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The purpose of this paper is, to study macroeconomic risk factors driving the expected stock returns of listed private equity (LPE). The authors use LPE indices divided into different styles and regions from January 2004 to December 2016 and a set of country stock indices to estimate the macroeconomic risk profiles and corresponding risk premiums. Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market βs that are greater than 1. A one-factor asset pricing model using world stock market returns as the only possible risk factor is rejected on the basis of generalized method of moments (GMM) orthogonality conditions. In contrast, using the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of economic policy uncertainty as additional risk factors, this multifactor model is able to price a cross-section of expected LPE returns. The risk-return profile of LPE differs from country equity indices. Consequently, LPE should be treated as a separate asset class.

Following Ferson and Harvey (1994), the authors use an unconditional asset pricing model to capture the structure of returns across LPE. The authors use 11 LPE indices divided into different styles and regions from January 2004 to December 2016, and a set of country stock indices as spanning assets to estimate the macroeconomic risk profiles and corresponding risk premiums.

Using a seemingly unrelated regressions (SUR) model to estimate factor sensitivities, the authors document that LPE indices exhibit stock market ßs that are greater than 1. The authors estimate a one-factor asset pricing model using world stock market returns as the only possible risk factor by GMM. This model is rejected on the basis of the GMM orthogonality conditions. By contrast, a multifactor model built on the change in a currency basket, the G-7 industrial production, the G-7 term spread, the G-7 inflation rate and a recently proposed indicator of global economic policy uncertainty as additional risk factors is able to price a cross-section of expected LPE returns.

Given data availability, the authors’ sample is strongly influenced by the financial crisis and its aftermath.

Information about the risk profile of LPE is important for asset allocation decisions. In particular, it may help to optimally react to contemporaneous changes in economy-wide risk factors.

To the best of authors’ knowledge, this is the first LPE study which investigates whether a set of macroeconomic factors is actually priced and, therefore, associated with a non-zero risk premium in the cross-section of returns.

The study examines the effect of macroeconomic risk, inflation uncertainty and instability associated with key macroeconomic indicators on the efficiency of financial institutions among economies in sub-Saharan Africa (SSA).

Data for the empirical inquiry were compiled from 35 SSA economies from 1996 to 2019. The empirical estimates were carried out using pooled ordinary least squares (POLS) with Driscoll and Kraay’s (1998) standard errors.

Reported empirical estimates show that macroeconomic risk and exchange rate volatility constrain the efficiency of financial institutions. Further results suggest that inflation uncertainty has a significant influence on the efficiency of financial institutions among economies in the subregion. Additionally, reviewed empirical estimates show that institutional quality positively moderates the nexus between inflation uncertainty and financial institution efficiency. At the same time, political instability is found to worsen the adverse effect of macroeconomic risk on the efficiency of financial institutions.

For policymakers and governments, improved institutional structures are recommended to ensure the operational efficiency of financial institutions, especially during an inflationary period. For decision-makers among financial institutions, the study recommends policies that have the potential to make their institutions less vulnerable to macroeconomic risk and exchange rate fluctuations.

The approach adopted in this study differs significantly from related studies in that the study examines and reviews interactions and relationships not readily found in the reviewed literature.

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 paper is to provide an analysis of the relationship between expected risk premia on property stocks and some major macroeconomic risk factors as reflected in the general business and financial conditions

Employs a three‐step estimation strategy (principal component analysis, GARCH (1,1) and GMM) to model the macroeconomic risk variables (GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate) and relate them to the first and second moments on property stock excess returns of four major markets, namely, Singapore, Hong Kong, Japan and the UK. Macroeconomic risk is measured by the conditional volatility of macroeconomic variables.

The expected risk premia and the conditional volatilities of the risk premia on property stocks are time‐varying and dynamically linked to the conditional volatilities of the macroeconomic risk factors. However there are some disparities in the significance, as well as direction of impact in the macroeconomic risk factors across the property stock markets. Consequently there are opportunities for risk diversification in international property stock markets.

Results help international investors and portfolio managers deepen their understanding of the risk‐return relationship, pricing of macroeconomic risk as well as diversification implications in major Asia‐Pacific and UK property stock markets. Additionally, policy makers may play a role in influencing the expected risk premia and volatility on property stock markets through the use of macroeconomic policy.

The purpose of this paper is to investigate the determinants of interest rate in emerging markets, focusing on banking financial institutions in Uganda.

Using the net interest margin model, interest rate was estimated by applying a panel random effects regression method on 24 banks, while controlling for bank-specific factors, industry and macroeconomic indicators. Data were drawn from annual reports provided by Bank of Uganda Depository Corporation survey from 2008 to 2016.

The results indicate that liquidity, equity capital, market power and reserve requirement have a positive effect on interest rate. The study further finds that operational efficiency, lending out ratio, concentration, public sector borrowing and private sector credit have a negative effect on interest rate. However, credit risk does not influence interest rate.

Studied banks are grouped in one panel data set; future studies would focus on the differences in banks and establish how these differences affect interest rate. Future study would also focus on how the determinants of interest rate in Uganda are compared with those of other banks in other emerging market countries.

Bank managers need to take interest in equity mobilization because it is a reliable and cheaper source of funding bank operations. Banks should emphasize efficient operations to reduce on the cost of doing business. Government should utilize funds borrowed from banks in efficient ways to improve economic growth. The central bank should minimize the use of reserve requirement as a means of controlling money in circulation.

This is the first paper that uses annual report data from several banks and periods to investigate the determinants of interest rate in an emerging country.

The purpose of this study is to analyze the benchmark model and offer a practical implementation of the macro stress test. The emergence of complicated instruments such as securitized products has rendered the risk management methodologies used in non-crisis periods insufficient. The macro stress test has become prominent as both an internal risk management tool for financial institutions and a way for supervisory authorities to maintain financial stability. However, no practical model is available for transforming macro stress scenarios into the risk parameters of an institution’s internal model.

This study presents a model for assessing a company’s default risk through a multi-level regression based on simultaneous estimates of the impacts of company-specific, macroeconomic and sector-specific risk factors using panel and time series data.

Equity capital, EBITDA, the current ratio and the fixed assets to fixed liability ratio are selected as the company-specific factors, while the CPI core rate, overall unemployment rate, overnight call rate and JGB yield to subscribers are selected as the macroeconomic factors. The correlation coefficients among the latent sector factors are significant at 5 per cent. In addition, the accuracy ratio values prove that the presented model has more default prediction power than do models without them.

This study is the first to provide a benchmark model for incorporating macroeconomic variables into a credit risk model for use in a bottom-up macro stress test.

The paper uses 101 years of Chilean and international financial assets returns to investigate mean-variance optimal portfolio allocations. The key conclusion is that the share of international unhedged investments is substantial even in minimum risk portfolios (20%), unless the period 1980–2002 is assumed to be drawn from a different distribution and previous history is disregarded. In addition to that, the paper finds that mean-variance optimal investors would have generated substantial demand for an asset replicating the return profile of an efficient pay-as-you-go pension scheme. Labour income and departures from log-normality of returns might, however, affect the latter conclusion.

In the past decade, firms have become more aware of supply chain disruptions and their impact on the firm. Developing a supply chain resilience organizational culture has been proposed as an effective way to manage supply chain risks. This study intends to explore how the geographical location risks impact the decision to develop a supply chain resilience strategy, in particular, to anticipate the disruption proactively and have a business continuity plan in place. Using a unique database including thousands of manufacturing locations that belong to over 7,000 firms across 102 countries, we test three hypotheses to understand if geographical location risks, frequency of disruptive events, and the region in which a site is located are factors for the likelihood of a firm having a business continuity plan at their locations. The study also seeks to understand if there are regional effects and firm effects affecting the decision to develop resilience. With a particular focus in Latin America and the firms with a manufacturing presence in that region. The main findings of the study are that natural disaster risks do tend to develop a culture of resilience, while macroeconomic risks tend to do the opposite. These results remain stable for firms' effects. The Latin America region shows no observable statistical difference in developing resilience compared to the Asia region. While the Northern America region shows more resilience compared to Asia. We conclude that economic risk is less predictable and harder to develop a plan for than disruptions, such as natural disasters. The findings of this study present an opportunity for governments to develop resilience plans that can make their countries more attractive for investment to multinational firms looking to establish new manufacturing locations around the world.

The objective of this paper is threefold. First, it aims to empirically study whether firm-specific/idiosyncratic uncertainty, macroeconomic/aggregate uncertainty and political uncertainty have an adverse influence on firms' investment decisions in Pakistan. After establishing this, it scrutinizes whether the uncertainty effects on investment are different for firms of different sizes. Finally, it investigates whether any heterogeneity exists in the uncertainty impacts across different industries.

The empirical analysis is based on an unbalanced panel data of 468 nonfinancial firms listed at the Pakistan Stock Exchange (PSX) during the period 2000–2018. Departing from the literature, the paper builds a time-varying composite volatility/uncertainty index based on the principal component analysis (PCA) by utilizing the constructed volatility series for sales, cash flows and return on assets to gauge firm-specific uncertainty for each firm included in the analysis. Likewise, the paper develops a PCA-based composite index for macroeconomic uncertainty by using the conditional variance series of consumer price index (CPI), industrial production index (IPI), the interest rate and the exchange rate obtained by estimating the (generalized) autoregressive conditional heteroscedastic, (G)ARCH, models. Finally, political uncertainty is measured by political risk components maintained by the Political Risk Services Group. The empirical framework of the paper augments the standard investment equation by incorporating all three types of uncertainty. Firms are grouped into small, medium and large categories based on firms' total assets and the size indicators are generated. Next, the indicators are multiplied by each uncertainty measure to quantify the differential effects of uncertainty across firm size. Firms are also differentiated by sectors to explore the sector-based asymmetries in the uncertainty effects. The “robust two-step system generalized method of moments (2SYS GMM) (dynamic panel data) estimator” is applied to estimate the empirical models.

The results provide robust and strong evidence of the detrimental influence of all three types of uncertainty on investment. Yet, it is observed that the strength of the influence considerably varies across uncertainty types. In particular, compared to firm-specific uncertainty, both macroeconomic and political uncertainties have more unfavorable effects. The analysis also reveals that the effects of all three types of uncertainty are quite different at small, medium and large firms. Specifically, it is observed that although the investment of all firms is influenced adversely by magnified uncertainty, the adverse effects of all three kinds of uncertainty are quite stronger at small firms than medium and large firms. These findings support the phenomenon of size-based asymmetries in the effects of uncertainty on investment. The results also provide evidence that either type of uncertainty quite differently affects the investment policy of firms in different sectors.

The findings help different stakeholders to know how different types of uncertainty differently affect corporate firms' investments. Further, they suggest that firm size has a vital role in ascertaining the adverse effects of uncertainty on investment. The paper identifies to which type of uncertainty investors and policymakers should care more about and to which types of firms and industries they should concern more during volatile times. Firms should have more fixed assets and expand their size to mitigate the detrimental effects on investment of internal and external uncertainties. The government should enhance the political stability to induce firms for a higher level of investment, which, in turn, will result in higher growth of the economy.

The originality of the paper is credited to four aspects. First, unlike most previous studies that have utilized a single volatility measure, this paper constructs composite uncertainty indices based on the weights determined by the PCA. Second, it examines the effect of political uncertainty over and above the effects of idiosyncratic and aggregate (macroeconomic uncertainty) for an emerging economy. Third, and most important, it provides first-hand empirical evidence on the role of firm size in establishing the asymmetric effects of uncertainty on investment. Finally, it provides evidence on the industry-based heterogeneity in the uncertainty effects.