摘要翻译:
广义自回归条件异方差(GARCH)模型是研究(条件)异方差的一个标准模型,即过程的方差随时间变化的现象,它在经济学和金融学中具有特别重要的意义。GARCH模型通常用准最大似然(QML)方法估计,该方法在温和的统计假设下工作。在这里,我们提出了一种有限样本方法,称为范围,以构造围绕QML估计的无分布置信区域,这些置信区域具有精确的复盖概率,尽管没有关于矩的额外假设。ScoPe是受最近发展的符号扰动和(SPS)方法的启发,但它不能应用于GARCH情况。ScoPe通过使用随机置换的残差扰动得分函数来工作。这将产生可供选择的样本,从而得到精确的置信区域。在模拟数据和股票市场数据上进行了实验,并与渐近理论和bootstrap方法进行了比较。
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英文标题:
《Score Permutation Based Finite Sample Inference for Generalized
AutoRegressive Conditional Heteroskedasticity (GARCH) Models》
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作者:
Bal\'azs Csan\'ad Cs\'aji
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最新提交年份:
2018
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分类信息:
一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Mathematics 数学
二级分类:Dynamical Systems 动力系统
分类描述:Dynamics of differential equations and flows, mechanics, classical few-body problems, iterations, complex dynamics, delayed differential equations
微分方程和流动的动力学,力学,经典的少体问题,迭代,复杂动力学,延迟微分方程
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一级分类:Quantitative Finance 数量金融学
二级分类:Statistical Finance 统计金融
分类描述:Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要:
A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here, we suggest a finite sample approach, called ScoPe, to construct distribution-free confidence regions around the QML estimate, which have exact coverage probabilities, despite no additional assumptions about moments are made. ScoPe is inspired by the recently developed Sign-Perturbed Sums (SPS) method, which however cannot be applied in the GARCH case. ScoPe works by perturbing the score function using randomly permuted residuals. This produces alternative samples which lead to exact confidence regions. Experiments on simulated and stock market data are also presented, and ScoPe is compared with the asymptotic theory and bootstrap approaches.
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PDF链接:
https://arxiv.org/pdf/1807.08390