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相关概念视频

Expected Value01:15

Expected Value

3.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Econometric Views (EViews)01:29

Econometric Views (EViews)

111
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
111
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Regression Analysis01:11

Regression Analysis

5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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相关实验视频

Updated: Jun 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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没有因子的截面:预期回报的字符串模型.

Walter Distaso1, Antonio Mele2, Grigory Vilkov3

  • 1Imperial College, South Kensington Campus, London SW7 2AZ, United Kingdom.

Quantitative finance
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的资产定价模型,其中回报是由资产相关性驱动的,而不仅仅是常见因素. 大型股票充当对冲,降低风险并降低相关性溢价.

关键词:
对于仲裁定价的定价.大股票的大股票.相关性溢价是指相关性溢价.申报表的横截面.隐含的相关性 隐含的相关性相关性风险保费 相关性风险保费字符串模型 字符串模型

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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An R-Based Landscape Validation of a Competing Risk Model
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相关实验视频

Last Updated: Jun 1, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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科学领域:

  • 量化金融 量化金融
  • 资产定价理论 资产定价理论
  • 金融计量经济学 金融计量经济学

背景情况:

  • 传统的资产定价模型通常依赖于共同的因素来解释预期的回报.
  • 对捕捉复杂资产间关系的替代模型的需求正在增长.
  • 现有的模型可能无法充分考虑资产回报的相互联系.

研究的目的:

  • 开发一种基于"串"概念的新型资产定价模型,通过相关性将资产回报联系起来.
  • 调查细分风险和相关性溢价在资产定价中的作用.
  • 在这个新的框架内,确定大型鱼类的独特特性.

主要方法:

  • 制定一种新的资产定价模型,其中包含资产回报的"链".
  • 应用无仲裁限制来定义基于跨资产风险的预期回报.
  • 对大型股票及其对冲性质的模型预测的分析.

主要成果:

  • 拟议的"字符串"模型假定预期回报受到资产对所有其他资产回报的暴露的影响,称为相关性溢价.
  • 大型股票在市场下滑期间表现出更高的连接性.
  • 这些大型股票充当相关性对冲,对相关性溢价做出负面贡献.

结论:

  • "串式"模型为资产定价提供了一个新的视角,强调资产之间的相关性.
  • 大型股票作为对冲起着至关重要的作用,可能降低投资组合风险.
  • 该模型与已建立的线性因子模型相比,显示出具有竞争力的性能.