Hedonic price models and indices based on boosting applied to the Dutch housing market

We create a hedonic price model for house prices for six geographical submarkets in the Netherlands. Our model is based on a recent data-mining technique called boosting. Boosting is an ensemble technique that combines multiple models, in our case decision trees, into a combined prediction. Boosting enables capturing of complex nonlinear relationships and interaction effects between input variables. We report mean relative errors and mean absolute error for all regions and compare our models with a standard linear regression approach. Our model improves prediction performance by up to 39% compared with linear regression and by up to 20% compared with a log-linear regression model. Next, we interpret the boosted models: we determine the most influential characteristics and graphically depict the relationship between the most important input variables and the house price. We find the size of the house to be the most important input for all but one region, and find some interesting nonlinear relationships between inputs and price. Finally, we construct hedonic price indices and compare these with the mean and median index and find that these indices differ notably in the urban regions of Amsterdam and Rotterdam. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal reinsurance under adjustment coefficient measure in a discrete risk model based on Poisson MA(1) process

In this paper, we study the retention levels for combinations of quota-share and excess of loss reinsurance by maximizing the insurer’s adjustment coefficient, which in turn minimizes the asymptotic result of ruin probability. Assuming that the premiums are determined by the expected value principle, we consider a discrete risk model, in which a dependence structure is introduced based on Poisson MA(1) process between the claim numbers for each period. The impact of dependence parameter on the adjustment coefficient is discussed and numerical examples are provided to illustrate the results obtained in this paper.     

A new way of looking at the risk in defined benefit pension plans

The portability feature of a defined contribution (DC) pension greatly reduces the risk to the accumulation of pension wealth. Conversely, defined benefit (DB) pensions have a variety of default risks that decrease the expected value of DB pension wealth. This paper examines those risks. Accrual of DB pension wealth is characterized in terms of purchases of risky bonds. Changing jobs triggers default on these bonds. Simulations are presented to show the potential loss in pension wealth from default. In addition, a methodology used to price corporate bonds is applied to generate estimates of the implied risk premiums of DB pension bonds over comparable riskless bonds.     

Valuation of an early exercise defined benefit underpin hybrid pension

In this paper, we consider three types of embedded options in pension benefit design. The first is the Florida second election (FSE) option, which has been offered to public employees in the state of Florida since 2002. The state runs both defined contribution (DC) and defined benefit (DB) pension plans. Employees who initially join the DC plan have the option to convert to the (DB) plan at a time of their choosing. The cost of the switch is assessed in terms of the ABO (Accrued Benefit Obligation), which is the expected present value of the accrued DB pension at the time of the switch. If the ABO is greater than the DC account, the employee is required to fund the difference. The second is the DB Underpin option, also known as a ‘floor offset’ or a ‘Greater-of-benefit’ plan, under which the employee participates in a DC plan, but with a guaranteed minimum benefit based on a traditional DB formula. The third option can be considered a variation on each of the first two. We remove the requirement from the FSE option for employees to fund any shortfall at the switching date. The resulting plan is similar to the DB underpin, but with the possibility of early exercise. We adopt an arbitrage-free pricing methodology to value each option. We analyse and value the optimal switching strategy for the employee by constructing an exercise frontier, and we illustrate numerically the difference between the FSE, DB Underpin and Early Exercise DB Underpin options.     

Adapting extreme value statistics to financial time series: dealing with bias and serial dependence

We handle two major issues in applying extreme value analysis to financial time series, bias and serial dependence, jointly. This is achieved by studying bias correction methods when observations exhibit weak serial dependence, in the sense that they come from \(\beta\)-mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the \(\beta\)-mixing condition. The bias correction procedure and the dependence structure have a joint impact on the asymptotic variance of the estimator. Then we construct an asymptotically unbiased estimator of high quantiles. We apply the new method to estimate the value-at-risk of the daily return on the Dow Jones Industrial Average index.     

Predicting issuer credit ratings using a semiparametric method

This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.     

Nonlinear Cointegration Relationships Between Non-Life Insurance Premiums and Financial Markets

The aim of this article is to study the adjustment dynamics of the non-life insurance premium (NLIP) and test its dependence to the financial markets in five countries (Canada, France, Japan, the United Kingdom, and the United States). First, we justify the linkage between the insurance and the financial markets by the underwriting cycle theory and financial models of insurance pricing. Second, we examine the relationship between the NLIP, the interest rate, and the stock price using the recent developments of nonlinear econometrics. We use threshold cointegration models: the switching transition error correction models (STECM). We show that STECM perform better than a linear error correction model (LECM) to reproduce the NLIP dynamics. Our empirical results show that the adjustment of the NLIP in France, Japan, and the United States is rather discontinuous, asymmetrical, and nonlinear. Moreover, we suggest a strong evidence of significant linkages between insurance and financial markets, show two regimes for the NLIP, and find that the NLIP adjustment toward equilibrium is time varying with a convergence speed that varies according to the insurance disequilibrium size.     

Does freezing a defined benefit pension plan affect firm risk?

This paper examines the impact of a defined benefit (DB) pension plan freeze on the sponsoring firm's risk and risk-taking activities. Using a sample of firms declaring a hard freeze on their DB plans between 2002 and 2007, we observe an increase in total risk (proxied by the standard deviation of EBITDA and asset beta), equity risk (standard deviation of returns), and credit risk following a DB-plan freeze. The increase in credit risk is reflected in a decline in credit ratings and an increase in bond yields for freezing firms. When we examine investment strategies, we observe a shift in investment from capital expenditures before the freeze to more-risky R&D projects after the freeze, and an increase in leverage. These strategies (increased focus on R&D and higher leverage) increase the operating and financial risk the firm faces. Overall, we observe an increase in risk-taking following DB plan freezes, consistent with theories that DB plans act as “inside debt” that aligns managers’ interests with bondholders’.     

Nonlife actuarial models,theory, methods and evaluation by Yiu-Kuen Tse

We consider two different portfolios of proportional reinsurance of the same pool of risks. This contribution is concerned with Gaussian-like risks, which means that for large values the survival function of such risks is, up to a multiplier, the same as that of a standard Gaussian risk. We establish the tail asymptotic behavior of the total loss of each of the reinsurance portfolios and determine also the relation between randomly scaled Gaussian-like portfolios and unscaled ones. Further, we show that jointly two portfolios of Gaussian-like risks exhibit asymptotic independence and their weak tail dependence coefficient is nonnegative.     

Asymptotic replication with modified volatility under small transaction costs

We consider the dynamic hedging of a European option under a general local volatility model with small proportional transaction costs. Extending the approach of Leland, we introduce a class of continuous strategies of finite cost that asymptotically (super-)replicate the payoff. An associated central limit theorem for the hedging error is proved. We also obtain an explicit trading strategy minimizing the asymptotic error variance.     

Iterative Adjustment of Survival Functions by Composed Probability Distortions

We introduce a parametric class of composite probability distortions that can be combined to converge to a target survival function. These distortions respect analytic invertibility and stability, which are shown to be relevant in many actuarial fields. We study the asymptotic impact of such distortions on hazard rates. The paper provides an estimation methodology, including hints for initialisation. Some applications to survival data bring results for catastrophic event impact modelling. We also obtain accurate parametric representations of the mortality trend over years. Finally, we suggest a prospective mortality simulation model that comes naturally from the above analysis.     

On a Statistical Analysis of Implied Data

We propose a method of evaluating the accuracy of the implied default probabilities. We modify the model proposed by Duffie et al. (Rev Fin Stud 12:678–720, 1999) to allow the parametric statistical analysis. The pseudo maximum likelihood estimator is defined and to justify our method we shall prove the consistency and the asymptotic normality of the estimator. The key step is to define a pseudo score vector and apply the method of Wald (Ann Math Stat 20: 595–601, 1949) and a delta method. We also introduce the bootstrap for estimating the accuracies, which is similar to that for regression models. To implement our method to the real data, we shall recommend the bootstrap rather than asymptotic normality.     

Non-stationary Hours in a DSGE Model

The time series fit of dynamic stochastic general equilibrium (DSGE) models often suffers from restrictions on the long-run dynamics that are at odds with the data. Using Bayesian methods we estimate a stochastic growth model in which hours worked are stationary and a modified version with permanent labor supply shocks. If firms can freely adjust labor inputs, the data support the latter specification. Once we introduce frictions in terms of labor adjustment costs, the overall time series fit improves and the model specification in which labor supply shocks and hours worked are stationary is preferred.     

Asymptotic Analysis of the Loss Given Default in the Presence of Multivariate Regular Variation

Consider a portfolio of n obligors subject to possible default. We propose a new structural model for the loss given default, which takes into account the severity of default. Then we study the tail behavior of the loss given default under the assumption that the losses of the n obligors jointly follow a multivariate regular variation structure. This structure provides an ideal framework for modeling both heavy tails and asymptotic dependence. Multivariate models involving Archimedean copulas and mixtures are revisited. As applications, we derive asymptotic estimates for the value at risk and conditional tail expectation of the loss given default and compare them with the traditional empirical estimates.     

基于空间关联性的广深两地产业结构调整研究——珠三角经济区域背景下的考察

本文以珠三角区域九大城市的产业结构演进与关联为背景,利用空间滞后模型SLM和地理加权回归模型GWR分全域和局域对珠三角九大城市产业结构变化的空间关系进行空间计量回归,在全域回归分析的结论基础上进一步验证广深两地局域存在的产业结构调整动态的空间关联性,结果显示：从全域空间计量的参数估计和分析结论来看,变量消费、产业结构初始值、人均GDP、技术进步率和政府干预显著,其余变量则不显著;进一步的GWR局域分析表明,产业结构初始值、外商直接投资和政府干预等因素,对区域内各城市之间的产业结构调整作用均显示出异质性。基于这两方面的分析,为珠三角区域产业结构调整,尤其是中心城市产业结构调整,提出对策建议。     

泛珠三角战略:湖南的定位与发展

"泛珠三角"区域合作是区域经济一体化的必然结果,具有重大的战略意义.湖南作为中部内陆省份,在东西部相继崛起的双重夹击下,如果不抓住机遇迅速融入"泛珠三角"经济圈,尽快找到突破口,就难以改变落后的被动局面.湖南必须用深远的战略眼光来考察发展全局,以积极的姿态主动地参与"泛珠三角"区域经济合作,发挥优势,扬长避短,突出产业重心,坚持实施"核心城市极化、产业集群化、企业集团化"的发展战略,更进一步地推进湖南的现代化进程.     

Product Market Competition and the Ownership Choice of Business Groups: Evidence from Korean Chaebols

This study investigates the effect of product market competition on the ownership choice of controlling shareholders in the Korean business groups known as chaebols. We find that member firms in more competitive markets have less disparity between the control and cash flow rights of controlling shareholders. The adjustment in ownership due to product market competition is implemented mainly through an adjustment in the ownership of affiliates rather than in the direct ownership of controlling shareholders. The disciplinary effect of product market competition is observed only in member firms with lower market power in their own industries. The result implies that product market competition works as a disciplinary mechanism that reduces the incentive of chaebols’ controlling shareholders to pursue the private benefits of control.     

An approximate dynamic programming approach to solving dynamic oligopoly models

In this article, we introduce a new method to approximate Markov perfect equilibrium in large‐scale Ericson and Pakes (1995)‐style dynamic oligopoly models that are not amenable to exact solution due to the curse of dimensionality. The method is based on an algorithm that iterates an approximate best response operator using an approximate dynamic programming approach. The method, based on mathematical programming, approximates the value function with a linear combination of basis functions. We provide results that lend theoretical support to our approach. We introduce a rich yet tractable set of basis functions, and test our method on important classes of models. Our results suggest that the approach we propose significantly expands the set of dynamic oligopoly models that can be analyzed computationally.     

A meta-analysis of mutual fund performance

The purpose of this article is to introduce the statistical technique of meta-analysis of regression results using as our example the Lee and Rahmann (1990) study of the performance of 93 mutual funds. Specifically, we derive and estimate the meta-analysis formulas, explicitly adjusted for correlated regression residuals, which quantify the effect of sampling error on their reported regression results. Our analysis of selectivity reveals some real variation around a mean risk-adjusted excess return of about 1% per year; while our analysis of market timing reveals some real variation around a negative mean value and confirms that the correction for heteroscedasticity does make a difference. An examination of the 80% probability interval for the mean selectivity value indicates that the best mutual funds can deliver substantial risk-adjusted excess returns.     

Asymptotic Fairness of Bonus-Malus Systems and Optimal Scales of Premiums

In this paper we try to evaluate the asymptotic fairness of bonus-malus systems, assuming the simplest case when there is no hunger for bonus. The asymptotic fairness has to be understood as the bonus-malus system ability in assessing the individual risks in the long run. Firstly we define the asymptotic fairness of a bonus-malus system following an expression that can be found in J. Lemaire [1985]: Automobile Insurance. Actuarial Models. Dordrecht: Kluwer-Nijhoff Publishing, p. 168. Secondly, we define a measure of the global asymptotic fairness considering the structure function of the risk group. Finally we try to calculate, for each set of transition rules and a given structure function, the scale of premiums that brings the global asymptotic fairness closest to the ideal situation where each insured pays in the long run a premium corresponding to its own claim frequency. This is possible thanks to the application of a multiobjective optimization technique named Goal Programming.