The predictive accuracy of credit ratings: Measurement and statistical inference

Credit ratings are ordinal predictions of the default risk of an obligor. The most commonly used measure for evaluating their predictive accuracy is the Accuracy Ratio, or equivalently, the area under the ROC curve. The disadvantages of these measures are that they treat default as a binary variable, thus neglecting the timing of default events, and they fail to use all of the information available from censored observations. We present an alternative measure which is related to the Accuracy Ratio but does not suffer from these drawbacks. As a second contribution, we study statistical inference for the Accuracy Ratio and the proposed measure in the case of multiple cohorts of obligors with overlapping lifetimes. We derive methods which use more sample information and lead to tests which are more powerful than alternatives which filter just the independent part of the dataset. All procedures are illustrated in the empirical section using a dataset of S&P Credit Ratings.     

城镇建设资金运作与改革

我国城镇基础设施建设资金严重不足 ,根本原因是城镇建设资金运作有悖于公共财政原理。本文结合公共产品理论 ,提出城镇建设产品的各类资金运作模式。     

Forecasting risk measures using intraday data in a generalized autoregressive score framework

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression set-up. We consider four intraday measures: the realized volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. In a forecasting study, the set of newly proposed semiparametric models are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized volatility measures, outperform the benchmark models consistently across all indices and various probability levels.     

A Bayesian approach to optimal monetary policy with parameter and model uncertainty

This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetary-policy models that include both forward- and backward-looking representations as well as large- and small-scale models. We find an optimal simple Taylor-type rule that accounts for both model and parameter uncertainty. For the most part, backward-looking models are highly fault tolerant with respect to policies optimized for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimized for backward-looking representations. In addition, backward-looking models often have lower posterior probabilities than forward-looking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forward-looking models.     

The analysis of risk perception with fuzzy means-end approach

Visitors’ risk perceptions have been found to influence the on-site behavior of tourists and their intention to return to a destination or to recommend it to others. This study discusses how the uses of a means-end approach with fuzzy conceptualization in eliciting the perception of tourism risks in a better understanding of the visitors’ perceptual orientation toward the tourism values. We provide a hierarchy value map that fuses the attribute–consequence–value (A–C–V) and fuzzy linguistics to effectively and efficiently understand vacation risks and risk characteristics. Fuzzy logic is also adopted to deal with the ill-defined nature of the tourist linguistic judgments required in the proposed means-end chain. This research findings suggest that additionally to managing the most likely risks, tourist resorts should be prepared to cope with worst case scenarios such as “Thunderstorm”, “Bus accident”, “Food poisoning” and “Cable car accident”. From an overall risk perceptive, tourists are most concerned with dominant perceptual orientation of risk delivers being “Bus accident” → “Decrease of trust in the safety management as a result of the event of damage” → “Anger”.     

The RWDAR model: A novel state-space approach to forecasting

This paper introduces the Random Walk with Drift plus AutoRegressive model (RWDAR) for time-series forecasting. Owing to the presence of a random walk plus drift term, this model shares some similarities with the Theta model of Assimakopoulos and Nikolopoulos (2000). However, the addition of a first-order autoregressive term in the state equation provides additional adaptability and flexibility. Indeed, it is shown that RWDAR tends to outperform the Theta model when forecasting both stationary and nearly non-stationary time series. This paper also proposes a simple estimation method for the RWDAR model based on the solution of the algebraic Riccati equation for the prediction error covariance of the state vector. Simulation results show that this estimator performs as well as the standard Kalman filter approach. Finally, using yearly data from the M3 and M4 competition datasets, it is found that RWDAR outperforms traditional forecasting methods.     

A dynamic programming approach to work force scheduling with time-dependent performance measures

An implicit enumeration algorithm is defined to obtain solutions to the commercial bank check processing encoder scheduling problem. The specific application is of particular interest because a significant factor in determining optimality is the float costs associated with checks which are unprocessed and unavailable for presentation at check clearing deadlines, thereby making the timing of the activity of crucial importance. A one day time horizon is employed to reflect those situations where banks have the scheduling flexibility afforded by part-time and/or temporary help in additon to a complement of full-time operators. Comparisons are made with other suggested approaches to daily encoder scheduling. Results indicate that dynamic programming can be an attractive methodology to attack this complex problem.     

A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales

We present our solution for the M5 Uncertainty competition. Our solution ranked sixth out of 909 submissions across all hierarchical levels and ranked first for prediction at the finest level of granularity (product-store sales, i.e. SKUs). The model combines a multi-stage state-space model and Monte Carlo simulations to generate the forecasting scenarios (trajectories). Observed sales are modelled with negative binomial distributions to represent discrete over-dispersed sales. Seasonal factors are handcrafted and modelled with linear coefficients that are calculated at the store-department level.     

Solving the incomplete market model with aggregate uncertainty using a perturbation method

We use a perturbation method to solve the incomplete markets model with aggregate uncertainty described in den Haan et al. [Computational suite of models with heterogeneous agents: incomplete markets and model uncertainty. Journal of Economic Dynamics & Control, this issue]. To apply that method, we use a “barrier method” to replace the original problem with occasionally binding inequality constraints by one with only equality constraints. We replace the structure with a continuum of agents by a setting in which a single infinitesimal agent faces prices generated by a representative-agent economy. We also solve a model variant with a large (but finite) number of agents. Our perturbation-based method is much simpler and faster than other methods.     

Rank-ordering of risk preferences with conventional and discrete measures

When the functional form of utility is unknown, conventional measures of risk aversion are often approximated by applying a Taylor series expansion to expected utility. This is shown to produce counterintuitive rank-orderings of risk preferences for individuals who are willing to pay equal reservation prices in lotteries with different prizes. Moreover, individuals who are unwilling to participate in favorable lotteries may be incorrectly identified as having a finite aversion to risk. Correct orderings are obtained by applying a discrete measure of relative risk aversion. The contrast between the conventional and discrete measures is illustrated with data from three Dutch surveys.     

The power of using video data

Most data for qualitative research studies in the management field are collected through observations and interviews. Written words and photographs are too static to accurately portray the depth of the given event or interview. We advocate the application of video data. The advantages of video data are its ability to capture the interaction of the participant with the environment, capturing of nonverbal cues, and establishing a permanent record. We recommend the use of Patten’s (Qualitative Evaluation and Research Methods, Sage, Newborn, 1990) process of analysis as a basis for an analytical procedure. To code video data research, software such as Vivo or ATLAS.ti can be used. The four standards to examine the validity and reliability of qualitative research studies (credibility, transferability, dependability and conformability) should also apply to video data analysis. The only outcome and true goal for the use of video data in management research can only be the provision of richer knowledge for academic development.     

The power of tests of predictive ability in the presence of structural breaks

This paper presents analytical, Monte Carlo, and empirical evidence on the effects of structural breaks on tests for equal forecast accuracy and encompassing. We show that out-of-sample predictive content can be hard to find because out-of-sample tests are highly dependent on the timing of the predictive ability. Moreover, predictive content is harder to find with some tests than others: in power, F-type tests of equal forecast accuracy and encompassing often dominate t-type alternatives. Based on these results and evidence from an empirical application, we conclude that structural breaks under the alternative may explain why researchers often find evidence of in-sample, but not out-of-sample, predictive content.     

A system maintenance approach to analyzing the Italian judicial system reform proposal of 2010

In this paper, we analyze the Italian reform of the judicial system proposed in 2010 by using multi-state models. The transitions between states are assumed to be generated by a semi-Markov process. The introduction of the reform is considered as a general system maintenance policy. The effects of the reform are evaluated by computing the system’s transition probabilities, failure rates and odds ratios of failure rates. The introduction of a reward structure measures the impact of the reform from the point of view of costs. An example showing the possibility of implementing a real data application is presented.     

An empirical examination of the effectiveness of dollar-cost averaging using downside risk performance measures

Some studies find the dollar-cost averaging investment strategy to be sub-optimal using a traditional Sharpe ratio performance ranking metric. Using both the Sortino ratio and the Upside Potential ratio, we empirically test four investment strategies for alternative asset investments. We find the relative ranking of dollar-cost averaging remains inferior to alternative investment strategies. (JEL G1, G11, N2)     

Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach

This paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by proposing a log-semi-nonparametric (log-SNP) distribution as the implicit RND when the Gram-Charlier model is used for option pricing. The performance of the model is compared to the lognormal (Black Scholes) benchmark for a sample of option prices for West Texas Intermediate (WTI) crude oil that were traded in the period between January 2016 and December 2017. Results show that the lognormal specification tends to systematically undervalue option prices and that the proposed log-SNP distribution, which explicitly adjusts for negative skewness and excess kurtosis, results in markedly improved accuracy, especially in periods of market instability. As a result, the implied skewness and excess kurtosis are relevant sources of information on market expectations that should be used for hedging and risk management purposes.     

Taking a new contour: A novel approach to panel unit root tests

The paper introduces a novel approach to testing for unit roots in panels, which takes a new contour that is drawn along the line given by the equi-squared-sum instead of the traditional one given by the equi-sample-size. We show in the paper that the distributions of the unit root tests are asymptotically normal along the new contour under both the null and the local-to-unity alternatives. Subsequently, we demonstrate that this startling finding may be exploited constructively to invent tools and methodologies for effective inferences in panel unit root models. Simulations show that our approach works quite well in finite samples.     

A novel approach to legacy donations with long-term benefits supported by numerical illustrations

Philanthropic donors face challenges in matching the causes to which they donate, the time horizon—and thus impact—of their donations, and the charitable vehicles they choose for making contributions. Wealthier donors may elect to create their own foundations and customize their charitable support. Less wealthy donors have limited choices: they may contribute to a nonprofit's current operations or to existing nonprofit endowments. We present a novel approach for making charitable donations, blending aspects of each of these strategies. Our approach has potential long-term financial benefits, allows donors to control their charitable donations in a convenient and easy-to-implement manner, can be established through an existing nonprofit organization, expands opportunities for more donors because it requires a smaller corpus contribution with lower management costs than creating a foundation, provides tax savings in the United States and other countries (e.g., the UK, Canada, and Australia) comparable to other planned giving vehicles, and may be implemented during one's lifetime using donor advised funds or as part of a legacy plan through the donor's estate documents, which is when the long-term benefits accrue.     

Sovereign default risk,debt uncertainty and fiscal credibility: The case of Brazil

Since Credit Default Swaps spreads reflect the sovereign risk and, thus, the uncertainties related to government solvency, the goal of this study is to examine the relation between sovereign risk and debt uncertainty (measured by the disagreement in expectations about public debt) in an important developing country – Brazil. Furthermore, the paper analyzes whether fiscal credibility plays a key role in mitigating the effect of debt uncertainty on sovereign risk. The results suggest the disagreement in expectations about public debt affects the sovereign risk, and fiscal credibility plays a twofold role, it reduces sovereign risk, and it mitigates the effect of debt uncertainty on sovereign risk. Besides, quantile regression estimates reveal that fiscal credibility improvements are even more important when sovereign risk levels are higher.