Financial Conditions Index Research Articles

Can the Australian Dollar and Canadian Dollar Be Predicted Using Financial Stress Index?

The purpose of this paper is to investigate if the St Louis Federal Financial Stress Index (STLFSI) can be used to predict the EUR/USD, AUD/USD and CAD/USD. Building on Gurrib (2018) who proposed a unified financial condition index and tested the predictability of the index on major foreign currency markets, this paper extends the analysis further allowing a comparison between the forecasts of the most actively traded currencies, tests for the model efficiency, and analyzes the actual and forecasted foreign currency values of the predicted model. Using weekly data over 1993-2018, and 1-week and 2-weeks ahead forecasts, the EUR/USD had the smallest normalized mean squared errors, with a significant p value of the index and homoscedasticity. Although series were stationary, the results were mixed across different currencies when lags were increased. The forecasted values were higher than the actual foreign currency values during the 2008-2009 crisis, and vice versa during the 2000-2002, explained by the STLFSI spiking up during the 2008-2009 event compared to the 2000-2002 events. The lower and upper band level under the 95% prediction interval, however, captured the effect of the global financial crisis of 2008-2009 and 2000-2002 events.

The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach

This paper investigates the impact of credit supply shocks on the macroeconomy and estimates a new financial conditions index. We calculated two credit supply factors using a time-varying parameter FAVAR model. The first factor is identified as the willingness to lend, while the second factor is the lending capacity. The impact of these two types of shocks and their changes over time is examined using Hungarian data. The two types of lending shocks affect macro variables rather differently: a positive lending capacity shock (in a banking system mostly owned by non-residents) influences GDP through a decrease in country risk and the easing of monetary policy, while willingness to lend primarily increases lending activity. The two financial shocks also differ in terms of their evolution over time: deviations from the average in the impact of a willingness to lend shock usually occur for short periods of time and are of a small degree between the various quarters. However, in the case of lending capacity, certain trends can be observed: before the crisis, the stability of the banking system played an increasing role in country risk, whereas after 2008 it appears that monetary policy paid increasing attention to financial stability. Finally, a new type of financial conditions index is quantified based on our estimates, which measures the impact of the banking system’s lending activity on GDP growth.

Assessment of Bad Loans in Turkey: What Role do Financial Conditions have?

This paper analyzes non-performing loans in the Turkish banking sector with a special focus on the role of financial conditions after the global crisis. In doing so, the empirical framework shows that the determinants of non-performing loans have changed after the crisis. In particular, non-performing loans are mostly shaped by bank-specific factors before the crisis, whereas these variables have a reduced effect after the global crisis. This finding is attributed to global developments in this period, which certainly had effects on both global and domestic financial conditions. The inclusion of financial conditions index as an additional regressor in the post-crisis episode also confirms this finding by yielding a statistically significant coefficient while the sign of the coefficient implies that tighter financial conditions cause higher non-performing loans. This supports our view that financial conditions take account for the dynamics of bad loans after the crisis. In the upcoming period, the course of global policy normalization may affect and even tighten financial conditions, thereby causing non-performing loans to gain momentum. All these analysis results signal challenges and prospects regarding the maintenance of financial stability and the conduct of monetary policy.

Can Countries Manage Their Financial Conditions Amid Globalization?

This paper examines the evolving importance of common global components underlying domestic financial conditions. It develops financial conditions indices (FCIs) that make it possible to compare a large set of advanced and emerging market economies. It finds that a common component, “global financial conditions,” accounts for about 20 percent to 40 percent of the variation in countries’ domestic FCIs, with notable heterogeneity across countries. Its importance, however, does not seem to have increased markedly over the past two decades. Global financial conditions loom large, but evidence suggests that, on average, countries still appear to hold considerable sway over their own financial conditions—specifically, through monetary policy. Nevertheless, the rapid speed at which foreign shocks affect domestic financial conditions may also make it difficult to react in a timely and effective manner, if deemed necessary.

Can Countries Manage Their Financial Conditions Amid Globalization?

This paper examines the evolving importance of common global components underlying domestic financial conditions. It develops financial conditions indices (FCIs) that make it possible to compare a large set of advanced and emerging market economies. It finds that a common component, 'global financial conditions,' accounts for about 20 percent to 40 percent of the variation in countries' domestic FCIs, with notable heterogeneity across countries. Its importance, however, does not seem to have increased markedly over the past two decades. Global financial conditions loom large, but evidence suggests that, on average, countries still appear to hold considerable sway over their own financial conditions-specifically, through monetary policy. Nevertheless, the rapid speed at which foreign shocks affect domestic financial conditions may also make it difficult to react in a timely and effective manner, if deemed necessary.

Broad Money Supply and Financial Condition Index for Indian Economy

In the context of recent global financial crisis, the impact of exogenous shock to the financial sector on the real sector of the economy has come to the notice of the economists all over the world. The traditional dichotomy between financial and real sector is found to be inadequate for either managing or predicting the economy's overall behavior. In view of this recent development, this paper makes an attempt to construct a Financial Condition Index (FCI) in the context of Indian economy, to explore the possibility of how such an index would be an improvement over the traditional Monetary Condition Index (MCI), which has been the central bank's main instrument. The FCI is constructed by incorporating broad money supply along with real interest rate and official exchange rate. Following a weighted sum approach, the index is constructed by taking into consideration macroeconomic data over two decades of time period. The FCI, thus constructed, shows a significant co-relationship with the variability economy's inflation rate, revealing its usefulness as an indicator for early warning system for Indian economy.

English

English

A Financial Conditions Index for the CEE Economies

Financial Conditions Indexes (FCIs) are analytical tools devised to synthesize the information contained in a set of financial variables in order to identify how financial conditions affect economic activity. In this paper, for each of the three main Central and Eastern EU member states outside the euro area (Hungary, Poland and the Czech Republic) an FCI is constructed as an unobserved factor estimated using the EM algorithm. After having assessed their performance in providing information about future economic activity (both in-sample and out-of-sample), these FCIs are used to describe the evolution of financial conditions in the three economies between 2001 and 2016. The overall findings of this study support a narrative whereby all three economies, after their integration into the EU, enjoyed very accommodative financial conditions until 2008; the Czech Republic and Hungary subsequently turned out to have been more exposed than Poland to the spillover effects from both the global financial crisis and euro sovereign debt crisis.

A Tourism Financial Conditions Index for Tourism Finance

The paper uses monthly data on tourism related factors from April 2005–June 2016 for Taiwan that applies factor analysis and Chang’s (2015) novel approach for constructing a tourism financial indicator, namely the Tourism Financial Conditions Index (TFCI). The TFCI is an adaptation and extension of the widely-used Monetary Conditions Index (MCI) and Financial Conditions Index (FCI) to tourism stock data. However, the method of calculation of the TFCI is different from existing methods of constructing the MCI and FCI in that the weights are estimated empirically. The empirical findings show that TFCI is statistically significant using the estimated conditional mean of the tourism stock index returns (RTS). Granger Causality tests show that TFCI shows strong feedback on RTS. An interesting insight is that the empirical results show a significant negative correlation between F1_visitors (Foreign Visitor Arrivals) and RTS, implying that tourism authorities might promote travel by the “rich”, and not only on inbound visitor growth. The use of market returns on the tourism stock sub-index as the sole indicator of the tourism sector, as compared with the general activity of economic variables on tourism stocks, is shown to provide an exaggerated and excessively volatile explanation of tourism financial conditions.

Comments by Don Hanna, on Financial Conditions Indexes and Monetary Policy in Asia

June 01 2017 Comments by Don Hanna, on Financial Conditions Indexes and Monetary Policy in Asia Author and Article Information Online Issn: 1536-0083 Print Issn: 1535-3516 © 2017 by the Earth Institute at Columbia University and the Massachusetts Institute of Technology2017Massachusetts Institute of Technology Asian Economic Papers (2017) 16 (2): 120–124. https://doi.org/10.1162/ASEP_a_00524 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Comments by Don Hanna, on Financial Conditions Indexes and Monetary Policy in Asia. Asian Economic Papers 2017; 16 (2): 120–124. doi: https://doi.org/10.1162/ASEP_a_00524 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsAsian Economic Papers Search Advanced Search Don Hanna: Because of the rise in the relative importance of capital flows and financial development across Asia in the run up to and following the 2008—09 global financial crisis (GFC) and the more recent deceleration in trade volumes relative to growth, monetary policy, with its closer link to capital flows and finance, has taken on more prominence. But the greater burden shouldered by monetary policy has also raised the question of what factors beyond simply inflation and growth should central banks use in calibrating monetary policy—or, more precisely, what transmission channels transmit monetary policy changes to the economy. From this concern has evolved work on financial conditions indexes (FCIs)—an effort to better measure the variety of channels through which monetary policy can affect an economy. Initially an effort to include the effects of exchange rate movements and money by the Bank of Canada and the Reserve Bank of New... You do not currently have access to this content.

Comments by Doo Yong Yang, on Financial Conditions Indexes and Monetary Policy in Asia

June 01 2017 Comments by Doo Yong Yang, on Financial Conditions Indexes and Monetary Policy in Asia Author and Article Information Online Issn: 1536-0083 Print Issn: 1535-3516 © 2017 by the Earth Institute at Columbia University and the Massachusetts Institute of Technology2017Massachusetts Institute of Technology Asian Economic Papers (2017) 16 (2): 118–120. https://doi.org/10.1162/ASEP_a_00523 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Comments by Doo Yong Yang, on Financial Conditions Indexes and Monetary Policy in Asia. Asian Economic Papers 2017; 16 (2): 118–120. doi: https://doi.org/10.1162/ASEP_a_00523 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsAsian Economic Papers Search Advanced Search Doo Yong Yang: This paper constructs financial conditions indexes (FCIs) for Asian economies using a common factor method. The authors found that bank-related indicators, various measures of financial stress and risk, and credit surveys fully reflect the state of the financial environment in Asia among a wide range of financial variables. Second, the FCIs for Asia successfully capture important episodes in each economy's financial history. This confirms the usefulness of the FCI in Asia. Only the indexes of financially advanced economies Japan and Singapore have sufficient forecasting power to predict output growth and inflation, however. Previous literature argues that the FCIs have sufficient forecasting power to predict GDP growth, but less forecasting power for inflation. This paper also suggests that close co-movement of Asian FCIs implies highly similar monetary policies in the region that are strongly linked with monetary policy in the United States. There has been a series of... You do not currently have access to this content.

Financial Conditions Indexes and Monetary Policy in Asia

This paper constructs quarterly financial conditions indexes (FCIs) for eight Asian economies—namely, Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, and Thailand—using a common factor methodology based on Hatzius et al. ( 2010 ). A wide array of financial data is included in the indexes based on identified monetary transmission channels in the literature. Bank-related indicators, various measures of financial stress and risk, and credit surveys, where available, are incorporated to fully reflect the state of the financing environment. The FCIs for Asia successfully capture important episodes in each economy's financial history, but only the indexes of financially advanced economies Japan and Singapore have sufficient forecasting power to predict output growth and inflation. High co-movement of Asian FCIs suggests highly similar monetary policies in the region that are strongly linked with monetary policy in the United States.

Summary of the General Discussion on “Financial Conditions Indexes and Monetary Policy in Asia”

Summary of the General Discussion on “Financial Conditions Indexes and Monetary Policy in Asia”

Panel Data Analysis of Exchange Rate for Fragile Five

After the Bretteon Woods System, as a result of the preffering especially the flexible exchange rate systems of developed countries the exchange rate risk has emerged. The transfer for the flexible exchange systems in the majority of developing countries started with the financial liberalization in the 1990’s. In this period, the progress of information technology and globalization have rendered exchange rate policies and exchange rates a priority for countries and exchange rates have become effective on macroeconomic indicators. In this study was examined exchange rate behaviour of Morgan Stanley’s “Fragile Five Countries” which are Brazil, India, Indonesia, South Africa and Turkey. For this purpose with the Panel Cointegration Tests was investigated if there is long termed relationship between the exchange rate and international reserves, money supply and The Bloomberg U.S. Financial Conditions Index(BFCIUS). As well as, Granger Causality test is applied using Panel Data Causality Techniques are used to uncover the direction of relation between variables. Thus Panel Vector Autoregression (PVAR) Model was estimated among the variables. The present study is distinguished from previous studies by investigation of long term relationship between also The BFCIUS, exchange rate. Data base is containing from the exchange rate index, international reserve index, Money supply index and BFCIUS variables presented by Bloomberg and monthly data includes 1015 observations done in the period of March 2000 – January 2017.

Constructing a dynamic financial conditions indexes by TVP-FAVAR model

ABSTRACTA factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility is used to constructing financial conditions index to explore the link between composite index of financial indicators and future inflation. Time variation in the models’ parameters allows for the weights attached to each financial variable in the index to evolve over time. A monthly data of Chinese CPI and a wide range of macroeconomic variables are adopted to construct FCI and the experiment result shows its good forecasting performance to inflation.

Estimating Financial Conditions Index for India

This article constructs financial condition indices (FCIs) for India and explores their predictive ability of business cycle. The estimated FCIs indicate substantial tightening in financial conditions in India since the global financial crisis barring a brief phase during 2010–11. Unlike in the literature, it shows the importance of standardising the financial variables by removing the influence of unit of measurement and not purging the influence of past economic activity as that improves the forecasting ability of FCI about business cycle. In predicting GDP growth, principal component analysis-based FCI outperforms vector autoregression-based FCI but both are better than OECD composite leading indicator, and indicate an upturn in business cycle in India in 2015–16. JEL Classification: E5, E17, E44

The role of financial conditions in transmitting external shocks to South Africa

This paper analyses the spillover effects of external financial conditions to South Africa using quarterly domestic and international data from 1996Q1 to 2014Q4. First, principal component analysis and vector autoregressive model are utilized to build financial conditions indices for South Africa and its main trading partners, namely, China, Germany, the United States, Japan, the United King, Netherlands, Italy, France and Belgium. Consistently across both methodologies, the financial conditions indices obtained track each other fairly well and capture the 2008/09 global financial crisis. Second, a Global Vector Autoregressive model comprised of financial indices and other macroeconomic variables is implemented to assess how international financial shocks spillover into South Africa. Our findings show that a sudden tightening of the US financial conditions has a significant but short lived effect on the South Africa's real GDP growth while the spillover effects from other trading partners appear to be of negligible impact throughout the sample period.

A UK Financial Conditions Index Using Targeted Data Reduction: Forecasting and Structural Identification

A financial conditions index (FCI) is designed to summarise the state of financial markets. We construct two with UK data. The first is the first principal component (PC) of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic variables weighted by the joint covariance with a subset of the financial indicators (a set of spreads), using multivariate partial least squares, again using the first factor. The resulting FCIs are broadly similar. They both have some forecasting power for monthly GDP in a quasi-real-time recursive evaluation from 2011-2014 and outperform an FCI produced by Goldman Sachs. A second factor that may be interpreted as a monetary conditions index adds further forecast power, while third factors have a mixed effect on performance. The FCIs are used to improve identification of credit supply shocks in an SVAR. The main effects relative to an SVAR excluding an FCI of the (adverse) credit shock IRFs are to make the positive impact on inflation more precise and to reveal an increased positive impact on spreads.

A UK Financial Conditions Index Using Targeted Data Reduction: Forecasting and Structural Identification

A financial conditions index (FCI) is designed to summarise the state of financial markets. We construct two with UK data. The first is the first principal component (PC) of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic variables weighted by the joint covariance with a subset of the financial indicators (a set of spreads), using multivariate partial least squares, again using the first factor. The resulting FCIs are broadly similar. They both have some forecasting power for monthly GDP in a quasi-real-time recursive evaluation from 2011-2014 and outperform an FCI produced by Goldman Sachs. A second factor that may be interpreted as a monetary conditions index adds further forecast power, while third factors have a mixed effect on performance. The FCIs are used to improve identification of credit supply shocks in an SVAR. The main effects relative to an SVAR excluding an FCI of the (adverse) credit shock IRFs are to make the positive impact on inflation more precise and to reveal an increased positive impact on spreads.

Incorporating Macro-Financial Linkages into Forecasts Using Financial Conditions Indices: The Case of France

How can information on financial conditions be used to better understand macroeconomic developments and improve macroeconomic projections? We investigate this question for France by constructing country-specific financial conditions indices (FCIs) that are tailored to movements in GDP, investment, private consumption and exports respectively. We rely on a VAR approach to estimate the weights of the financial components of each FCI, including equity market returns (which turn out having a relatively strong weight across all FCIs), private sector risk premiums, long-term interest rates, and banks’ credit standards. We find that the tailored FCIs are useful as leading indicators of GDP, investment, and exports, and as a contemporaneous indicator of private consumption. Credit volumes turn out to be lagging indicators of growth. The indices inform us on macro-financial linkages in France and are used to improve the accuracy of quarterly forecasting models and high-frequency “nowcast” models. We show that FCI-augmented models could have significantly improved forecasts during and after the global financial crisis.