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This paper demonstrates an application of the Arbitrage Pricing Theory using canonical analysis as an alternative to the conventional factor analysis. Following the traditional view that asset prices are influenced by unanticipated economic events, the systematic effects of the major composite economic indices on a wide spectrum of industry returns are explored. The main conclusion is that profitability may be considered as the single most important factor that influences security returns. Also, the composite lagging economic indicators appear to be more useful to investors in forming market expectations than the composite leading economic indicators. Finally, it is argued that the composite index of coincident economic indicators do not exhibit any significant influence in the pricing of capital assets.

Several developing economies witnessed a large number of systemic financial and currency crises since the 1980s that resulted in severe economic, social, and political problems. The devastating impact of the 1982 and 1994–1995 Mexican crises, the 1997–1998 Asian financial crisis, the 1998 Russian crisis, and the ongoing financial crisis of 2008–2009 suggests that maintaining financial sector stability through reduction in vulnerability is highly crucial. The world is now witnessing an unprecedented systemic financial crisis originated from the USA in September 2008 together with a deep worldwide economic recession, particularly in developed countries of Europe and North America. This calls for devising and using on a regular basis an appropriate and effective monitoring and policy formulation system for detecting and addressing vulnerabilities leading to crisis. This chapter proposes a macroprudential/financial soundness monitoring, analysis, and remedial policy formulation system that can be used by most developing countries with or without crisis experience as well as with limited data. It also discusses a process for identifying and compiling a set of leading macroprudential/financial soundness indicators. An empirical illustration using Philippines data is presented. There is an urgent need for increased coordination, collaboration, and partnership among central banks, banking and financial market supervision agencies, and ministries of finance, economic, and planning for proper macroprudential monitoring. A high-level national financial stability committee under the auspices of the head of the state as well as a ‘‘regional financial stability board’’ needs to be established to complement and support the activities of an “international stability board.”

The purpose of this paper is to provide academic researchers and practitioners with a better understanding of the current Ivey Purchasing Managers Index (IPMI), with alternative IPMIs, and with their appropriateness as an indicator of the performance of the Canadian economy.

The paper makes use of principal component analysis to investigate the choice of principal variables for computing new IPMIs based on monthly data for five Ivey indexes for the period from December 2000 to May 2006. Statistical tests were made for the validity of the existing and new IPMIs using two major indicators of Canadian business and economic activities.

The results suggest that a new composite purchasing managers index for Canada similar to its US counterparts be computed based on four identified Ivey indexes. For constructing a simpler and parsimonious IPMI, the results support Ivey's current practice of using only one Ivey index, namely, the Purchases index.

There was a limited amount of data used for the analysis (i.e. monthly data for less than six years). Also there are issues on data comparability between the Ivey data and the US data (i.e. the Ivey does not collect separate data for the manufacturing sector or the non‐manufacturing sector).

Using a composite index akin to the PMI, business organizations and policymakers will have an accurate sense of what is happening in the Canadian economy. Furthermore, enhancing the power and accuracy of such an index will benefit supply professionals, economic forecasters, and policy experts.

The present study offers additional insights to both practitioners and academics. It helps supply chain managers and practitioners come up with a more reliable business strategy by providing them with a weighted composite index for the Canadian economy. It also makes contributions to the academic community in the area of statistical theory applied to supply management as it has introduced the principal components variable selection analysis in the construction of a new IPMI.

This study aims to investigate the impact of the macroeconomic environment on South African industrial sector returns.

Using standardized coefficients derived from time-series factor models, the authors quantify the impact of macroeconomic influences on industrial sector returns. The authors analyze the structure of the resultant residual correlation matrices to establish the level of factor omission and apply a factor analytic augmentation to arrive at a specification that is free of omitted common factors.

The authors find that global influences are the most important drivers of returns and that industrial sectors are highly integrated with the global economy. The authors show that specifications that comprise only macroeconomic factors and proxies for omitted factors in the form of residual market factors are likely to be underspecified. This study demonstrates that a factor analytic augmentation is an effective approach to ensuring an adequately specified model.

The findings have a number of implications that are of interest to investors, econometricians and researchers. While the study focusses on a single market, the South African stock market, as represented by the Johannesburg Stock Exchange (JSE), it is a highly developed and globally integrated market. In terms of market capitalization, it exceeds the Madrid Stock Exchange, the Taiwan Stock Exchange and the BM&F Bovespa. Yet, a limited number of studies investigate the macroeconomic drivers of the South African stock market.

Investors should be aware that while the South African domestic environment, especially political risk, has an impact on returns, global influences are the greatest determinants of returns. No industrial sectors are insulated from global influences and this limits the potential for diversification. This study suggests an alternative set of macroeconomic factors that may be used in further analysis and asset pricing studies. From an econometric perspective, this study demonstrates the usefulness of a factor analytic augmentation as a solution to factor omission in models that use macroeconomic factors to proxy for systematic influences that describe asset prices.

The contribution lies in providing insight into a large and well-developed yet understudied financial market, the South African stock market. This study considers a much broader set of macroeconomic factors than prior studies. A methodological contribution is made by estimating and interpreting standardized coefficients to discriminate between the impact of domestically and internationally driven factors. This study shows that should coefficients not be standardized, inferences relating to the relative importance of factors will differ. Finally, the authors unify an approach of using pre-specified factors with a factor analytic approach to address factor omission and to ensure a valid and readily interpretable specification.

The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic effect of stress indices.

The study constructs the composite indicator of systemic stress of Hollo, Kremer and Lo Duca (2012) for India using two different methods for computing time-varying cross-correlation matrix, namely, exponentially weighted moving average (EWMA) and dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH). The derived indices are evaluated with widely used, equal variance and principal component weighting indices in terms of tracing stress events, mapping with the business cycles and the macroeconomic effect. For this purpose, the study identifies various episodes of financial stress and uses the business cycle dates in the sample covering from January 2001 to October 2018.

The results suggest that stress indices based on EWMA and DCC-GARCH accurately identify the well-known stress periods and capture the recession dates and show an adverse effect on economic activity. Primarily, the DCC-GARCH-based stress index emerges as a better indicator of stress because it efficiently locates all the major-minor events, traces the build-up of stress and reverts to the normal level during stable times.

The DCC-GARCH-based stress index is a very useful indicator for policymakers in regularly monitoring India’s financial conditions and providing timely identification of systemic stress to avoid adverse repercussion effects of the financial crisis.

The 2007–2008 financial crisis and subsequent recurrent instability in the financial markets highlighted the requirement for an appropriate financial stress indicator for a timely assessment of the system-wide financial stress. To the authors’ knowledge, this is the first study that incorporates the systemic nature of financial stress in the construction of stress indices for India and provides a holistic evaluation of the financial stress from an emerging country’s perspective.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.

The purpose of this paper is to present a methodological framework and practical application of the sustainable development assessment in Lithuania.

The assessment was performed by deriving composite economic, social and environmental indexes and combining them into the integrated sustainable development index. Each composite index aggregates number of selected indicators, which were essential for the assessment of the country's development during the last decade. Having in mind that sustainable development is the development in consistency, the authors have used equal weights for calculation of economic, social and environmental indexes.

The composite indexes indicate rapid economical growth and environmental state; however, social development was comparatively slow. The economical decline in 2008 has influenced also environmental and social development and revealed new trends of sustainable development in Lithuania. The analysis results demonstrate that goals brought forward in the Strategy for Sustainable Development of Lithuania were not achieved.

The paper provides a methodology of integrated sustainable development index and its application for the Lithuanian case. The greatest advantage of a proposed calculation methodology is its flexibility, because it can be applied for any period of sustainable development evaluation, with a possibility to select a desired number of sustainability aspects that reflect an evolution of a country the best.

Purpose – The purpose of this chapter is twofold: to forecast gross domestic product (GDP) using nonparametric method, known as multivariate k-nearest neighbors method, and to provide asymptotic properties for this method.

Methodology/approach – We consider monthly and quarterly macroeconomic variables, and to match the quarterly GDP, we estimate the missing monthly economic variables using multivariate k-nearest neighbors method and parametric vector autoregressive (VAR) modeling. Then linking these monthly macroeconomic variables through the use of bridge equations, we can produce nowcasting and forecasting of GDP.

Findings – Using multivariate k-nearest neighbors method, we provide a forecast of the euro area monthly economic indicator and quarterly GDP, which is better than that obtained with a competitive linear VAR modeling. We also provide the asymptotic normality of this k-nearest neighbors regression estimator for dependent time series, as a confidence interval for point forecast in time series.

Originality/value of chapter – We provide a new theoretical result for nonparametric method and propose a novel methodology for forecasting using macroeconomic data.