Motivated by the prevalence of corporate sustainability and the rise of uncertainty at the national level, we investigate the impact of three sources of uncertainty; namely, economic policy, climate change, and political instability, on firms' sustainability performance. Using a sample of 6804 firms from 72 countries spanning 15 years, our study revealed that uncertainty due to climate change, economic policy, and political instability negatively affects firms' sustainability performance. This finding is in line with the real options theory that uncertainty in an external environment discourages firms' long-term investment (e.g. investment in corporate sustainability). In addition, the results show that the option for delay in sustainability investment moderated the relation between uncertainty at the national level and firms' sustainability performance. Firms with better sustainability performance had higher firm value when facing uncertainty. Interestingly, firms with higher profitability performed better in sustainability when facing uncertainty at the national level.

We propose a semiparametric two‐step inference procedure for a finite‐dimensional parameter based on moment conditions constructed from high‐frequency data. The population moment conditions take the form of temporally integrated functionals of state‐variable processes that include the latent stochastic volatility process of an asset. In the first step, we nonparametrically recover the volatility path from high‐frequency asset returns. The nonparametric volatility estimator is then used to form sample moment functions in the second‐step GMM estimation, which requires the correction of a high‐order nonlinearity bias from the first step. We show that the proposed estimator is consistent and asymptotically mixed Gaussian and propose a consistent estimator for the conditional asymptotic variance. We also construct a Bierens‐type consistent specification test. These infill asymptotic results are based on a novel empirical‐process‐type theory for general integrated functionals of noisy semimartingale processes.

This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting.

We develop econometric tools for studying jump dependence of two processes from high-frequency observations on a fixed time interval. In this context, only segments of data around a few outlying observations are informative for the inference. We derive an asymptotically valid test for stability of a linear jump relation over regions of the jump size domain. The test has power against general forms of nonlinearity in the jump dependence as well as temporal instabilities. We further propose an efficient estimator for the linear jump regression model that is formed by optimally weighting the detected jumps with weights based on the diffusive volatility around the jump times. We derive the asymptotic limit of the estimator, a semiparametric lower efficiency bound for the linear jump regression, and show that our estimator attains the latter. The analysis covers both deterministic and random jump arrivals. In an empirical application, we use the developed inference techniques to test the temporal stability of market jump betas.

Employing an international sample of bank loans from 37 countries, we find that both borrowers' intensive media coverage and positive media sentiment reduce the interest rate spreads on bank loans. In syndicated lending, positive media sentiment increases the likelihood of a non-relationship bank leading or participating in a loan syndicate and decreases the loan share of the lead arranger. Furthermore, we demonstrate that the negative impact of media news on loan spreads is more pronounced in countries with better financial information and governance environments, a higher representation of privately owned media, and lower government control of banks. These findings underscore the significance of media coverage and sentiment in shaping the costs of bank loans worldwide.

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high‐frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first‐order asymptotic results highlight how the same‐sign and mixed‐sign components load differently on economic information related to stochastic correlation and jumps. The second‐order asymptotic results reveal the structure underlying the same‐sign semicovariances, as manifested in the form of co‐drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross‐section of individual stocks to empirically document distinct dynamic dependencies in the different realized semicovariance components. We show that the accuracy of portfolio return variance forecasts may be significantly improved by exploiting the information in realized semicovariances.