A supply chain network equilibrium model with a smoothing newton solution scheme

ABSTRACT

Considering that a supply chain comprises several independent decision makers, a supply chain network equilibrium model that consists of manufacturers, retailers and consumers is developed. After analysing the optimal conditions of various decision makers in the model, the equilibrium condition is established as an equivalent, finite-dimensional variational inequality formulation and is solved by a smoothing Newton method. The global and quadratic convergence of the method is established. The numerical results show the rapid convergence of the method. Additionally, the rapid convergence of the smoothing Newton method is beneficial when solving a complicated network model in the real world.     

Digital Financial Inclusion and Consumption Smoothing in China

In this paper, we investigate the effect of digital financial inclusion (DFI) on household consumption smoothing in China. We use four waves of the biennial China Family Panel Studies from 2010 to 2016, during which time DFI has significantly developed alongside financial technology across China. We split household income shocks into permanent and transitory components, and evaluate if DFI may help households to buffer against these shocks. We find that households are not able to insure against permanent shocks to income, but they can smooth approximately 70 percent of transitory shocks to income. We also find that DFI has diminished households’ ability to insure against transitory income shocks. This is partly because online purchase may lead to the oversensitivity of consumption to income. In addition, we find that contrary to DFI, traditional financial sector development contributes to better household consumption smoothing against transitory income shocks.     

Improving time series forecasting: An approach combining bootstrap aggregation,clusters and exponential smoothing

Some recent papers have demonstrated that combining bagging (bootstrap aggregating) with exponential smoothing methods can produce highly accurate forecasts and improve the forecast accuracy relative to traditional methods. We therefore propose a new approach that combines the bagging, exponential smoothing and clustering methods. The existing methods use bagging to generate and aggregate groups of forecasts in order to reduce the variance. However, none of them consider the effect of covariance among the group of forecasts, even though it could have a dramatic impact on the variance of the group, and therefore on the forecast accuracy. The proposed approach, referred to here as Bagged.Cluster.ETS, aims to reduce the covariance effect by using partitioning around medoids (PAM) to produce clusters of similar forecasts, then selecting several forecasts from each cluster to create a group with a reduced variance. This approach was tested on various different time series sets from the M3 and CIF 2016 competitions. The empirical results have shown a substantial reduction in the forecast error, considering sMAPE and MASE.     

A bootstrap-based non-parametric forecast density

Interest in density forecasts (as opposed to solely modeling the conditional mean) arises from the possibility of dynamics in higher moments of a time series, as well as in forecasting the probability of future events in some applications. By combining the idea of Markov bootstrapping with that of kernel density estimation, this paper presents a simple non-parametric method for estimating out-of-sample multi-step density forecasts. The paper also considers a host of evaluation tests for examining the dynamic misspecification of estimated density forecasts by targeting autocorrelation, heteroskedasticity and neglected non-linearity. These tests are useful, as a rejection of the tests gives insight into ways to improve a particular forecasting model. In an extensive Monte Carlo analysis involving a range of commonly used linear and non-linear time series processes, the non-parametric method is shown to work reasonably well across the simulated models for a suitable choice of the bandwidth (smoothing parameter). Furthermore, an application of the method to the U.S. Industrial Production series provides multi-step density forecasts that show no sign of dynamic misspecification.     

Nonparametric methods for estimating and testing for constant betas in asset pricing models

In this article, we study the performance of a smoothing spline method in estimating and testing for constant betas in two well-known asset pricing models, the usual market model and the three-factor model. The spline estimator is computed taking into account the conditional heteroscedasticity of the errors. Using the right model and estimation procedure for the variance term plays a crucial role in gaining efficiency in beta estimators. A simulation study shows the good performance of our method; in all the scenarios simulated, it outperforms the benchmark rolling estimator. The method enables users to obtain confidence intervals and to test for the significance and constancy of betas. Finally, the method is applied to US data, comprising 25 portfolios formed based on size and the ratio of book equity to market equity. The results show that the time-variability of the betas plays an important role, mainly when sensitivity to the HML factor is considered.     

Calibrating the exponential Ornstein–Uhlenbeck multiscale stochastic volatility model

This paper demonstrates a tractable and efficient way of calibrating a multiscale exponential Ornstein–Uhlenbeck stochastic volatility model including a correlation between the asset return and its volatility. As opposed to many contributions where this correlation is assumed to be null, this framework allows one to describe the leverage effect widely observed in equity markets. The resulting model is non-exponential and driven by a degenerate noise, thus requiring a high level of care in designing the estimation algorithm. The way this difficulty is overcome provides guidelines concerning the development of an estimation algorithm in a non-standard framework. The authors propose using a block-type expectation maximization algorithm along with particle smoothing. This method results in an accurate calibration process able to identify up to three timescale factors. Furthermore, we introduce an intuitive heuristic which can be used to choose the number of factors.     

Bond Ratings and Income Smoothing in Brazil

ABSTRACT

In this article we use the variables proposed by Eckel (1981 Eckel , N. ( 1981 ). “The Income Smoothing Hypothesis Revisited.” Abacus , Vol. 17 , No. 1 , pp. 28 – 40 . [Google Scholar]) and Leuz et al. (2003 Leuz , C. , Nanda , D. , and Wysocki , P. ( 2003 ). “Earnings Management and Investors Protection: an International Comparison,” Journal of Financial Economics , Vol. 3 , No. 69 , pp. 505 – 527 . [Google Scholar]) as proxies to measure income smoothing and find that Brazilian companies that engage in this behavior receive better ratings in their public bond issues. Using data obtained from the National Bond Registration System and the Economatica, we evaluated public bond offerings. The results of univariate and multivariate analyses and robustness tests attest to the significance of the income smoothing factor, regardless of the rating agency. The results support the notion that income smoothing is an information-signaling mechanism and has an impact on bond ratings.     

Estimating Beta-Coefficients of German Stock Data: A Non-Parametric Approach

Abstract

Although the consumption based asset pricing theory appears to be theoretically superior and more elegant than the beta pricing model, in practice the beta pricing model is more widely applied. Indeed, beta pricing models are one of the most widely adopted tools in financial analysis. They readily allow handling systematic risk as priced in financial assets. However, accurately estimating beta-coefficients is not as straightforward as implicitly suggested by Sharpe's standard market model, i.e. simply using the ordinary least-squares (OLS) regression. This is primarily because beta-coefficients cannot generally be assumed to be stable over time. In order to overcome this deficiency, we present and apply a non-parametric estimation technique that allows capturing this time effect and promises both more reliable estimates than obtained with an OLS regression as well as better manageability compared with the existing time-series approaches dealing with time-varying beta-coefficients. Estimation results for constant and time-varying betas are presented for portfolios of German industries.     

Effects of earnings management on bank cost of debt

This study investigates how earnings management influences credit ratings, and thus the cost of debt, using bank data from 85 countries. Using cross‐country data also facilitates the investigation of how information asymmetry affects the influence of earnings management on ratings. The results indicate that raters downgrade ratings when they perceive earnings management, after controlling for other potential determinants of bank credit ratings, implying that earnings management increases borrowing costs. The negative effect of earnings management is mitigated for banks in countries with more extensive and effective banking regulations owing to lower information asymmetry, but aggravated in counties with less robust banking regulations.