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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.

The causal relationship between money and income (output) has been an important topic and has been extensively studied. However, those empirical studies are almost entirely on Granger-causality in the conditional mean. Compared to conditional mean, conditional quantiles give a broader picture of an economy in various scenarios. In this paper, we explore whether forecasting conditional quantiles of output growth can be improved using money growth information. We compare the check loss values of quantile forecasts of output growth with and without using past information on money growth, and assess the statistical significance of the loss-differentials. Using U.S. monthly series of real personal income or industrial production for income and output, and M1 or M2 for money, we find that out-of-sample quantile forecasting for output growth is significantly improved by accounting for past money growth information, particularly in tails of the output growth conditional distribution. On the other hand, money–income Granger-causality in the conditional mean is quite weak and unstable. These empirical findings in this paper have not been observed in the money–income literature. The new results of this paper have an important implication on monetary policy, because they imply that the effectiveness of monetary policy has been under-estimated by merely testing Granger-causality in conditional mean. Money does Granger-cause income more strongly than it has been known and therefore information on money growth can (and should) be more utilized in implementing monetary policy.

This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.

This paper aims to examine the effect of conditional conservatism on audit fees and whether the firm’s engagement in sustainable practices moderates the relationship between conditional conservatism and audit fees.

Using a sample of 3,767 firm-year observations from 14 European Union countries over the period of 2006–2019, the authors adopt the ordinary least square estimator to perform a panel data analysis of the effect of conditional conservatism on audit fees, and the moderating role of the environmental, social and governance (ESG) scores on the relationship between conditional conservatism and audit fees.

The authors find that conditional conservatism has a significant negative effect on audit fees, suggesting that auditors charge lower audit fees on more conservative clients. The authors also find that firms engaging in ESG actions, whether combined or individual, pay higher audit fees. More interestingly, the authors provide evidence that the negative effect of conditional conservatism on audit fees is mitigated only when ESG performance is considered in combination. This implies that firms exhibiting less commitment to ESG sustainability practices are prone to paying reduced audit fees when engaged in more conservative reporting. The findings remain robust after conducting a battery of tests.

The findings of this study have practical implications for several parties, including companies, auditors and regulators. This study emphasizes the potential benefit associated with using conservative accounting practices in terms of shaping downward the amount of audit fees. However, it also highlights the importance of considering the additional audit costs associated with higher ESG scores when making decisions about implementing sustainable practices.

Unlike prior studies that investigate the direct impact of sustainable practices on audit fees, the present work contributes to the literature on the benefits and costs of ESG by examining the moderating role of ESG performance in the association between audit fees and conditional conservatism. To the best of the authors’ knowledge, this study is the first to examine this relationship. Theoretically, the research integrates the theories of audit risk and agency to provide a more comprehensive understanding of the drivers of audit fees.

The purpose of this paper is to examine whether managers of bankrupt firms are more or less conditionally conservative in their financial reporting relative to non-bankrupt firms. The study further examines the cross-sectional differences in conditional conservatism among bankrupt and non-bankrupt firms.

The study employs a sample of US firms to investigate conditional conservatism in firms that experience financial distress and go bankrupt relative to non-stressed non-bankrupt firms. The study also uses switching regression models to identify the drivers of the cross-sectional difference in conditional conservatism among bankrupt and non-bankrupt firms.

Empirical results show that bankrupt firms are timelier in recognizing bad news than good news when compared to non-bankrupt firms. The higher level of conditional conservatism in bankrupt firms is mainly driven by their higher levels of leverage and tax-reduction incentives. The cross-sectional analyses show that these results largely hold for more leveraged firms and firms with higher tax costs. Taken together, these results suggest that the conservative tendency of managers of bankrupt firms can stem from the agency problem between lenders and managers and from tax-decreasing motivations.

The novelty of the authors’ research stands in studying the drivers of the cross-sectional differences in conditional conservatism between bankrupt and non-bankrupt firms and specifically, the demonstration that taxation also induces conditional conservatism in the setting of ex post bankrupt firms.