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The main purpose of this paper is to empirically examine how firm-specific (idiosyncratic) and macroeconomic risks affect the external financing decisions of UK manufacturing firms. The paper also explores the effect of both types of risk on firms' debt versus equity choices.

The paper uses a firm-level panel data covering the period 1981-2009 drawn from the Datastream. Multinomial logit and probit models are estimated to quantify the impact of risks on the likelihood of firms' decisions to issue and retire external capital and debt versus equity choices, respectively.

The results suggest that firms considerably take into account both firm-specific and economic risk when making external financing decisions and debt-equity choices. Specifically, the results from multinomial logit regressions indicate that firms are more (less) likely to do external financing when firm-specific (macroeconomic) risk is high. The results of probit model reveal that the propensity to debt versus equity issues substantially declines in uncertain times. However, firms are more likely to pay back their outstanding debt rather than to repurchase existing equity when they face either type of risk. Of the two types of risk, firm-specific risk appears to be more important economically for firms' external financing decisions.

The findings of the paper are equally useful for corporate firms in making value-maximizing financing decisions and authorities in designing effective fiscal and monetary policies to stabilize macroeconomic conditions. Specifically, the findings emphasize on the stability of the overall macroeconomic environment and firms' sales/earnings, which would result stability in firms' capital structure that help smooth firms' investments and production.

Unlike prior empirical studies that mainly focus on examining the impact of risk on target leverage, this paper attempts to examine the influence of firm-specific and macroeconomic risk on firms' external financing decisions and debt-equity choices.

The Iranian banking industry has been greatly affected by dramatic changes in macroeconomic conditions over the past several decades owing to volatile oil revenues, changing fiscal and monetary policies, and the imposition of US sanctions. The main objective of this paper is to estimate potential credit losses in the Iranian banking sector due to macroeconomic shocks and assess the minimum economic capital requirements under the baseline and distressed scenarios. The paper also contrasts the applications of linear and nonlinear models in estimating the impacts of macroeconomic shocks on financial institutions.

The paper uses a multistage approach to derive the portfolio loss distribution for banks. In the first step, the dynamic relationship between the selected macroeconomic variables are estimated using a VAR model to generate the stress scenarios. In the second step, the default probabilities are estimated using a quantile regression model and the results are compared with those of the conventional linear models. Finally, the default probabilities are simulated for a one-year time horizon using Monte-Carlo method and the portfolio loss distribution is calculated for hypothetical portfolios. The expected loss includes the loss given default for loans drawn randomly and uniformly distributed and exposed at default values when loans are assigned a fixed value.

The results indicate that the loss distributions under all scenarios are skewed to the right, with the linear model results being very similar to those of quantile at the 50% quantile, but very unlike those at the 10% and 90% quantiles. Specifically, the quantile model for the 90% (10%) quantile generates estimates of minimum economic capital requirement that are considerably higher (lower) than those using the linear model.

The study has focused on credit risk because of lack of data on other types of risk at individual bank level. The future studies can estimate the aggregate economic capital using a risk aggregation approach and a panel data (not presently available), which could further improve the accuracy of the estimates.

The fiscal and monetary authorities in developing countries, specially oil-exporting countries, can follow the risk assessment approach to assess the health of their banking system and adapt policies to mitigate the impacts of large macroeconomic shocks on their financial markets.

This is the first paper estimating the portfolio loss distribution for the Iranian banks under turbulent macroeconomic conditions using linear and nonlinear models. The case study can be applied to other developing and emerging countries, particularly those highly dependent on natural resources, prone to extreme macroeconomic shocks.

This paper aims to examine the relationship between the conditional variance of the factors from the Fama–French three‐factor model and macroeconomic risk, where macroeconomic risk is proxied by the conditional variance for a default risk premium and real gross domestic product (GDP) growth.

A generalised autoregressive conditional heteroscedastic model is used to generate the conditional volatilities and bivariate Granger causality tests are used to examine the empirical relationship between the risk measures.

Past values of the conditional variance for a default risk premium have information that is precedent to the conditional volatility for value premium and the small stock risk premium, and the conditional variance for the market risk premium has information about the future volatility of macroeconomic risk, as proxied by the conditional variance for GDP growth.

The implications are that conditional volatility associated with default is related to current and future volatility in value premium; however, volatility associated with the market risk premium appears to be a predictor of future macroeconomic risk. A caveat is that the results are dependent on the proxies used for macroeconomic risk and more refined measures of macroeconomic risk may yield different results.

This paper suggests that examination of the relationship between the volatility of macroeconomic factors and the explanatory factors in asset‐pricing models will help to further understanding of the relationship between risk and expected return.

This paper focuses directly on the links between risk associated with the Fama–French factors and macroeconomic risk. This added knowledge is beneficial to practitioners and academics whose interest lies in asset price modelling.

This study aimed to know the effect of cross-sectional risk, which comprises business-specific risk and stock market volatility, as a variable for estimating macroeconomic risk in Indonesia. This study observes public companies in Indonesia and Indonesian macroeconomic data from 2004 to 2020. In this study, the author uses term spread as the dependent variable that reflects macroeconomic risk. The cross-sectional risk comprises financial friction (FF), cash flow (CF), debt–service ratio, and stock market volatility as independent variables. By using the Autoregressive Distributed Lag (ARDL) Model method, this study shows that business-specific and stock market risk can estimate macroeconomic risk, so that it becomes an early signal of economic shock, such as recession or high inflation, in the future. The model in this study also examines the cross-sectional risk relationship with other macroeconomic indicators, such as the Consumer Confidence Index (CCI), money supply (M0), and Indonesia’s trade balance (TB).

The purpose of this study is to investigate the impact of idiosyncratic and macroeconomic risks on capital structure on SADC countries.

Employing data from the African Financials database, the analysis is conducted over a ten year period spanning from 2009 to 2018 for 309 companies. Unit Root Fisher Chi-Square test and Granger Causality test were employed to test for unidirectional and bidirectional relationships cross-sectionally. To resolve endogeneity issues, System GMM was used as main topology for panel regression analysis.

The study confirmed that companies become risk averse when there is an increase in idiosyncratic and macroeconomic risk and therefore take less leverage. According to the perking order theory, a higher variability in earnings shows that the bankruptcy probability amplifies. Hence, institutions with high income employ more internal finance during periods of high idiosyncratic and macroeconomic uncertainty thereby lowering leverage. A positive significant and statistically relationship is also confirmed between idiosyncratic risk and leverage in Botswana, South Africa and Tanzania. Companies with higher leverage make riskier investment in line with the trade-off theory. In short, executives from the SADC region consider more importance to fluctuations in risk while accelerating or diminishing leverage in their capital structure.

The study is among one of the pioneering work underpinning the idiosyncratic risk and macroeconomic risk on capital structure and relying on a large number of companies across the SADC region. In this respect, it adds contribution to the existing literature on risks and capital structure to the socio-economic goals of the SADC region.

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 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 examine short- and long-run effects of specific macroeconomic conditions on risk premium estimates on lending.

Empirical estimates are based on error correction and autoregressive distributed lag models.

The results suggest that, in the short run, inflation expectations, recession expectations and actual inflationary conditions tend to have a significant impact on risk premium estimates; in the long run, however, only inflation expectations and recession expectations are significant in risk premium estimates on lending.

This study examines how specific conditions of uncertainty and expectations influence variability in risk premium estimates on lending in the US economy.

The study evaluates the role of institutional framework and macroeconomic instability on financial market development among emerging economies.

The study uses panel data compiled from 32 countries from the sub-region of Sub-Sahara Africa (SSA), covering the period starting from 1996 to 2019. Empirical analyses were carried out using the two-step system generalized method of moments (TS-GMM) statistical framework.

Reviewed results suggest that institutional quality, effective governance and corruption control have a significant positive impact on financial market development among economies in the sub-region. Further empirical estimates show that macroeconomic risk and macroeconomic uncertainty have significant adverse effects on financial market development. Additionally, reported empirical estimates suggest that an improved institutional framework has the potential to lessen the adverse effect of macroeconomic instability on financial market development among economies in the sub-region.

The uniqueness of this empirical inquiry compared to related studies in the present literature stems from the fact that studies employing similar empirical approaches on the subject matter for economies in the sub-region are rare. Additionally, the analysis pursued in this study employs critical variables whose impact on financial market performance in the sub-region has not been examined per our review. These variables include indexes such as macroeconomic risk and institutional quality, which are unique to this study based on their construction; these indexes are generated using a principal component analysis procedure with different underlying variables compared to what may be found in the literature.

In addition to leading a new tide of global financial technology, blockchain delivers advantages in terms of risk control compared to traditional financial systems. By exploring the relationship between blockchain technology and macroeconomic uncertainty, this study aims to identify the hedge risk attribute of blockchain technology.

From a data set comprising 6,323 Chinese firms with A-shares listed on the Shenzhen and Shanghai Stock Exchanges in 2015–2018, the authors obtain the use of blockchain technology by listed companies on the basis of annual reports, news reports, search engines and prospectuses. These documents are then subjected to text analyses based on computer technology. Cross-sectional and propensity score matching analyses are used to ensure robustness.

The empirical results show that with an increase in macroeconomic uncertainty, blockchain technology can potentially enable companies to reduce their systemic risks and enhance their investment efficiency.

This study expands the literature on the application of blockchain technology, offers references for enterprises to address future risks based on specific macroeconomically uncertain environments and provides guidelines for governments to maintain financial market stability.