amongst individual presenting with non-fatal self-harm to hospitals in Sri Lanka. Methods Data on non-fatal self-harm presentations were collected from an ongoing surveillance project in 52 hospitals in Sri Lanka. A questionnaire captured data on two forms of financial stress: unmet need (i.e., costs and bills that cannot be paid) and required support (i.e., steps taken to cover costs, such as selling belongings). Additional data on demographic, economic and clinical characteristics were also collected. Results The sample included 2516 individuals. Both forms of financial stress were very common, with pawning/selling items (47%) and asking family or friends for money (46%) in order to pay bills or cover costs being commonly reported. Greater financial stress was associated with being aged 26-55 years, limited education, and low socioeconomic position. Financial stress was greater in women than men after adjusting for other factors. Conclusion The results indicate that financial stress is commonly reported amongst individuals presenting to hospital with non-fatal self-harm in Sri Lanka, especially women. The research highlights a need to attend to financial stress both within self-harm prevention and aftercare.

Business and financial cycle across regimes: Does financial stress matter?

Identifying the business and financial cycles and their regimes is crucial for understanding the economy's state and financial market dynamics. Using a bivariate four-regime Markov-switching framework, we assess the accuracy of a model that identifies the business cycle by combining financial and real variables and compare it to a model that only uses real variables. We find that a model that incorporates information from a financial stress index outperforms a model based solely on real variables, not only during a financial crisis, but also, and more interestingly, during economic downturns. The empirical evidence provides insights for policymakers and investors to improve economic policy formulation, investment strategies, strategic decision-making, and proactive risk management. The results are robust to various model specifications.

Untangling the finance-growth nexus: The dual role of financial development in the transmission of shocks

Does financial development shield countries from the pass-through of financial shocks to real outcomes? We evaluate this question by characterizing the probability density of GDP growth conditional on foreign and domestic financial stress indicators in a panel of 24 emerging countries. Our robust results unveil a previously unexplored dual impact of higher degrees of financial development on the transmission of financial stress: while the effect of global factors is attenuated, the impact of domestic factors is exacerbated. This result highlights a previously unexplored channel through which financial development can alter the link between financial (in)stability and GDP growth.

THE EFFECT OF FINANCIAL STRESS ON STOCK MARKETS: AN EXAMPLE OF MINT ECONOMIES

Increasing uncertainties due to developments in financial markets lead to uncontrollable financial behaviors. Financial stress indices are created by taking into account many different financial indicators directly related to the financial system. So, it is important to examine the impact of financial stress indices on stock markets, which have an important share in financial markets. The paper aims to investigate the short and long-term effects of emerging markets' financial stress index (EFSI) and global financial stress indices (GFSI) on the stock markets of MINT (Mexico, Indonesia, Nigeria, and Turkey) economies. For this purpose, analyses were made using the ARDL (Autoregressive Distributed Lag) Bounds Test method using weekly data for the period 10/01/2014-26/04/2024. It has been determined that EFSI and GFSI negatively affected the benchmark stock market indices of all MINT economies in the short term and that the negative effect continued in the long term but it has been significant for only EFSI in all MINT economies, as important results of the analysis. It has been determined that following financial stress indices can be a leading indicator for stock market investors. It is hoped that the results of the paper may be useful for financial market actors who are considering investing in these markets.

경제정책 불확실성이 한국 금융 불안에 미치는 영향 분석

Korea is a small open economy and is significantly affected by macroeconomic shocks in major countries. Previous studies have shown that financial markets such as the stock market and foreign exchange market are sensitive to financial instability and economic policy uncertainty in major developed countries. However, these studies have typically focused on individual financial markets and have not comprehensively analyzed how financial stress in Korea is affected by uncertainty in major developed countries from the perspective of financial stability. In this study, we use the spillover index proposed by Diebold and Yilmaz(2009, 2012) to analyze the impact of economic policy uncertainty (EPU) in the United States, Europe, Japan, and Korea on Korea's financial stress index (FSI). The study examines the influence of economic policy uncertainty in these major developed countries on Korea's financial stress over time. Furthermore, the findings indicate that during periods of heightened uncertainty, major developed countries have a relatively large impact on Korea's financial stress.

Revisiting the economic growth in the shadow of financial stress in times of crisis: evidence from FIGARCH and wavelet coherence approach

PurposeThis study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.Design/methodology/approachTo address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.FindingsFinancial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.Originality/valueThe study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

Global, local, or glocal? Unravelling the interplay of geopolitical risks and financial stress

Since the start of the previous decade, regionalization of trade and investment flows has led to complex coupling in local (country-level) and global risks. To-date, little is known about how global uncertainty interacts with local uncertainty across the financial systems. Our study investigates the nexus between global and country-specific Geopolitical Risks (GPRs) and financial stress index (FSI) in five highly-open, large developed economies. We employ the cross-quantilogram and cross-spectral quantile coherency approaches on weekly data over 2000–2022 to show heterogeneous dependency of local and global geopolitical uncertainty on the financial stress conditions. Specifically, our results show that in the U.S. and the UK dependency of local and global geopolitical uncertainty on the financial stress conditions is influenced by the varying market conditions. In contrast, Canadian FSI is intensified by both global and country-level geopolitical risks, whereas German and French financial systems exhibit significant resilience to the global and local geopolitical shocks. Hence, the latter markets show hedging properties vis-a-vis global geopolitical risks.

Quantifying the volatility spillover dynamics between financial stress and US financial sectors: Evidence from QVAR connectedness

This study uses quantile vector-autoregressive to examine volatility connectedness among a global financial stress index (including five categories: credit, equity valuation, funding, safe assets, and volatility) and US financial sectors under low, moderate, and extreme volatility conditions. The dataset includes the special periods covering the global financial crisis, China crisis, COVID-19 pandemic, Russian–Ukrainian war, Silicon Valley Bank failure, and Credit Suisse bank crisis. The findings imply that spillover effects among the series are higher during extreme volatility than during low and moderate volatility periods. During periods of low volatility, the credit category of the financial stress index and the US financial sector indices are net shock transmitters, but during extreme volatility periods, the US financial sectors become net shock receivers alongside the credit and funding categories of the financial stress indices. US financial sectors also exhibit net shock recipient roles at extreme volatility levels during those special periods.

A proposal for a composite indicator based on ratios (CIBOR) to compare the evolution of Spanish financial institutions

This paper introduces a new Financial Stress Indicator (FSI) named Composite Indicator Based on Ratios (CIBOR). This paper discusses the importance of monitoring the quality of loans and capital, operational performance, profitability, and liquidity of financial institutions to prevent systemic risks in the financial system. To address this, CIBOR is proposed as a means to indirectly capture the instability of a financial entity by identifying potential tensions and their underlying causes. Specifically, we compare the results for 25 financial entities operating in the Spanish banking market, analysing the evolution since 2018 to 2022. CIBOR permits a straight interpretation of the variations between periods and a dynamic analysis that not only measures the variation between the ratios over time, but also identifies the sources of such variations: variations derived from changes in sub-indicators, changes stemming from the oscillation of the baseline, and the impact of the selection of weights in the construction of the composite indicator.

Designing a Financial Stress Index Based on the GHARCH-DCC Approach and Machine Learning Models

Designing a Financial Stress Index Based on the GHARCH-DCC Approach and Machine Learning Models

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

A news-based economic policy uncertainty index for Nigeria

In this study, we develop the first daily news-based Economic Policy Uncertainty (EPU) index for Nigeria, which was previously not covered in recent EPU indices. The need to track economic uncertainties in Nigeria becomes crucial for investment and policy, especially with the renewed interest in the country as an important investment destination. To construct the EPU index, we use relevant keywords from articles in prominent newspapers in the country, covering the aftermath of the global financial crisis and the COVID pandemic, with a data scope of January 2010 to November 2022. We evaluate the predictability of the index by examining its connection with economic and financial variables like exchange rates, stock prices, and inflation in Nigeria. The results are robust to alternative model specifications, data frequencies, and multiple forecast horizons. We hope to extend this exercise to other useful indices, including Geopolitical Risk, Financial Stress Indicators, and Monetary Policy Uncertainty, which are not readily available for Africa, including Nigeria.