Checkmate9949的博客

title: “Value at Risk estimation using GARCH model”author: “Ionas Kelepouris & Dimos Kelepouris”date: “July 6, 2019”output:html_document:fig_height: 7fig_width: 10highlight: tangotoc: yesknitr::opts_chunk$set(echo = TRUE , warning = FALSE , e.

一、描述创建单变量GARCH二、用法ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1), submodel = NULL, external.regressors = NULL, variance.targeting = FALSE), mean.model = list(armaOrder = c(1, 1), include.mean = TRUE, archm = FALSE, archpo

Garch和Arch模型的简单应用1、Problem with VarianceAutoregressive models can be developed for univariate time series data that is stationary (AR), has a trend (ARIMA), and has a seasonal component (SARIMA).一元时间序列数据的自相关模型：平稳AR，趋势ARIMA，季节成分SARIMAOne aspect of a uni

继上节SPA继续介绍StepM和MSC检验【算法检验】SPA_Checkmate9949的博客-优快云博客一、概念The multiple comparison procedures all allow for examining aspects of superior predictive ability.用于检验更优的预测能力There are three available:SPA - The test of Superior Predictive Ability, also known as

一、概念The multiple comparison procedures all allow for examining aspects of superior predictive ability.用于检验更优的预测能力There are three available:SPA - The test of Superior Predictive Ability, also known as the Reality Check (and accessible as RealityChec

1、算法思路Step 1: Calculate the daily cumulative sentiment score using the extracted news headlines related to crude oil price.Step 2: Decompose the target variable (i.e., the daily logarithmic returns and 7-day volatility) into different intrinsic modes u

1. 算法介绍1.1 参考文献复旦大学邱锡鹏老师课题组的研究论文《How to Fine-Tune BERT for Text Classification?》。论文: https://arxiv.org/pdf/1905.05583.pdf1.2 论文思路旨在文本分类任务上探索不同的BERT微调方法并提供一种通用的BERT微调解决方法。这篇论文从三种路线进行了探索： (1) BERT自身的微调策略，包括长文本处理、学习率、不同层的选择等方法； (2) 目标任务内、领域内

1. 爬取Oil price2. 处理文本（1）Tokenization 分词中文分词:https://github.com/fxsjy/jieba