Financial Stress Index Research Articles

Financial stress dynamics in the MENA region: Evidence from the Arab Spring

In this paper we analyse the impact of instability caused by the Arab Spring on the co-movements and volatility spillovers of aggregated Financial Stress Indices for eight MENA countries. Using a dynamic frequency connectedness framework, we conclude that stress transmission between markets is higher at low frequencies than at high frequencies, which implies that MENA markets are slow in adjusting to the information they receive. The Global Financial Crisis generated stronger spillover effects between MENA markets than the political turmoil of the Arab Spring. These results are useful for investors with different investment horizons, and have policy implications for the maintenance of financial stability in this region.

Time-varying predictability of oil market movements over a century of data: The role of US financial stress

In this paper we analyze whether a news-based measure of financial stress index (FSI) in the US can predict West Texas Intermediate oil returns and (realized) volatility over the monthly period of 1889:01 to 2016:12, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fail to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from FSI to oil returns and volatility cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 75 percent and 80 percent of the sample periods, FSI in fact do strongly predict the oil returns and volatility respectively. Overall, our results highlight that FSI is helpful in predicting oil returns and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

Macro Factor Mimicking Portfolios

The estimation of risk factors and their replication through mimicking portfolios are of critical importance for academics and practitioners in finance. We propose a general optimization framework to construct macro-mimicking portfolios that encompasses existing mimicking approaches, such as two-pass cross-sectional regressions (Fama and MacBeth, 1973) and maximal correlation portfolio approach (Huberman et al., 1987). We incorporate machine learning estimation improvements to mitigate the impact of estimation errors in the observed macro factors on mimicking portfolios. We provide an application to the construction of mimicking portfolios that replicate three uncorrelated global macro factors: namely growth, inflation surprises, and financial stress indicators. We show how these machine-learning mimicking portfolios can be used to improve the risk-return profile of a typical endowment asset allocation.

Not the usual suspects: Critical indicators in a dollarized country's Financial Stress Index

In this paper we investigate the importance of the dollarization rate in heavily dollarized economies as one of the indicators in a Financial Stress Index. We use data on Lebanon, a middle-income developing country, considered to be one of the highest dollarized countries in the world to construct the first Financial Stress Index for Lebanon. Our findings indicate that the dollarization rate is the most important indicator for the FSI. This should be of relevance to many dollarized countries such as Bolivia, Peru, Uruguay, and others. Remarkably, none of the existing FSIs for dollarized countries has included the dollarization rate.

Transmission mechanisms of financial stress into economic activity in Turkey

Measuring, analyzing and understanding systemic risk in financial system have become very important in the light of the recent global crisis. In this study, we follow Holló, Kremer, and Lo Duca (2012) and evaluate systemic stress of financial system of Turkey with a high frequency (daily) financial stress index which consists of daily 13 financial market indicators. Dynamics of the financial stress index indicate that the index creates proper signals to the well-known financial stress events. The dynamic interaction between financial stress and real economic activity is investigated with application of structural VAR (SVAR) model. Results of the study suggest that deterioration of financial conditions impacts real economic activity significantly and adversely.

High-Dimensional Sparse Financial Networks Through a Regularised Regression Model

We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectation-Maximisation algorithm. The proposed model accounts for two sets of covariates. The first set contains predetermined variables which are not penalised in the model (i.e., the autoregressive component and common factors) while the second set of variables contains all the (lagged) financial institutions in the system, included with a given probability. The financial linkages are expressed in terms of inclusion probabilities resulting in a weighted directed network where the adjacency matrix is built “row by row. In the empirical application, we estimate the network over time using a rolling window approach on 1248 world financial firms (banks, insurances, brokers and other financial services) both active and dead from 29 December 2000 to 6 October 2017 at a weekly frequency. Findings show that over time the shape of the out degree distribution exhibits the typical behavior of financial stress indicators and represents a significant predictor of market returns at the first lag (one week) and the fourth lag (one month).

Measuring financial systemic stress for Turkey: A search for the best composite indicator

In this study, we aim to construct a single financial stress indicator (FSI) for Turkey adopting weekly data from between April 2005 and December 2016. To do so, we compose 15 different FSIs using 14 variables that will represent five different markets, i.e. the money market, the bond market, the foreign exchange market, the equity market and the banking sector. We aggregate these five different markets using a variety of techniques, including principal component analysis (PCA), basic portfolio theory, variance equal weights and the Bayesian dynamic factor model. We compare 15 different FSIs on the basis of their relation to, and the forecasting power of, different variables such as the growth rate of industrial production, the OECD business condition index and the OECD composite leading indicator for Turkey. Our results suggest that there is no simple best indicator for Turkey to measure financial systemic stress. Some indicators offer good forecasting power for economic growth while others have a stronger correlation with systemic risk. Therefore, we offer a final FSI for Turkey conducting a model averaging method via a rolling correlation based weighting scheme to benefit from the information content of all the FSIs and observe that the final FSI successfully indicates the tension periods.

The OFR Financial Stress Index

We introduce a financial stress index that was developed by the Office of Financial Research (OFR FSI) and detail its purpose, construction, interpretation, and use in financial market monitoring. The index employs a novel and flexible methodology using daily data from global financial markets. Analysis for the 2000–2018 time period is presented. Using a logistic regression framework and dates of government intervention in the financial system as a proxy for stress events, we found that the OFR FSI performs well in identifying systemic financial stress. In addition, we find that the OFR FSI leads the Chicago Fed National Activity Index in a Granger causality analysis, suggesting that increases in financial stress help predict decreases in economic activity.

THE FINANCIAL CONNECTEDNESS BETWEEN EUROZONE CORE AND PERIPHERY: A DISAGGREGATED VIEW

This paper examines the financial stress interconnectedness among Greece, Ireland, Italy, Portugal, and Spain (GIIPS) economies and Germany. Based on market-level financial stress indices, we examine the stress transmission process as well as the causal network relationships in banking sector, bond, money, and stock markets. The period under investigation, 2001–2013, allows to test the effects of the 2007–2009 financial crisis as well as the subsequent European sovereign crisis. Using two alternative techniques for connectedness analysis, our evidence suggests that the peripheral economies of Italy and Spain play a highly significant role in the stress transmission in all markets, especially in the cases of banks and equity markets. Moreover, we visualize our results using network analysis. Contrary to common wisdom, Portugal, Ireland, and mainly Greece do not seem to have an important role in amplifying stress levels.

Индекс финансового стресса как обобщающий индикатор финансовой нестабильности

The paper provides an overview of the methods and models for quantitative evaluation of the financial instability of economic systems. The authors identify the indicators of financial instability and show their connection with various stress tests, assessing the vulnerability of economic systems to various shocks. The main results of Russian and foreign studies devoted to the development of early warning systems of financial instability, including various leading indicators of currency, debt and banking crises, are discussed. Particular attention is paid to the experience of building an aggregated financial stress index (FSI) and its use as a generalized indicator of financial instability of economic systems. Using the vector model for error correction, the authors construct cointegration equations for three main variables demonstrating a quick response to external shocks: oil price, ruble exchange rate against the US dollar, and the RTS index. The application of the principal component analysis allowed the authors to aggregate adequately the information contained in these variables. Based on the moving average and standard deviation of the principal components’ growth rates, the authors propose a new financial stress index. In addition, with the help of this index the authors obtained estimates of the level of financial stress in the Russian economy from January 2000 up to February 2019 and revealed differences in the nature of financial stress during the 2008–2009 and 2014–2016 crisis periods.

Оценка трансграничной трансмиссии системного финансового риска в странах ЕС

Amid global financial instability timely identification and propagation of systemic risks within both national and international banking systems becomes essential. It implies that it is highly important to assess not only inner factors of systemic banking risk and channels of its propagation, but also external sources which are due to cross-boarder spread of systemic banking risks. This article focuses on researching cross-boarder mechanism of systemic risks spread using method of decomposition of the variance of errors of VAR-models basing on monthly aggregated country-level indices of financial stress for EU-countries (CLIFS: Country-Level Index of Financial Stress). This investigation seems to be acute for coordinating macroprudential policies of national regulators of EU- countries and mastering macroprudential policy of Bank of Russia due to high globalization of financial markets of EU-countries and growth of financial innovations. The matrix of connectedness of 27 countries belonging to EU allows to obtain index of systemic connectedness (84 %), which reflects high dependence of each country on external shocks transferred from other countries-members of EU. Indices of net connectedness allow to divide countries into two groups: net-acceptors and net-donors of systemic risks. Empirical analysis has confirmed the hypothesis that countries subject more to inner shocks — Greece, Cyprus, Bulgaria and Croatia-have really survived starting from 2008 banking crises, explained with internal imbalances of banking systems.

Индекс финансового стресса для России: новые подходы

The paper introduces a new monthly index of financial stress for Russia for the March 2008 - March 2018 period. The index is based on 12 well-established and mostly publicly available standalone metrics of financial instability, including credit-to-GDP gap, debt-service-ratio and real estate price index, provided by the BIS. I seek an optimal method to aggregate the metrics to derive a composite index. Based on the local projections technique [Jorda, 2005, 2009] and Bayesian model averaging, I show that conventional aggregation methods such as principal component analysis (PCA) can be outperformed by the approaches, better capturing the nonlinear and non-Gaussian nature of the standalone indicators of financial instability. Namely, the dynamic factor model with a single factor fares best of all the considered methods.The composite index based on the dynamic factor model accurately captures the dynamcs of financial instability in the Russian financial sector, with the peaks occurring in the late 2008 and the late 2014 - early 2015. I also show that the financial stress index exerts an adverse effect on industrial production alongside the VIX index, explicitly accounting for oil prices, global and domestic indices of economic policy uncertainty as well as geopolitical risk. This negative effect of financial stress exhibits persistence in the medium run.

Financial Stress Dynamics in MENA Region: Evidence from the Arab Spring

We analyse the impact of instability caused by Arab Spring on co-movements and volatility spillovers of aggregated Financial Stress Indices for eight MENA countries. Using a dynamic frequency connectedness framework, we find that stress transmission between markets is higher at low frequencies than at high frequencies, which implies that MENA markets are slow in adjusting the information they receive. The Global Financial crisis generated a stronger spillovers effect between MENA markets than political turmoil of the Arab Spring. These results are useful for investors with different investment horizons, and have policy implication for maintenance of financial stability in this region.

Financial Stress Connectedness and Network Analysis

Financial instability in one country may quickly spread out to the rest of the world on different transmission channels. This leads to financially connected countries and creates a risk of rapid contagion of the financial crisis. The last global financial crisis was an example of this contagion effect. Therefore, the financial connectedness of both developed and developing countries is very important in order to determine the spillovers of financial stress across countries. In this study, we follow Diebold and Yilmaz approaches and examine total, directional and net spillovers between high frequency (daily) financial stress indexes of 9 countries from 2007/03/17 through 2016/11/17. In addition, we analyze the 200-day rolling window of the total spillover index with well-known financial stress events. Finally, the network topology of directional spillovers is given to visualize financial connectedness.

금융불안지수 개발과 금융불안 요인 변화 분석 (Constructing a Financial Stress Index and Changes of Financial Stress Determinants after the Global Financial Crisis)

Korean Abstract: 본 연구는 한 나라의 금융불안을 측정할 수 있는 KIEP 금융불안지수를 개발하고, 이를 활용해 우리나라와 신흥국에 대해 금융불안 결정요인 변화를 분석하였다. 그리고 미국 금리인상, 중국 금융불안, 통상불확실성 등 대외 충격이 우리나라 금융불안에 미치는 영향을 분석하였다. 이를 위해 세계 주요 신흥국을 대상으로 국가패널 분석과 우리나라를 대상으로 시계열 및 구조 VAR 분석을 실시하였다. 본 연구결과는 글로벌 금융위기 이후 주요국의 금융불안 결정요인 변화에 대한 이해의 폭을 넓히고, 우리나라의 금융불안 리스크 관리 강화 및 금융불안 완화 방안 마련에 활용될 수 있을 것으로 기대된다 English Abstract: Following the global financial crisis in 2008, various forms of financial instability have continued to emerge due to the European fiscal crisis, US taper tantrum, plunge in resource prices, and worries about a hard landing in the Chinese economy. This financial stress is likely to continue on the domestic and international fronts when considering the deepening US-China trade war, sustained US interest rate hikes, and economic unrest in China and emerging economies. Upon this backdrop, this study develops a financial stress index which can measure financial instability, and analyzes changes in determinants of financial instability in Korea and emerging economies, as well as the influence of various external shocks on Korean financial stress.

Financial Stress Index of Turkey and Determination of High Stress Periods by Markov Regime Switching Model

Gelişmekte olan ülkeler, küresel piyasalardaki gelişmelere ve sermaye hareketlerine karşı daha duyarlıdır. Bu sebeple finansal piyasalardan kaynaklı sorunlarla karşılaştıklarında oluşan finansal stresin ekonomik toparlanmayı tehlikeye atacak kadar yüksek olmaması gerekmektedir. Bu çalışmada Türkiye için 1990:01-2017:02 döneminde finansal istikrarı izlemek amacıyla bir finansal stres endeksi oluşturularak politika yapıcılara fayda sağlanması amaçlanmıştır. Endekste yer verilen değişkenler, finansal piyasalardaki yüksek stres durumlarını tanımlayan değişkenler arasından seçilmiştir. Bu finansal stres endeksi için Markov rejim değişim modelleri tanımlanmış, bu modeller yardımı ile finansal piyasalardaki düşük stres, normal stres ve yüksek stres dönemleri belirlenmiştir. Yüksek stres dönemlerinin 1991, 1994, 1998, 2000-2001, 2008 kriz yıllarında yoğunlaştığı bulgusuna ulaşılmıştır. Bu da oluşturulan finansal endeksin Türkiye krizlerini öngörmede başarısını göstermektedir.

Protocol for the Development of a Food Stress Index to Identify Households Most at Risk of Food Insecurity in Western Australia.

Food stress, a similar concept to housing stress, occurs when a household needs to spend more than 25% of their disposable income on food. Households at risk of food stress are vulnerable to food insecurity as a result of inadequate income. A Food Stress Index (FSI) identifies at-risk households, in a particular geographic area, using a range of variables to create a single indicator. Candidate variables were identified using a multi-dimensional framework consisting of household demographics, household income, household expenses, financial stress indicators, food security, food affordability and food availability. The candidate variables were expressed as proportions, of either persons or households, in a geographic area. Principal Component Analysis was used to determine the final variables which resulted in a final set of weighted raw scores. These scores were then scaled to produce the index scores for the Food Stress Index for Western Australia. The results were compared with the Australian Bureau of Statistics’ Socio-Economic Indexes for Areas to determine suitability. The Food Stress Index was found to be a suitable indicator of the relative risk of food stress in Western Australian households. The FSI adds specificity to indices of relative disadvantage specifically related to food insecurity and provides a useful tool for prioritising policy and other responses to this important public health issue.

Domestic financial instability and foreign reserves accumulation in China

AbstractUsing a time series analysis, this paper argues that domestic financial instability, which increases the potential for resident‐based capital flight from the domestic currency, provides an incentive for China to hold more foreign reserves in the short run. To measure its domestic financial conditions, we construct a monthly Chinese financial stress index, which is used as a proxy for the possibility of capital flight. The empirical results show that this index is a significant determinant of the movements of China's foreign reserves around its trend and that using M2 as a proxy for domestic financial instability as suggested by previous studies is not a valid strategy for China. It is suggested that greater attention should be given to the role of domestic financial conditions in explaining China's short‐run demand for foreign reserves.

The Relationship Between Price Stability and Financial Stability: Evidence From Major Emerging Economies

ABSTRACTWe examine the relationship between price stability and financial stability for major emerging economies using a Markov regime-switching model. Empirical results suggest that monetary policy is consistent with the Taylor rule in all countries except for India and all countries followed both low and high inflation targeting monetary policy regimes. Low inflation targeting regime seems to be more persistent and has higher duration than high inflation targeting regimes except for Indonesia and South Africa. All countries seem to have had financial stability concerns when they formulated their monetary policy as the coefficient of the financial stress index is statistically significant at least in one regime. Overall the results suggest that Taylor rule-based monetary policies have been implemented to various degrees in major emerging economies to achieve economic stability, price stability, and financial stability.

RETRACTED: IoT study on the total risk management and cluster-coordinated development based on synergy theory

Abstract Based on the new perspective of the financial crisis contagion channel of the international listed companies, this Paper aims to a real-time, targeted and international financial risk warning index system of listed companies and synthesize the financial stress index of listed company that adapt to China’s situations from such three dimensions as foreign exchange market, banking industry and stock market. The empirical study of financial risk early warning in Chinese listed companies based on the Markov regime-switching model has shown that the M2/GDP growth rate, the volatility of stock market and the foreign trade dependence have a positive relationship with the current financial risk of the listed companies in China; stock market returns and foreign exchange reserves/GDP have inverse relationship with the financial risks of listed companies; the financial risks of listed companies in China mainly come from the excessively loose monetary policy during the crisis and the imperfection of the stock market and its regulatory system. The forecast shows that China will be in the state of low financial risk in listed companies from 2014 to 2015.