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Jingdong book

统计学精品译丛：金融数据分析导论（基于R语言）

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Jingdong book

统计学精品译丛：金融数据分析导论（基于R语言）

Name: 统计学精品译丛：金融数据分析导论（基于R语言）
Brand: Jingdong book
SKU: 3110195761
Price: 36.25 USD
Availability: OutOfStock

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《统计学精品译丛：金融数据分析导论（基于R语言）》向读者展示了可视化金融数据的基本概念，共有7章内容，涉及R软件、线性时间序列分析、资产波动率的不同计算方法、波动率模型在金融中的实际应用、高频金融数据的处理、用于风险管理的量化方法等.贯通全书，作者都是通过R图形以可视化的形式把讨论主题展现给读者，并以两个详细案例展示了金融中统计学的应用。
《统计学精品译丛：金融数据分析导论（基于R语言）》是高年级本科生或研究生阶段学习时间序列和商务统计学的优秀教材，对于希望进一步加强对金融数据和当今金融市场理解的研究人员以及商业、金融和经济领域的从业者。

Catalogue

前言
第1章金融数据及其特征
1.1 资产收益率
1.2 债券收益和价格
1.3 隐含波动率
1.4 R软件包及其演示
1.4.1 R软件包的安装
1.4.2 Quantmod软件包
1.4.3 R的基本命令
1.5 金融数据的例子
1.6 收益率的分布性质
1.7 金融数据的可视化
1.8 一些统计分布
1.8.1 正态分布
1.8.2 对数正态分布
1.8.3 稳态分布
1.8.4 正态分布的尺度混合
1.8.5 多元收益率
习题
参考文献

第2章金融时间序列的线性模型
2.1 平稳性
2.2 相关系数和自相关函数
2.3 白噪声和线性时间序列
2.4 简单自回归模型
2.4.1 AR模型的性质
2.4.2 实践中AR模型的识别
2.4.3 拟合优度
2.4.4 预测
2.5 简单移动平均模型
2.5.1 MA模型的性质
2.5.2 MA模型定阶
2.5.3 模型估计
2.5.4 用MA模型预测
2.6 简单ARMA模型
2.6.1 ARMA（1,1）模型的性质
2.6.2 一般ARMA模型
2.6.3 ARMA模型的识别
2.6.4 用ARMA模型进行预测
2.6.5 ARMA模型的三种表示方式
2.7 单位根非平稳性
2.7.1 随机游动
2.7.2 带漂移的随机游动
2.7.3 趋势平稳时间序列
2.7.4 一般单位根非平稳模型
2.7.5 单位根检验
2.8 指数平滑
2.9 季节模型
2.9.1 季节差分
2.9.2 多重季节模型
2.9.3 季节哑变量
2.10 带时间序列误差的回归模型
2.11 长记忆模型
2.12 模型比较和平均
2.12.1 样本内比较
2.12.2 样本外比较
2.12.3 模型平均
习题
参考文献

第3章线性时间序列分析案例学习
3.1 每周普通汽油价格
3.1.1 纯时间序列模型
3.1.2 原油价格的使用
3.1.3 应用滞后期的原油价格数据
3.1.4 样本外预测
3.2 全球温度异常值
3.2.1 单位根平稳
3.2.2 趋势非平稳
3.2.3 模型比较
3.2.4 长期预测
3.2.5 讨论
3.3 美国月失业率
3.3.1 单变量时间序列模型
3.3.2 一个替代模型
3.3.3 模型比较
3.3.4 使用首次申请失业救济金人数
3.3.5 模型比较
习题
参考文献

第4章资产波动率及其模型
4.1 波动率的特征
4.2 模型的结构
4.3 模型的建立
4.4 ARCH效应的检验
4.5 ARCH模型
4.5.1 ARCH模型的性质
4.5.2 ARCH模型的优点与缺点
4.5.3 ARCH模型的建立
4.5.4 例子
4.6 GARCH模型
4.6.1 实例说明
4.6.2 预测的评估
4.6.3 两步估计方法
4.7 求和GARCH模型
4.8 GARCH-M模型
4.9 指数GARCH模型
4.9.1 第一个示例
4.9.2 模型的另一种形式
4.9.3 第二个示例
4.9.4 用EGARCH模型进行预测
4.10 门限GARCH模型
4.11 APARCH模型
4.12 非对称GARCH模型
4.13 随机波动率模型
4.14 长记忆随机波动率模型
4.15 另一种方法
4.15.1 高频数据的应用
4.15.2 应用日开盘价、最高价、最低价和收盘价
习题
参考文献

第5章波动率模型的应用
5.1 GARCH波动率期限结构
5.2 期权定价和对冲
5.3 随时间变化的协方差和β值
5.4 最小方差投资组合
5.5 预测
习题
参考文献

第6章高频金融数据
6.1 非同步交易
6.2交易价格的买卖报价差
6.3交易数据的经验特征
6.4价格变化模型
6.4.1 顺序概率值模型
6.4.2 分解模型
6.5 持续期模型
6.5.1 日模式的成分
6.5.2 ACD模型
6.5.3 估计
6.6 实际波动率
6.6.1 处理市场微结构噪声
6.6.2 讨论
附录A概率分布概览
附录B危险率函数
习题
参考文献

第7章极值理论、分位数估计与VaR
7.1 风险测度和一致性
7.1.1 风险值
7.1.2 期望损失
7.2 计算风险度量的注记
7.3 风险度量制
7.3.1 讨论
7.3.2 多个头寸
7.4 VaR计算的计量经济学方法
7.5 分位数估计
7.5.1 分位数与次序统计量
7.5.2 分位数回归
7.6 极值理论
7.6.1 极值理论概览
7.6.2 经验估计
7.6.3 股票收益率的应用
7.7 极值在VaR中的应用
7.7.1 讨论
7.7.2 多期VaR
7.7.3 收益率水平
7.8 超出门限的峰值
7.8.1 统计理论
7.8.2 超额均值函数
7.8.3 估计
7.8.4 另外一种参数化方法
7.9 平稳损失过程
习题
参考文献
索引

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