Coincident Indicators Research Articles

Introducing a novel fragility index for assessing financial stability amid asset bubble episodes

This paper is devoted to the development of an innovative fragility index designed to capture comprehensively the dynamics of financial stability during periods characterized by asset bubbles. Utilizing a multifaceted methodological framework, the study begins by identifying bubble occurrences and calculating the delta CoVaR systemic risk metric. Building on established macroeconomic methodologies, we then propose an indicator to quantify the impact of financial bubbles on the stability of an emerging market. Specifically, we construct two coincident indicators based on daily observations of financial stability for the twenty most liquid Romanian companies. The first indicator is derived from the delta CoVaR values of these companies, while the second is computed using the residuals of a model that employs the same financial stability metric as the dependent variable, with a binary variable representing the presence of asset bubbles as the explanatory factor. This second coincident indicator tracks financial stability in the absence of bubble effects. The disparity between these two indicators forms the basis for the creation of an index, termed the “bubble fragility index,” which measures the overall susceptibility of financial stability to asset bubbles. In the context of the Romanian market, this study demonstrates that periods marked by asset bubbles are associated with elevated systemic risks. Our findings indicate that the presence of bubbles significantly intensifies financial risk factors, creating conditions conducive to severe market corrections and economic downturns. We identify specific intervals during which fragility reaches peak levels, including June to September 2018, December 2018 to March 2019, March 2020, and March 2022.

A novel business cycle indicator of the Korean shipping industry

The aim of this study is to construct monthly coincident and leading composite indicators for the shipping industry in Korea. The coincident and the leading indicators are computed using the generalized dynamic factor model. We use the production index of the water transport industry as the reference variable and compute the coincident indicator based on the common component of eight economic indicators. The analysis shows that the Korean shipping industry went through four business cycles during the sample period from 2007M1 to 2021M5. The leading indicator provides early signals of turning points in business cycles with a high correlation. The results suggest that the business cycle indicators present in this study may be useful diagnostic tools for understanding the timely and frequent economic state of the Korean shipping industry and its likely development in the near future.

Assessing the economy using faster indicators

AbstractThis paper constructs a coincident indicator for the United Kingdom employing—for the first time—a novel set of “faster” indicators recently published by the Office for National Statistics. Most of these variables were first released during the COVID‐19 pandemic with the aim of facilitating economics and social research by more timely indicators. The empirical evidence suggests that a coincident indicator based on a novel weekly dataset successfully captures the economic conditions in real time. We further use this indicator in an out‐of‐sample macroeconomic nowcasting exercise targeting monthly economic growth, prices and retail sales.

Detecting business cycles for Hungarian leading and coincident indicators with a Markov switching dynamic model to improve sustainability in economic growth

This paper applies the hidden Markov switching dynamic regression (MSDR) model to estimate transition probabilities of the Hungarian GDP between recessionary and expansionary periods. The transition probabilities are then compared to the OECD Hungarian binary business cycle indicator to assess the predictive power of the model. The paper proposes a linear model with a mean and a homoscedastic component. The level of symmetricity between the GDP and business cycles is explained by the panel data variables (Unemployment rate, IPI index, Inflation, BUX year-on-year change, and 10-3 Year sovereign bond yield spreads). It is assumed in this paper that by extending the model to encompass an exogenous variable listed in the panel data, essentially making the model bivariate, the maximum likelihood function would capture the business cycle more accurately. The results show that by plugging the unemployment rate as the exogenous variable in the regression, our model’s accuracy is 70%.

The System of Short-Term Regional Economic Monitoring in Moscow.

The article describes the system of short-term regional economic monitoring. The system has been developed by the authors and tested in Moscow since 2020. It consists of four hierarchical levels: aggregated regional economy, largest industries of regional economy, largest organizations by industry, small business. The system is a tool for short-term detailed analysis of regional economic performance. Both official national statistics and different auxiliary statistical sources have been embedded into the monitoring. Within the system a set of coincident indicators have been developed that allows for providing monthly estimates of gross regional product’s growth rate with one month lag.

Analyzing the performance of South Africa’s commodity market prices through business cycle indicators

"The soundness of the capital market is crucial in establishing resilient financial market deepening and general economic progress. Equally, the health of the financial market’s commodity market is undoubtedly a key determinant of inclusion, equitability, including sustained growth and development, especially in commodity-dependent countries. However, countries worldwide are faced with the continued challenge of falling commodity prices, presenting varied negative effects. Understanding the performance of the commodity market through lenses of fundamental or real-side indicators, other than just micro-specific financial or monetary variables, could prove helpful in constructing better inferences of the commodity market from an industrial, investor and policy standpoint. This study conducted a comprehensive evaluation of South Africa’s official component series of the business cycle indicators (BCI), to assess their potential and capacity to serve as explanatory signals for commodity market prices. The study utilized the cross-correlations tests, Granger causality tests, variance decomposition and charting techniques to assess the co-movement and concordance between business cycle component series (regressors) and the All-commodity index (regressand). Monthly observations from June 2003 to November 2017 were employed. Evidence of existing co-movement or concordance was established between the commodity market and most of the BCIs. Significant BCIs were identified as leading, lagging and coincident indicators for the commodity market based on the underlying properties established in the empirical estimates of the study. Keywords: Business cycle indicators, commodity market, capital market, South Africa. JEL Classification: Q02, F44 "

Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions

Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies. However, as the number of series and high-frequency observations per low-frequency period grow, MF-VARs suffer from the “curse of dimensionality.” We curb this curse through a regularizer that permits hierarchical sparsity patterns by prioritizing the inclusion of coefficients according to the recency of the information they contain. Additionally, we investigate the presence of nowcasting relations by sparsely estimating the MF-VAR error covariance matrix. We study predictive Granger causality relations in a MF-VAR for the U.S. economy and construct a coincident indicator of GDP growth. Supplementary materials for this article are available online.

The effects of climate risks on economic activity in a panel of US states: The role of uncertainty

We analyse the impact of climate risks (temperature growth and its volatility) on the coincident indicator of the 50 US states in a panel data set-up, over the monthly period of March, 1984 to December, 2019. Using impulse response functions (IRFs) from a linear local projections (LPs) model, we show that climate risks negatively impact economic activity to a similar degree, irrespective of whether such risks are due to changes in temperature growth or its volatility. More importantly, using a nonlinear LPs model, the IRFs reveal that the adverse effect of climate risks is contingent on the regimes of economic and policy-related uncertainty of the states, with the impact being significantly much stronger under relatively higher values of uncertainty, rather than lower values of the same. In addition to this, temperature growth volatility is found to contract economic activity nearly five times more compared to when temperature growth increases by a similar magnitude in the higher uncertainty-based regime of the nonlinear model. Understandably, our results have important policy implications.

Constructing a Coincident Economic Indicator for India: How Well Does It Track Gross Domestic Product?

In India, the first official estimate of quarterly gross domestic product (GDP) is released approximately 7–8 weeks after the end of the reference quarter. To provide an early estimate of current quarter GDP growth, we construct Coincident Economic Indicators for India (CEIIs) using a sequentially expanding list of 6, 9, and 12 high-frequency indicators. These indicators represent various sectors, display high contemporaneous correlation with GDP, and track GDP turning points well. CEII-6 includes domestic economic activity indicators, while CEII-9 incorporates indicators of trade and services and CEII-12 adds financial indicators in the model. We include a financial block in CEII-12 to reflect the growing influence of the financial sector on economic activity. CEIIs are estimated using a dynamic factor model which extracts a common trend underlying the high-frequency indicators. The extracted trend provides a real-time assessment of the state of the economy and helps identify sectors contributing to economic fluctuations. Furthermore, GDP nowcasts using CEIIs show considerable gains in both in-sample and out-of-sample accuracy. In particular, we observe that our GDP growth nowcast closely tracks the recent slowdown in the Indian economy.

Markov switching panel with endogenous synchronization effects

This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties of the model, develop a Bayesian inference framework and an efficient Markov Chain Monte Carlo algorithm for estimating parameters, latent states, and endogenous network layers. An application to the US-state coincident indicators shows that the synchronization in the US economy is generated by network effects among the states. The inclusion of a multi-layer network provides a new tool for measuring the effects of the public policies that impact the connectivity between the US states, such as mobility restrictions or job support schemes. The proposed new model and the related inference are general and may find application in a wide spectrum of datasets where the extraction of endogenous interaction effects is relevant and of interest.

An unconventional weekly economic activity index for Germany

We introduce an unconventional activity index for the German economy at weekly frequency in order to monitor economic developments in real time. The index contains nine high-frequency, rather unconventional weekly indicators. These are augmented by monthly industrial production and quarterly gross domestic product (GDP). The weekly activity index is then calculated as the common factor of the mixed-frequency dataset. We show that the index (i) exhibits a high correlation with quarterly GDP growth, (ii) is able to serve as a reliable weekly coincident indicator for economic activity, and (iii) can be used to generate timely nowcasts for GDP growth in Germany.

The Synchronization of ASEAN +3 Business Cycles: Prerequisites for Common Currency Union

This study aims to analyze the relationship between the business cycles of the ASEAN +3 countries. In addition, the effects of the spillover value on the coincident indicators are determined. This study employs secondary data and uses multivariate time series of five ASEAN countries, namely, Indonesia, Malaysia, Singapore, Thailand, and the Philippines. The proxy was the real gross domestic product (GDP) collected annually from the CEIC, the IMF, and the World Bank for the period from 1964 to 2016. The data was plotted against two time periods, 1964–1998 as the pre-crisis period, and 1999–2016 as the post-crisis period. The index data was changed to the base year 2010. The data was subsequently separated from the trends and the cyclic components. The cyclic components were obtained by using Hondrick-Prescott filter, and them were further analyzed. The analytical method used was Contemporaneous and Cross-Correlation tools. The results showed that, before and after the crisis, the value of the business cycle correlation between ASEAN +3 countries was stronger and moved together at the same level of lag value. The implication of this research was an initial finding of the ASEAN +3 countries’ prerequisites for the formation of a common currency.

Forecasting US overseas travelling with univariate and multivariate models

AbstractThis study makes use of specific econometric modelling methodologies to forecast US outbound travelling flows to certain destinations: Europe, Caribbean, Asia, Central America, South America, Middle East, Oceania and Africa, spanning the period 2000–2019 on a monthly basis. Both univariate (jointly with business conditions) and multivariate models are employed, whereas out‐of‐sample forecasts are generated, and the results are compared based on popular forecasting performance criteria. These criteria show that in the case of univariate models, the largest forecasting gains are obtained when the modelling process follows the kitchen sink autoregressive of order one (KS‐AR[1]) model with the business cycles being measured as the coincident indicator. In the case of multivariate models, the largest forecasting gains occur with the standard vector autoregressive (VAR) model for very short forecasting horizons and with the Bayesian VAR for longer horizons. The results are robust to both total and individual destinations. The findings allow interested stakeholders to gain insights into near‐future US outbound tourism to popular diversified international destinations, as well as to better understand its positive and negative impacts for strategic planning and destination adaptation purposes.

Monthly Composite Macroeconomic Performance Score based Country Rankings Assessing Early Effects of the COVID-19 Pandemic

In this paper we try to fill the void in availability of comprehensive and yet all inclusive indicators for judging relative country performances on the economic front. Forward looking monthly economic indicators like, even the Purchasing Managers Indices (PMIs), estimated either officially by the government or agencies, become subject to a much greater degree of uncertainty during economic and financial crises. Survey based leading indicators like PMIs are crucial but they do not and cannot cover the entire spectrum of the actual economic variables. Alternatively, considering a range of official monthly indicators leave open important questions of addressing incommensurability among them, and also implicit weighting and aggregation methods for arriving at a comprehensive picture of macroeconomic performance. The need for a single measure, to compare all aspects of macroeconomic performance, is accentuated by the fact that policies pursuing different macroeconomic goals of growth and stability usually involve certain tradeoffs. In the wake of global crises it becomes even more important to consider tools for frequently comparing country-wise performances to evaluate and learn from experiences.In the backdrop of the unprecedented crisis brought about by the COVID-19 outbreak, we introduce an indicator which allows a parsimonious representation of a variety of different facets of economic performance and its comparison across countries and is thus most useful in providing guidance to the relative stages of economic recovery around the globe. We take the composite indicators approach that consists in merging several available official monthly indicators of economic wellbeing and financial sector conditions, into a single metric. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology used in OR (operations research), specifically MCDM (multiple-criteria decision making) is used to arrive at the composite performance scores (CPS). A Rank Mobility Index (RMI) using the CPS based monthly rankings of 21 countries with comparable data on chosen variables reveal the severity of crisis across countries, the changing effects on economic wellbeing The RMIs clearly show the adverse effect of the pandemic on emerging market economies, in the early stages of the pandemic, particularly when world trade and production lines had been impaired and also confirms that high levels of government debt can be debilitating specially under unprecedented circumstances like this pandemic when the private sector has been forced to downsize all over the globe. As a composite indicator, the CPS is expected to reveal, more comprehensibly, than a plethora of indicators, the relative position of an economy at any point in time and provides us with a tool for continuous evaluation of relative economic performances of countries over time in crisis as also under normal circumstances. The TOPSIS ranking methodology is robust such that the choice of variables also can be modified according to the ready availability of coincident indicators or high frequency ones and/or to suit specific evaluation needs. CPS based rankings and the RMIs would help in assessing relative positions and provide readily a comprehensible picture of the global economy overall. We hope these monthly rankings would help policy makers, investors, and analysts to identify the short-term inter-temporal changes in country performances and offer some timely actionable intelligence over and above those provided by individual macro-financial indicators of countries.

Credit risk assessment in real estate investment trusts: A perspective on blockholding and lending networks

This study assesses the credit risk of Japan's real estate investment trusts (J-REITs) in two related markets during the fiscal years 2008–2017. The first J-REIT market involves blockholders, while the second is a lending market of institutions, i.e., banks and insurers. J-REITs are corporation type of closed-end funds listed on the stock exchange and thus, has corporate credit risk. Consequently, a J-REIT's financial variables and its sponsor have a substantial effect on the J-REIT's credit risk. A sponsor's probability of default is a leading indicator of the J-REIT's default and double default probability acts as a coincident indicator of default. Network analysis indicates that some network centralities are proxies for funding liquidity via blockholding and lending networks. Rather than increases in other centralities, an increase in the degree of lending to a J-REIT explains a decrease in the issued J-REIT's credit risk.

Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models

The analysis of mixed-frequency (MF) time series has been limited mainly to the vector autoregressive integrated moving average (ARIMA) framework, even though the exponential smoothing (ETS) method—a competing model to ARIMA—has made considerable progress in recent years. The ETS method provides a useful multivariate time series specification for estimating missing observations of low-frequency variable(s) and constructing forecasts of future values. Hence, this study proposes the vector ETS (VETS) method as a suitable alternative to ARIMA for smoothing and forecasting MF time series. To illustrate the superiority of the VETS method, we obtain high-frequency smoothed estimates of low-frequency variables and forecasts of MF vector time series using US data on four monthly coincident indicators and quarterly real gross domestic product. Furthermore, the method's forecast accuracy is investigated through a Monte Carlo simulation. The results show that the proposed method is suitable for short and medium-term forecasting.

An Augmented Measurement of the Housing Affordability Cycles in Malaysia

Malaysia’s property market has been going through a difficult phase as the supply of property stocks are excessive with the demand unable to catch up, and hence, many unsold units remaining on the market. The primary aim of this paper is to develop an index-based housing affordability indicator known as the housing affordability leading index (HALI), which is based on the indicator compilation approach founded by the National Bureau of Economic Research (NBER). The time-varying Markov switching (TVMS) model is then employed to assess the transition probabilities of the constructed housing affordability indicator. The transition probabilities estimate the prospects of the housing affordability condition and how long it will stay in that particular condition before having any major turnover. As the data employed was monthly data from year 2000 to year 2015, the constructed HALI successfully reflects the prior movements of the non-index housing affordability indicator price to income ratios (PIR). The empirical results show that the HALI has an average leading period of 9.5 months when taking the PIR as a benchmark of coincidence indicator for housing affordability movement.

Corona, crisis and conditional heteroscedasticity

ABSTRACT In this paper, we illustrate the macroeconomic risk associated with the early stage of the corona-virus outbreak. Using monthly data ranging from July 1991 to March 2020 on a recently developed coincidence indicator of global output growth, we estimate an autoregressive model with GARCH effects and non-Gaussian disturbances. Our results indicate that i) accounting for conditional heteroscedasticity is important and ii) risk, measured as the volatility of the shocks to the process, is at a very high level – largely on par with that experienced around the financial crisis of 2008–2009.

Are the European Commission’s Business and Consumer Survey Results Coincident Indicators for Maltese Economic Activity?

The European Commission’s business and consumer surveys are the most extensive regular surveys of Maltese firms and households. The Economic Sentiment Indicator (ESI) for Malta is closely correlated with real GDP growth, particularly when one focuses on the first vintage of national accounts data. This suggests that the opinions expressed by economic agents are partly driven by news prevailing at the time. The sectoral confidence indicators that underpin the ESI are quite highly correlated, with construction sentiment being the most synchronised with sentiment in other sectors. In general, sectoral expectations on future activity appear to be less strongly correlated to changes in national accounts sectoral value added than survey responses to planned employment changes are to observed changes in sectoral employment. Maltese household economic expectations appear to be mostly reflective of current conditions and could be useful to forecast variables that are issued with some time lag, like real GDP.

CAUSAL EFFECT OF ECONOMIC INDICATORS ON INDIAN AUTOMOBILE SECTOR

In this paper we examine the co-movement and casual relationship between economic indicators and automobile index. We postulate that leading and coincident economic indicators will help explain, cause, and predict movements in automobile index. To test our hypotheses we source data for four coincident indicators, namely Index of Industrial Production (IIP); Index of Manufacture of Motor Vehicles, Trailers and Semi-trailers (MVI); Index of Manufacture of Other Transport Equipment (TEI); and West Texas Intermediate (WTI) crude oil price, for leading indicator we use Bombay Stock Exchange (BSE) sensitive index (SENSEX). We consider BSE’s AUTO index as proxy for Indian Automobile Industry. We collect monthly data of our study variables from April 2012 to June 2019. We break our full sample into two sub-samples based on break point in AUTO index and run separate analysis for each sample period. We find significant contemporaneous co-movements between SENSEX and AUTO index. In contrast to our hypothesis AUTO index cause SENSEX in all the three sample periods. Granger test confirms this result with different lags. In addition, we also find evidence for causation from AUTO index to MVI and TEI with five and three lags respectively in our second sub-sample period. Our results indicate that automobile industry influence Indian stock markets and automobile industrial production. Our results suggest that policy makers and regulators should be vigilant in making policies pertaining to automobile industry and should consider the repercussions of their decisions, because wrong policy decisions about auto industry may hurt the Indian economy. Keywords : Indian economy, Indian stock markets, automobile industry performance, policy implications, regulatory norms JEL Classifications : G11, G18, E23, E32 DOI: https://doi.org/10.32479/ijefi.9175