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Datasets for testing the performances of jump diffusion models.

Weijun Xu1, Guifang Liu1, Hongyi Li2

  • 1School of Business Administration, South China University of Technology, Guangzhou 510640, China.

Data in Brief
|December 17, 2016
PubMed
Summary
This summary is machine-generated.

This study provides financial datasets of daily percentage returns, derived from closing prices. These datasets serve as valuable benchmarks for evaluating the performance of novel jump diffusion models.

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Area of Science:

  • Quantitative Finance
  • Financial Econometrics

Background:

  • Financial markets exhibit complex dynamics, including price jumps and diffusion.
  • Existing models may not fully capture the intricacies of asset price movements.

Purpose of the Study:

  • To present datasets of continuous composite daily percentage returns.
  • To provide benchmarks for assessing novel jump diffusion models.

Main Methods:

  • Data compilation from daily closing prices.
  • Statistical property analysis of the return datasets.
  • Splitting datasets into in-sample and out-of-sample for robust evaluation.

Main Results:

  • Datasets characterized by continuous composite daily percentage return values.
  • Statistical properties of the financial return data are described.
  • Clearly defined in-sample and out-of-sample data partitions.

Conclusions:

  • The provided datasets are suitable for validating jump diffusion models.
  • These datasets can aid researchers in assessing model performance.
  • Facilitates advancements in financial modeling and risk management.