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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Cumulative Frequency Distribution01:04

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A cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Related Experiment Video

Updated: Nov 10, 2025

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Index tracking strategy based on mixed-frequency financial data.

Xiangyu Cui1,2, Xuan Zhang1

  • 1School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

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|April 6, 2021
PubMed
Summary
This summary is machine-generated.

Investment managers can now improve index tracking portfolio accuracy by using mixed-frequency financial data. Our FACTOR-MIDAS-POET model integrates intraday, daily, and monthly economic data for better market return replication.

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Area of Science:

  • Quantitative Finance
  • Financial Econometrics
  • Investment Management

Background:

  • Index tracking portfolios aim to replicate market average returns.
  • Existing methods often rely on financial data of a single, homogenous frequency.
  • This limitation can hinder the precision of portfolio replication.

Purpose of the Study:

  • To introduce a novel methodology for constructing index tracking portfolios.
  • To address the limitations of homogenous frequency data in portfolio construction.
  • To enhance tracking accuracy by incorporating mixed-frequency financial data.

Main Methods:

  • Development of the FACTOR-MIDAS-POET model.
  • Integration of intraday return data, daily risk factor data, and monthly/quarterly macroeconomic data.
  • Application of mixed-frequency data analysis techniques.

Main Results:

  • The proposed FACTOR-MIDAS-POET model effectively utilizes data from multiple frequencies simultaneously.
  • Out-of-sample analysis confirmed significant improvements in tracking accuracy.
  • Demonstrated the capability to capture market dynamics more precisely.

Conclusions:

  • The FACTOR-MIDAS-POET model offers a superior approach to index tracking portfolio construction.
  • Utilizing mixed-frequency data is crucial for enhancing portfolio replication accuracy.
  • This methodology provides a valuable tool for investment managers seeking to optimize market return replication.