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非参数时变系数模型中的贝叶斯带宽估计AI 解读

Bandwidth plays an important role in determining the performance of nonparametric estimators, such as the local constant estimator. In this article, we propose a Bayesian approach to bandwidth estimation for local constant estimators of time-varying coefficients in time series models. We establish a large sample theory for the proposed bandwidth estimator and Bayesian estimators of the unknown parameters involved in the error density. A Monte Carlo simulation study shows…

基于非对称拉普拉斯分布的半参数VaR与ES预测AI 解读

Value at Risk (VaR) forecasts can be produced from conditional autoregressive VaR models, estimated using quantile regression. Quantile modeling avoids a distributional assumption, and allows the dynamics of the quantiles to differ for each probability level. However, by focusing on a quantile, these models provide no information regarding expected shortfall (ES), which is the expectation of the exceedances beyond the quantile. We introduce a method for predicting ES…

信用风险计算中的宏观因素：混合治愈模型中的时变协变量AI 解读

The prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the…

结构变化下滤波序列的相关系数恒定性检验AI 解读

Summary The paper proposes a test for constant correlations that allow for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is because incorrect standardization of the series transfers to the sample correlations onto which the tests are built. Correcting for variance breaks at unknown time will have an…

系统重要性金融机构的尾部事件驱动网络AI 解读

The interdependence, dynamics and riskiness of financial institutions are the key features frequently tackled in financial econometrics. We propose a Tail Event driven Network Quantile Regression (TENQR) model which addresses these three aspects. More precisely, our framework captures the risk propagation and dynamics in terms of a panel quantile autoregression involving network effects that are quantified through a time-varying adjacency matrix. To reflect the risk content…

卡特尔效应起止时间的判定AI 解读

Summary The begin and end dates of cartels are often ambiguous, despite competition authorities stating them with precision. The legally established infringement period(s) from documentary evidence need not coincide with the period(s) of actual cartel effects. In this paper, we show that misdating cartel effects leads to a (weak) overestimation of but‐for prices and an underestimation of overcharges. Total overcharges based on comparing but‐for prices to actual prices are a…

尾部贪心的自下而上数据分解与快速多变点检测AI 解读

This article proposes a “tail-greedy”, bottom-up transform for one-dimensional data, which results in a nonlinear but conditionally orthonormal, multiscale decomposition of the data with respect to an adaptively chosen unbalanced Haar wavelet basis. The “tail-greediness” of the decomposition algorithm, whereby multiple greedy steps are taken in a single pass through the data, both enables fast computation and makes the algorithm applicable in the problem of consistent…

用自回归条件Fréchet模型刻画极大值AI 解读

This paper introduces a novel dynamic generalized extreme value (GEV) framework for modeling the time-varying behavior of maxima in financial time series. Specifically, an autoregressive conditional Fréchet (AcF) model is proposed in which the maxima are modeled by a Fréchet distribution with time-varying scale parameter (volatility) and shape parameter (tail index) conditioned on past information. The AcF provides a direct and accurate modeling of the time-varying behavior…

考虑可靠性的已实现协方差建模与预测AI 解读

We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less)…

断点数量未知的大维因子模型估计AI 解读

In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the…

高频股票价格变动的动态离散 Copula 模型AI 解读

Summary We develop a dynamic model for the intraday dependence between discrete stock price changes. The conditional copula mass function for the integer tick‐size price changes has time‐varying parameters that are driven by the score of the predictive likelihood function. The marginal distributions are Skellam and also have score‐driven time‐varying parameters. We show that the integration steps in the copula mass function for large dimensions can be accurately approximated…

利用状态区段的可逆跳跃 MCMC 发现恶意软件家族AI 解读

Malware is computer software that has either been designed or modified with malicious intent. Hundreds of thousands of new malware threats appear on the internet each day. This is made possible through reuse of known exploits in computer systems that have not been fully eradicated; existing pieces of malware can be trivially modified and combined to create new malware, which is unknown to anti-virus programs. Finding new software with similarities to known malware is…

大量类别多项数据的变点检测AI 解读

We consider a sequence of multinomial data for which the probabilities associated with the categories are subject to abrupt changes of unknown magnitudes at unknown locations. When the number of categories is comparable to or even larger than the number of subjects allocated to these categories, conventional methods such as the classical Pearson’s chi-squared test and the deviance test may not work well. Motivated by high-dimensional homogeneity tests, we propose a novel…

反向嵌套描述子的渐近理论及干细胞分化推断AI 解读

For sequences of random backward nested subspaces as occur, say, in dimension reduction for manifold or stratified space valued data, asymptotic results are derived. In fact, we formulate our results more generally for backward nested families of descriptors (BNFD). Under rather general conditions, asymptotic strong consistency holds. Under additional, still rather general hypotheses, among them existence of a.s. local twice differentiable charts, asymptotic joint normality…

确定性季节性与季节均值变化的稳健检验AI 解读

We develop tests for the presence of deterministic seasonal behaviour and seasonal mean shifts in a seasonally observed univariate time series. These tests are designed to be asymptotically robust to the order of integration of the series at both the zero and seasonal frequencies. Motivated by the approach of Hylleberg, Engle, Granger and Yoo, we base our approach on linear filters of the data that remove any potential unit roots at the frequencies not associated with the…

内生随机单位根模型中的 IV 与 GMM 推断AI 解读

Lieberman and Phillips (2017; LP) introduced a multivariate stochastic unit root (STUR) model, which allows for random, time varying local departures from a unit root (UR) model, where nonlinear least squares (NLLS) may be used for estimation and inference on the STUR coefficient. In a structural version of this model where the driver variables of the STUR coefficient are endogenous, the NLLS estimate of the STUR parameter is inconsistent, as are the corresponding estimates…

非平稳 AR(1) 模型中的结构变化AI 解读

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k 0 . Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$ , where I {·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T , and the other is equal to…

β-混合随机向量极端依赖的结构突变检验AI 解读

We derive a structural break test for extremal dependence in |$\beta$|-mixing, possibly high-dimensional random vectors with either asymptotically dependent or asymptotically independent components. Existing tests require serially independent observations with asymptotically dependent components. To avoid estimating a long-run variance, we use self-normalization, which obviates the need to estimate the coefficient of tail dependence when components are asymptotically…

高维时间序列的多变点检测与因子分析AI 解读

We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most flexible definition of piecewise stationarity, and estimate the number and locations of change-points consistently as well as identifying whether they originate in the common or idiosyncratic components. Through the use of wavelets, we transform the problem of change-point detection in the…

高频金融数据的随机尾指数模型与贝叶斯分析AI 解读

This paper proposes a new dynamic model called Stochastic Tail Index (STI) model to analyze time-varying tail index for financial asset using high frequency return data. Bayesian tools are developed to estimate the model, make related inferences, and perform model selection. To construct efficient posterior sampler for the STI model by an approximation approach, a new algorithm called ALSO (Auxiliary Least Squares Optimization) is introduced, which can quickly make…

半参数多元模型驱动的投资组合 VaR 估计风险AI 解读

Joint estimation of market and estimation risks in portfolios is investigated, when the individual returns follow a semi-parametric multivariate dynamic model and the asset composition is time-varying. Under ellipticity of the conditional distribution, asymptotic theory for the estimation of the conditional Value-at-Risk (VaR) is developed. An alternative method – the Filtered Historical Simulation – which does not rely on ellipticity, is also studied. Asymptotic confidence…

移动健康干预中的时变因果效应调节评估AI 解读

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-individual characteristics, time-varying…

高维分位数回归中变点的预言式估计AI 解读

In this paper, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop $\ell_1$-penalized estimators of both regression coefficients and the threshold parameter. Our penalized estimators not only select covariates but also discriminate between a model with homogeneous sparsity and a model with a change point. As a result, it is not necessary to know or pretest whether the change point is…

借助最优控制与加性扰动估计病态且不确定的动力系统AI 解读

Ordinary differential equations (ODE) are routinely calibrated on real data for estimating unknown parameters or for reverse-engineering. Nevertheless, standard statistical techniques can give disappointing results because of the complex relationship between parameters and states, which makes the corresponding estimation problem ill-posed. Moreover, ODE are mechanistic models that are prone to modeling errors, whose influences on inference are often neglected during…