Conditional Extremes in Asymmetric Financial Markets

ABSTRACT

The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.     

SCOAP3 and Open Access

Abstract

SCOAP3 is an innovative Open Access initiative for publishing in high-energy physics. The model is viewed by many as a potential solution to multiple issues related to the financial crisis, the peer review system, scholarly communication, and the need to support institutional repositories. This installment of “The Balance Point” presents articles written by three Open Access advocates, outlining the SCOAP3 proposal, benefits of participation, and some of the roles libraries, publishers and scientists can play in making important changes to scholarly communication. Contributors discuss scalability and transferability issues of SCOAP3, as well as other matters of concern.     

Nonparametric estimation of the mean function of a stochastic process with missing observations

In an attempt to identify similarities between methods for estimating a mean function with different types of response or observation processes, we explore a general theoretical framework for nonparametric estimation of the mean function of a response process subject to incomplete observations. Special cases of the response process include quantitative responses and discrete state processes such as survival processes, counting processes and alternating binary processes. The incomplete data are assumed to arise from a general response-independent observation process, which includes right- censoring, interval censoring, periodic observation, and mixtures of these as special cases. We explore two criteria for defining nonparametric estimators, one based on the sample mean of available data and the other inspired by the construction of Kaplan-Meier (or product-limit) estimator [J. Am. Statist. Assoc. 53 (1958) 457] for right-censored survival data. We show that under regularity conditions the estimated mean functions resulting from both criteria are consistent and converge weakly to Gaussian processes, and provide consistent estimators of their covariance functions. We then evaluate these general criteria for specific responses and observation processes, and show how they lead to familiar estimators for some response and observation processes and new estimators for others. We illustrate the latter with data from an recently completed AIDS clinical trial.     

Closing the Loop on Collections Review

Abstract

Resource cancelations happen all the time; however, assessing the outcomes of this process isn’t necessarily a priority. As our profession continues to move toward data supported decisions, how can we connect new data to decisions made 2–4?years ago? Western Carolina University will outline their approach to assessing their most recent cancelation decisions using interlibrary loan and historical journal price analysis data and discuss their plans for using key findings to plan for future collections reviews and to make budget asks.     

金融危机传染效应的资产动态组合视角解析

由金融危机三阶段视角透视跨国投资组合供需动态变化过程中金融危机的传染特性。以跨国投资者投资决策与投资业绩互动为突破点,剖析在金融危机三阶段内调整跨国资产组合配置的微观交易行为所引致的金融危机传染性。经由9个国家金融危机期间基金交易数据的计量检验得出：金融危机中跨国投资者资产组合再分配是金融危机重要的传染渠道;与金融危机发源国分享风险偏好型跨国投资者的国家最容易被危机感染;金融危机三阶段传染效应的强度呈动态变化;金融市场上投资者的信息搜集在化解市场风险方面具有重要作用。     

Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

This article documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The article is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focus on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.     

Estimating and Testing Nonlinear Local Dependence Between Two Time Series

ABSTRACT

The most common measure of dependence between two time series is the cross-correlation function. This measure gives a complete characterization of dependence for two linear and jointly Gaussian time series, but it often fails for nonlinear and non-Gaussian time series models, such as the ARCH-type models used in finance. The cross-correlation function is a global measure of dependence. In this article, we apply to bivariate time series the nonlinear local measure of dependence called local Gaussian correlation. It generally works well also for nonlinear models, and it can distinguish between positive and negative local dependence. We construct confidence intervals for the local Gaussian correlation and develop a test based on this measure of dependence. Asymptotic properties are derived for the parameter estimates, for the test functional and for a block bootstrap procedure. For both simulated and financial index data, we construct confidence intervals and we compare the proposed test with one based on the ordinary correlation and with one based on the Brownian distance correlation. Financial indexes are examined over a long time period and their local joint behavior, including tail behavior, is analyzed prior to, during and after the financial crisis. Supplementary material for this article is available online.     

基于适度财务指标和遗传算法的财务危机预测模型研究

以往进行财务危机预测主要使用统计方法建模,迄今尚未见到因考虑财务指标的适度性而建立遗传算法模型的中外文献。适度财务指标是指该指标在某个区间内公司才是健康的,过大或过小则公司发生财务危机的可能性都较大。故以中国上市公司数据为样本,建立了基于适度财务指标和遗传算法的创新型财务危机预测模型,结果显示该类模型具有较高的预测准确率。     

Risk minimization for an insurer with investment and reinsurance via g-expectation

Abstract

This paper is devoted to the study of a risk-based optimal investment and proportional reinsurance problem. The surplus process of the insurer and the risky asset process in the financial market are assumed to be general jump-diffusion processes. We use a convex risk measure generated by g-expectation to describe the risk of the terminal wealth with investment and reinsurance. Under the aim of minimizing the risk, the problem is solved by using techniques of stochastic maximum principles. Two interesting special cases are studied and the explicit expressions for optimal strategies and corresponding minimal risks are derived.     

Consistency of information criteria for model selection with missing data

ABSTRACT

In this paper, we investigate the consistency of the Expectation Maximization (EM) algorithm-based information criteria for model selection with missing data. The criteria correspond to a penalization of the conditional expectation of the complete data log-likelihood given the observed data and with respect to the missing data conditional density. We present asymptotic properties related to maximum likelihood estimation in the presence of incomplete data and we provide sufficient conditions for the consistency of model selection by minimizing the information criteria. Their finite sample performance is illustrated through simulation and real data studies.     

A robust R control chart based on a two-step estimator of the process dispersion

ABSTRACT

Control charts are the frequently used tools for monitoring and controlling the processes. Classical control charts are sensitive to existing contaminated data which may be presented in the data collected from the processes. Thus, these charts are not able to control the processes precisely when the data are contaminated. Robust control charts are those which are less sensitive to contamination. Some robust control charts for monitoring the process variability were proposed in the past which are robust to some sorts of contamination. In this paper a new robust R control chart is proposed which is less sensitive to wide range of contaminations, i.e. general and local contaminations. Simulation studies are performed to compare the performance of the proposed control chart with some classical and robust control charts, using ARL and MSD as criteria for comparisons purposes. The simulation results show a very good performance of the proposed chart when both types of contaminations exist.     

Robust bootstrap densities for dynamic conditional correlations: implications for portfolio selection and Value-at-Risk

ABSTRACT

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions depend on the forecasts covariance matrix little is known about effects of outliers on the uncertainty associated with these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation models when the uncertainty is measured using bootstrap methods. We also propose a bootstrap procedure to obtain forecast densities for return, volatilities, conditional correlation and Value-at-Risk that is robust to outliers. The results are illustrated with simulated and real data.     

Estimation and Simulation of the Riesz-Bessel Distribution

ABSTRACT

In this article, further properties of the Riesz-Bessel distribution are provided. These properties allow for the simulation of random variables from the Riesz-Bessel distribution. Estimation is addressed by nonlinear generalized least squares regression on the empirical characteristic function. The estimator is seen to approximate the maximum likelihood estimator. The distribution is illustrated with financial data.     

Goodness-of-fit tests for the Gompertz distribution

Abstract

While the Gompertz distribution is often fitted to lifespan data, testing whether the fit satisfies theoretical criteria is being neglected. Here four goodness-of-fit measures – the Anderson–Darling statistic, the correlation coefficient test, a statistic using moments, and a nested test against the generalized extreme value distributions – are discussed. Along with an application to laboratory rat data, critical values calculated by the empirical distribution of the test statistics are also presented.     

The Hard Task of Soft Skills: Project Management for the Materials Review Process

Abstract

This article summarizes a presentation given by Kurt Blythe and Jennifer Solomon at the 28th annual North Carolina Serials Conference in 2019. Blythe and Solomon were brought on to lead a project to review resources in anticipation of budget constraints in 2018 and 2019. The presentation sought to answer why libraries should apply project management principles in such a situation and what the presenters learned from their experience and offered tips to any audience members considering applying project management practices to their own work.     

Some optimal block designs with nested rows and columns for research on alternative methods of limiting slug damage

Some criteria of optimality of a block design with nested rows and columns are considered. The criteria are based on the eigenvalues of the information matrix C or on the eigenvalues of the matrix C with respect to a diagonal matrix R of treatment replications. New constructions of some optimal block designs with nested rows and columns are presented for application to special plant protection experiments.     

Pricing longevity-linked derivatives using a stochastic mortality model

Abstract

We propose a 2-factor MBMM model with exponential Lévy process to develop a stochastic mortality process. The two components are fitted by two independent NIG distributions. Compared to Lee–Carter model or 1-factor MBMM model, our mortality model explains more variation and improves the goodness of fit by including the second time component. Based on the improved model, we price three longevity-linked financial instruments, namely the longevity bond, q-forward and s-forward. The pricing is demonstrated on English and Welsh males aged 65 in 2013. Results indicate that the 2-factor MBMM model gives the highest price for mortality-related type of contract.     

ADAPTIVE GROUP SEQUENTIAL PROCEDURES FOR MULTIPLE COMPARISON OF TREATMENTS

ABSTRACT

We present a flexible group sequential procedure for comparing several treatments to a control. Though longitudinal data corresponding to a two stage mixed effects model are considered, ranges of application include any process with independent increments. The procedure allows the experimenter to drop the inferior treatments from the trial as soon as they are detected. It control strongly the familywise error rate. We also discuss a new error spending function (ESF) and study the performance of the procedure using various ESFs and time scales. Finally, the procedure is illustrated on a real example and implementation considerations are discussed.     

Testing the Martingale Difference Hypothesis

Abstract

In this paper we consider testing that an economic time series follows a martingale difference process. The martingale difference hypothesis has typically been tested using information contained in the second moments of a process, that is, using test statistics based on the sample autocovariances or periodograms. Tests based on these statistics are inconsistent since they cannot detect nonlinear alternatives. In this paper we consider tests that detect linear and nonlinear alternatives. Given that the asymptotic distributions of the considered tests statistics depend on the data generating process, we propose to implement the tests using a modified wild bootstrap procedure. The paper theoretically justifies the proposed tests and examines their finite sample behavior by means of Monte Carlo experiments.