A test of goodness-of-fit based on extreme spacings with some efficiency comparisons

Tests for the goodness-of-fit problem based on sample spacings, i.e., observed distances between successive order statistics, have been used in the literature. We propose a new test based on the number of “small” and “large” spacings. The asymptotic theory under close alternative sequences is also given thus enabling one to calculate the asymptotic relative efficiencies of such tests. A comparison of the new test and other spacings tests is given.     

A goodness-of-fit test in the multinomial legit model based on weighted squared residuals

The asymptotic (normal) distribution of the sum of weighted squared residuals in the multinomial logit model is derived. The performance of a chi-squared test based on the asymptotic normality is discussed using some empirical examples.     

A semiparametric GARCH model for foreign exchange volatility

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily returns data, the semiparametric volatility model outperforms the GJR model as well as the more commonly used GARCH(1,1) model in terms of goodness-of-fit, and forecasting, by correcting overgrowth in volatility.     

A Portfolio Index GARCH model

This paper develops the structure of a parsimonious Portfolio Index (PI) GARCH model. Unlike the conventional approach to Portfolio Index returns, which employs the univariate ARCH class, the PI-GARCH approach incorporates the effects on individual assets, leading to a better understanding of portfolio risk management, and achieves greater accuracy in forecasting Value-at-Risk (VaR) thresholds. For various asymmetric GARCH models, a Portfolio Index Composite News Impact Surface (PI-CNIS) is developed to measure the effects of news on the conditional variances. The paper also investigates the finite sample properties of the PI-GARCH model. The empirical example shows that the asymmetric PI-GARCH-t model outperforms the GJR-t model and the filtered historical simulation with a t distribution in forecasting VaR thresholds.     

Exact confidence sets and goodness-of-fit methods for stable distributions

Usual inference methods for stable distributions are typically based on limit distributions. But asymptotic approximations can easily be unreliable in such cases, for standard regularity conditions may not apply or may hold only weakly. This paper proposes finite-sample tests and confidence sets for tail thickness and asymmetry parameters (α$\alpha$ and β$\beta$) of stable distributions. The confidence sets are built by inverting exact goodness-of-fit tests for hypotheses which assign specific values to these parameters. We propose extensions of the Kolmogorov–Smirnov, Shapiro–Wilk and Filliben criteria, as well as the quantile-based statistics proposed by McCulloch (1986) in order to better capture tail behavior. The suggested criteria compare empirical goodness-of-fit or quantile-based measures with their hypothesized values. Since the distributions involved are quite complex and non-standard, the relevant hypothetical measures are approximated by simulation, and p$p$-values are obtained using Monte Carlo (MC) test techniques. The properties of the proposed procedures are investigated by simulation. In contrast with conventional wisdom, we find reliable results with sample sizes as small as 25. The proposed methodology is applied to daily electricity price data in the US over the period 2001–2006. The results show clearly that heavy kurtosis and asymmetry are prevalent in these series.     

On distances and goodness-of-fit tests for detecting multimodal distributions

Various distances between distributions and between densities are considered. The corresponding goodness-of-fit tests derived from them are examined for their abilities to detect multimodal alternatives. It is found that many well known techniques fail to detect such alternatives, while others do better in terms of their power results. These are mainly the tests derived from the variational metric which are based on spacings and gaps.     

A test for normality

Summary An analogue of the C ramer - von M ism W ²-statistic is given for testing the composite hypothesis of normality with unspecified parameters. Some Monte Carlo percentiles of this statistic are provided. A power comparison with the test developed in [4] shows that the present test is better against certain alternatives.     

GARCH模型族在沪深300中的比较研究

风险测量一直是金融研究领域的热门话题,而如何构建合适的模型来衡量风险自然而然成为众多学者研究的关注点.VaR方法是当今应用最广泛的衡量金融风险的方法之一,其核心又在构建良好的波动率估计模型.GARCH模型族能很好地描述股指波动率呈现的重尾、波动性聚集、杠杆效用等,是当前效果比较好的条件异方差性的模型.本文着重研究基于GARCH模型族(GARCH、EGARCH、PGARCH)在不同分布假定下(高斯分布、t分布、广义误差分布)的表现,从而计算出沪深300的在险价值( VaR),比较分析模型拟合效果,选出适合的模型,对规范国内沪深300的风险管理提供了理论依据.     

Manipulation of the running variable in the regression discontinuity design: A density test

Standard sufficient conditions for identification in the regression discontinuity design are continuity of the conditional expectation of counterfactual outcomes in the running variable. These continuity assumptions may not be plausible if agents are able to manipulate the running variable. This paper develops a test of manipulation related to continuity of the running variable density function. The methodology is applied to popular elections to the House of Representatives, where sorting is neither expected nor found, and to roll call voting in the House, where sorting is both expected and found.     

A maximin test for means

Summary Let X be a normal vector with mean μ and known covariance matrix. This note is concerned with the problem of showing that the test based on X^-1X is maximin most powerful for μ= 0 versus μ belongs to a given ellipsoid and uniformly most powerful for μ = 0 versus μ belongs to a family of ellipsoids.     

Forecasting risk with Markov-switching GARCH models:A large-scale performance study

We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching $\text{GARCH}$ ($\text{MSGARCH}$) models from a risk management perspective. We find that $\text{MSGARCH}$ models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.     

基于高频数据的分类信息混合分布GARCH模型研究

本文提出了基于高频数据的分类信息混合分布GARCH模型．以上证指数的五分钟高频数据作为研究对象,引入修正的混合分布(MMM)模型,将去除了趋势性和序列相关性的不同性质的对数交易量分解为进入市场的正的随机信息流和负的随机信息流两部分,作为分类信息流代理。加入GARCH模型的方差方程中,考察好消息、坏消息对上证指数波动性的影响。     

Recent Theoretical Results for Time Series Models with GARCH Errors

This paper provides a review of some recent theoretical results for time series models with GARCH errors, and is directed towards practitioners. Starting with the simple ARCH model and proceeding to the GARCH model, some results for stationary and nonstationary ARMA–GARCH are summarized. Various new ARCH–type models, including double threshold ARCH and GARCH, ARFIMA–GARCH, CHARMA and vector ARMA–GARCH, are also reviewed.     

BAYESIAN INFERENCE METHODS FOR UNIVARIATE AND MULTIVARIATE GARCH MODELS: A SURVEY

This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non‐parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non‐parametric approach.     

A non-parametric test for industrial specialization

We introduce a non-parametric microdata based test for industrial specialization and apply it to a single urban area. Our test employs establishment densities for specific industries, a population counterfactual, and a new correction for multiple hypothesis testing to determine the statistical significance of specialization across both places and industries. Results highlight patterns of specialization that are extremely varied, with downtown places specializing in a number of service sector industries, while suburban places specialize in both manufacturing and service industries. Business service industries are subject to more specialization than non-business service industries while the manufacturing sector contains the lowest representation of industries with specialized places. Finally, we compare results for specialization with localization and show that both measures contribute to our understanding of industry and place specific agglomerative forces.     

A moment-based test for extreme-value dependence

This paper proposes a new rank-based test of extreme-value dependence. The procedure is based on the first three moments of the bivariate probability integral transform of the underlying copula. It is seen that the test statistic is asymptotically normal and its finite- and large-sample variance are calculated explicitly. Consistent plug-in estimators for the variance are proposed, and a fast algorithm for their computation is given. Although it is shown via counterexamples that no test based on the probability integral transform can be consistent, the proposed procedure achieves good power against common alternatives, both in finite samples and asymptotically.     

A low-dimension portmanteau test for non-linearity

A new test for non-linearity in the conditional mean is proposed using functions of the principal components of regressors. The test extends the non-linearity tests based on Kolmogorov–Gabor polynomials (,  and ), but circumvents problems of high dimensionality, is equivariant to collinearity, and includes exponential functions, so is a portmanteau test with power against a wide range of possible alternatives. A Monte Carlo analysis compares the performance of the test to the optimal infeasible test and to alternative tests. The relative performance of the test is encouraging: the test has the appropriate size and has high power in many situations.