Stress-testing US bank holding companies: A dynamic panel quantile regression approach

We propose an econometric framework for estimating capital shortfalls of bank holding companies (BHCs) under pre-specified macroeconomic scenarios. To capture the nonlinear dynamics of bank losses and revenues during periods of financial stress, we use a fixed effects quantile autoregressive (FE-QAR) model with exogenous macroeconomic covariates, an approach that delivers a superior out-of-sample forecasting performance relative to the standard linear framework. According to the out-of-sample forecasts, the realized net charge-offs during the 2007–09 crisis fall within the multi-step-ahead density forecasts implied by the FE-QAR model, but are frequently outside the density forecasts generated using the corresponding linear model. This difference reflects the fact that the linear specification substantially underestimates loan losses, especially for real estate loan portfolios. Employing the macroeconomic stress scenario used in CCAR 2012, we use the density forecasts generated by the FE-QAR model to simulate capital shortfalls for a panel of large BHCs. For almost all institutions in the sample, the FE-QAR model generates capital shortfalls that are considerably higher than those implied by its linear counterpart, which suggests that our approach has the potential to detect emerging vulnerabilities in the financial system.     

Asymmetric effects of corporate sustainability strategy on value creation among global automotive firms: A dynamic panel quantile regression approach

This study explores the asymmetric effects of corporate sustainability strategy on firm value at different conditioning quantiles by performing a dynamic panel quantile regression analysis on global automotive firms from 2011 to 2017. Further, this study measures the distinct effects of positive and negative corporate sustainability strategies on firm value, which has remained unconsidered as yet. The findings suggest that low-value and midvalue firms respond more strongly to positive and negative corporate sustainability strategies than high-value firms. This implies that for low-value and midvalue corporations that are in a growth phase, an investment in positive corporate sustainability strategies is essential to increase firm value by enhancing public perception of their efforts. Therefore, positive corporate sustainability strategy contributes substantially to future growth. Conversely, positive corporate sustainability strategy may not be a priority in increasing firm value for high-value corporations, because these strategies do not enhance the public's discernment of their efforts in ethics management and hence do not contribute to a future increase in value. Meanwhile, engagement in negative corporate sustainability strategy worsens firm value in all quantiles, although the effect is somewhat weaker for high-value firms. Nevertheless, however high valued and well established a firm is, it is not immune to crisis.     

Common correlated effects estimation of heterogeneous dynamic panel quantile regression models

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.     

Insider Trading and regulation: A look at bank holding companies

With the passage of the Insider Trading Sanctions Act (ITSA) of 1984, regulators have attempted to reduce insider trading activities through their increased power to impose stiffer penalties on violators. In their study of trading activity associated with tender offers, Arshadi and Eyssell (1991) find that insiders went from being heavy net purchasers of their own firms' stock prior to tender offer announcements to being weak net sellers. The special status of bank holding companies suggests that the trading patterns of insiders would differ between bank holding companies and non-bank holding companies. The results in this paper indicate this to be the case as there is no change in the trading patterns for insiders of bank holding companies between the two regulatory periods.     

Instrumental variable quantile regression: A robust inference approach

In this paper, we develop robust inference procedures for an instrumental variables model defined by Y=D^′α(U)$Y=D^{\prime }\alpha (U)$ where D^′α(U)$D^{\prime }\alpha (U)$ is strictly increasing in U and U is a uniform variable that may depend on D but is independent of a set of instrumental variables Z. The proposed inferential procedures are computationally convenient in typical applications and can be carried out using software available for ordinary quantile regression. Our inferential procedure arises naturally from an estimation algorithm and has the important feature of being robust to weak and partial identification and remains valid even in cases where identification fails completely. The use of the proposed procedures is illustrated through two empirical examples.     

Robust penalized quantile regression estimation for panel data

This paper investigates a class of penalized quantile regression estimators for panel data. The penalty serves to shrink a vector of individual specific effects toward a common value. The degree of this shrinkage is controlled by a tuning parameter λ$\lambda$. It is shown that the class of estimators is asymptotically unbiased and Gaussian, when the individual effects are drawn from a class of zero-median distribution functions. The tuning parameter, λ$\lambda$, can thus be selected to minimize estimated asymptotic variance. Monte Carlo evidence reveals that the estimator can significantly reduce the variability of the fixed-effect version of the estimator without introducing bias.     

Endogeneity in quantile regression models: A control function approach

This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions. The paper adjusts for endogeneity by adopting a control function approach and presents a simple two-step estimator that exploits the partially linear structure of the model. The first step consists of estimation of the residuals of the reduced-form equation for the endogenous explanatory variable. The second step is series estimation of the primary equation with the reduced-form residual included nonparametrically as an additional explanatory variable. This paper imposes no functional form restrictions on the stochastic relationship between the reduced-form residual and the disturbance term in the primary equation conditional on observable explanatory variables. The paper presents regularity conditions for consistency and asymptotic normality of the two-step estimator. In addition, the paper provides some discussions on related estimation methods in the literature.     

Asymptotics for panel quantile regression models with individual effects

This paper studies panel quantile regression models with individual fixed effects. We formally establish sufficient conditions for consistency and asymptotic normality of the quantile regression estimator when the number of individuals, n$n$, and the number of time periods, T$T$, jointly go to infinity. The estimator is shown to be consistent under similar conditions to those found in the nonlinear panel data literature. Nevertheless, due to the non-smoothness of the objective function, we had to impose a more restrictive condition on T$T$ to prove asymptotic normality than that usually found in the literature. The finite sample performance of the estimator is evaluated by Monte Carlo simulations.     

Agency costs and dividend payments: The case of bank holding companies

This paper examines the hypothesis that agency costs are a primary factor motivating dividend payments. Norohna et al. (1996) present evidence that the agency cost rationale is context specific and that dividends will not be driven by agency costs when other mechanisms exist for controlling agency problems. We argue that regulation of bank holding companies involves a context specific case where agency costs may be less relevant. Using an empirical methodology similar to Born and Rimbey’s (1993), we find that the abnormal returns associated with dividend announcements by bank holding companies are not related to their external financing activities. The monitoring activities of the capital markets are not a rationale for dividend payments in the presence of bank regulation. Our results are robust to an alternative explanation involving the signaling role of new equity financings.     

Productivity and efficiency at bank holding companies in the U.S.: a time-varying heterogeneity approach

This paper investigates the productivity and efficiency of large bank holding companies (BHCs) in the United States over the period 2004–2013, by estimating a translog stochastic distance frontier (SDF) model with time-varying heterogeneity. The main feature of this model is that a multi-factor structure is used to disentangle time-varying unobserved heterogeneity from inefficiency. Our empirical results strongly suggest that unobserved heterogeneity is not only present in the U.S. banking industry, but also varies over time. Our results from the translog SDF model with time-varying heterogeneity show that the majority of large BHCs in the U.S. exhibit increasing returns to scale, a small percentage exhibit constant returns to scale, and an even smaller percentage exhibit decreasing returns to scale. Our results also show that on average the BHCs have experienced small positive or even negative technical change and productivity growth.     

Measuring heterogeneity in hospital productivity: a quantile regression approach

Journal of Productivity Analysis - This paper focuses on acute-care local public hospitals in Japan and evaluates differences in hospital technology, as reflected in the productivity of labor...     

Determinants of house prices in Istanbul: a quantile regression approach

This paper uses quantile regression methods where a hedonic equation is estimated for each quantile of the conditional distribution of housing prices. The survey data are used to investigate the relationship between house prices and housing characteristics in Istanbul. This data set includes some housing characteristics of the dwellings like numbers of room, bathroom, heating system, location of house etc. In the results of this paper show some similarities and differences from earlier studies on housing prices. We find that age, cable tv, security, heating system, garage, kitchen area, increasing numbers of room and bathroom increase the house prices. Our findings also show that side variable which is a special factor for Istanbul real estate market has negative effect on the prices. It is clear that the factors of housing prices can change because of the properties of country, region or city. The results of this study may give some important interpretations for developing real estate market.     

Corporate social responsibility and corporate performance: a quantile regression approach

This paper investigates the impact of corporate social responsibility activities on corporate performance. In view of the inconsistent empirical findings in the literature, and the limitations of least squares regressions, we adopt a quantile regression method to fill this gap in the literature. An important finding is that the sensitivity of a company’s performance to its engagement in corporate social responsibility activities does not vary with the quantile location of the firm’s performance level, and the engagement in corporate social responsibility activities has a significant positive relation with corporate performance across all quantiles. This study argues that undertaking corporate social responsibility leads to greater financial returns than the related costs. Therefore, this study concludes that engaging in corporate social responsibility is beneficial for firms, and thus worth implementing.     

Measuring systemic risk of the Chinese banking industry: A wavelet-based quantile regression approach

In systemic risk measure, a large amount of literature has emerged, but few of them take into account the multi-scale natures of financial data. Considering these natures, we develop a novel W-QR-CoVaR method to measure systemic risk. To be specific, the W-QR-CoVaR method combines the wavelet multiresolution analysis (MRA) with the conditional value-at-risk (CoVaR) method based on the quantile regression (QR) framework. We then apply it to measure the systemic risk in the Chinese banking industry covering the period from September 2007 to September 2018. Our experiment results show that the hybrid W-QR-CoVaR method performs better than the traditional CoVaR method in terms of predictive accuracy. Furthermore, we also explore the relation between the systemic risk contribution of each individual bank and the bank-specific characteristics. Size and leverage appear to be the most robustness determinants. The findings suggest that regulators should pay more attention to the banks with smaller size and higher leverage.     

Local employment growth in West Germany: A dynamic panel approach

In this paper we study the dynamics of local employment growth in West Germany from 1980 to 2001. Using dynamic panel techniques, we analyse the timing of the impact of diversity and specialisation, as well as of the human capital structure of local industries. Diversity has a positive effect on employment growth in the short run, which is stronger in manufacturing than in services. Concerning specialisation we find also positive effects. They are, however, not strong enough to reinforce growth permanently, since there is mean reversion in the process. A positive effect of education is only found in manufacturing. Additionally, we look at the impact of firm size and regional wages on local employment growth.     

On downside risk predictability through liquidity and trading activity: A dynamic quantile approach

Most downside risk models implicitly assume that returns are a sufficient statistic with which to forecast the daily conditional distribution of a portfolio. In this paper, we analyze whether the variables that proxy for market-wide liquidity and trading conditions convey valid information for forecasting the quantiles of the conditional distribution of several representative market portfolios, including volume- and value-weighted market portfolios, and several Book-to-Market- and Size-sorted portfolios. Using dynamic quantile regression techniques, we report evidence of conditional tail predictability in terms of these variables. A comprehensive backtesting analysis shows that this link can be exploited in dynamic quantile modelling, in order to considerably improve the performances of day-ahead Value at Risk forecasts.     

Semiparametric quantile regression estimation in dynamic models with partially varying coefficients

We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both parametric and nonparametric functional coefficients are proposed. In particular, we propose a three stage semiparametric procedure. Both consistency and asymptotic normality of the proposed estimators are derived. We demonstrate that the parametric estimators are root-n$n$ consistent and the estimation of the functional coefficients is oracle. In addition, efficiency of parameter estimation is discussed and a simple efficient estimator is proposed. A simple and easily implemented test for the hypothesis of a varying-coefficient is proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimators.     

Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution

This paper develops a Bayesian method for quantile regression for dichotomous response data. The frequentist approach to this type of regression has proven problematic in both optimizing the objective function and making inferences on the parameters. By accepting additional distributional assumptions on the error terms, the Bayesian method proposed sets the problem in a parametric framework in which these problems are avoided. To test the applicability of the method, we ran two Monte Carlo experiments and applied it to Horowitz's (1993) often studied work‐trip mode choice dataset. Compared to previous estimates for the latter dataset, the method proposed leads to a different economic interpretation. Copyright © 2010 John Wiley & Sons, Ltd.     

Asymmetric dependence structures for regional stock markets: An unconditional quantile regression approach

Owing to the asymmetry of stock markets, this study investigates the dependence structures for six regional stock markets according to different market conditions by applying the unconditional quantile regression (UQR) approach. This approach can address the traditional conditional quantile regression (CQR) approach’s limitation that its distributions are defined conditional on specific covariates. Specifically, we not only examine the detailed linkages among these six regional stock markets, but also explore the effect of global economic factors on them, given the strengthening of both international investment and the globalization of financial markets. The results show these dependence structures are often an asymmetric U-shaped or inverted U-shaped structure, which indicates that the impacts of both other geographically and economically close stock markets and economic factors are more pronounced during bear and bull markets than during normal markets, especially so in bear markets. Moreover, the UQR approach provides stronger extreme-value relationships and more significant asymmetric effects than the traditional CQR approach.     

The relationship between board characteristics and performance of bank holding companies: before and during the financial crisis

We examine the relationship between performance of the bank holding company and several board characteristics. We find that board size, CEO tenure and board tenure enhance bank performance. However, we find no evidence that board structure or CEO power influences bank performance. More importantly, we show that the effect of board characteristics during the crisis is quite different. During the crisis, board size has a negative effect on Tobin’s Q and the non-performing asset ratio, which supports Jensen’s (1993) argument that large boards are less likely to function effectively. Further, we report that the non-performing asset ratio decreases with board independence during the crisis.