DOI: 10.5061/dryad.18931zd65
title: 5-minute high-frequent data for SSE 50, CSI300, CSI500 and CSI 1000 indices

description: The realized recurrent conditional heteroscedasticity (RealRECH) model
 improves volatility prediction by integrating long short-term memory
 (LSTM), a recurrent neural network unit, into the realized generalized
 autoregressive conditional heteroskedasticity (RealGARCH) model. However,
 at present, there is no literature on the ability of the RealRECH model to
 fit and predict volatility in the Chinese market. In this paper, a study
 is conducted to test the in-sample explainability and out-of-sample
 prediction ability of the RealRECH model for the SSE50, CSI300, CSI500,
 and CSI1000 indices in the Chinese market and to determine whether it
 performs better than the RealGARCH model. The results of the
 in-sample analysis show that the RealRECH model not only
 provides better in-sample interpretability for all four
 indices but also captures the complex dynamics of time
 series volatility that the RealGARCH model cannot capture, such as
 long-term dependence and nonlinearity. The results of out-of-sample
 volatility prediction show that the RealRECH model better predicts
 the volatility of the CSI500 and CSI1000 indices but yields worse
 predictions for the SSE50 and CSI300 indices. Thus, the
 RealRECH model can be used for CSI500 and CSI1000 prediction.
description: The high-frequency data in our research is from the Wind
 Financial Terminal at Southwestern University of Finance and Economics
 (SWUFE). SWUFE has purchased access to the Wind Database and has Wind
 terminals on campus, which allows us to download the data needed for our
 research from these terminals. Currently, there are no free public
 channels for accessing high-frequency data on Chinese stock indices.
 Researchers can either use the Wind Financial Terminal for paid access or
 purchase it through Chinese exchanges or brokers.
description: # 5-minute high-frequent data for SSE 50, CSI300, CSI500 and CSI 1000
 indices The objective of this study is to conduct in-sample analysis and
 out-of-sample prediction of the volatility of four Chinese stock indices
 using the RealGARCH model and the LSTM-RealGARCH(RealRECH) model, and to
 compare their effectiveness in analyzing and predicting the volatility of
 the Chinese stock indices. Therefore, we divided all data into in-sample
 and out-of-sample datasets. The compressed package Data1 includes 2
 folders, which are CSI300 and CSI500, and they both include 2 folders,
 SMC_for_RealGARCH and SMC_for_LSTM_RealGARCH. The compressed package
 Data2&code includes 3 folders: CSI1000, SSE50 and RealRECH_norm, a
 compressed package RealRECH_norm and a file realized_china. The folders
 CSI1000 and SSE50 also include 2 folders, SMC_for_RealGARCH and
 SMC_for_LSTM_RealGARCH.The file realized_china contains the raw data we
 used in this study, which includes 5-minute high-frequency data for 2000
 trading days of four Chinese stock indices. The folders SSE50, CSI1000,
 CSI300 and CSI500 all contain the output datasets of the volatility
 predictions of the four Chinese stock indices using 5-minute
 high-frequency data for 500 trading days. They are the results of the two
 models we used with the SMC algorithm with data annealing and can be
 compared with the actual data. Of course, the folder SMC_for_RealGARCH
 contains the output datasets of the volatility predictions through the
 RealGARCH model and the folder SMC_for_LSTM_RealGARCH contains the output
 datasets of the volatility predictions through the
 LSTM-RealGARCH(RealRECH) model. The compressed package RealRECH_norm and
 the folder RealRECH_norm contain the same contents. In the folder
 RealRECH_norm, the folder in_sample contains the results of in_sample
 analysis of 1500 trading days of 5-minute high-frequency data, in this
 folder, there is a folder utils_bag_in_sample contains the code file lors,
 conditional_variance_realgarch and conditional_variance_lstm_realgarch, a
 folder Conditional Variance Fig contains four conditional variance figs of
 Chinese indices and a folder residual_QQ contains four residual QQ plots
 of these Chinese indices. There are also four code files in the folder
 in_sample, which are code file calculate_lors,
 plot_conditional_variance_omega, plot_residual_QQ and residuals_analysis.
 And the three analysis result files are residual_analysis.xlsx,
 summary_lors.txt and summary_lors.xlsx. The code files lors and
 calculate_lors are using for the modified Lo’s test proposed by Lo in
 1991, and the result files summary_lors.txt and summary_lors.xlsx contain
 the same results of the modified Lo’s test. The code files
 conditional_variance_realgarch and conditional_variance_lstm_realgarch are
 using for in_sample analysis through the RealGARCH model and the
 LSTM-RealGARCH(RealRECH) model, respectively. The code file
 residuals_analysis is for descriptive statistics such as the skewness and
 kurtosis of residuals and the result file residual_analysis.xlsx is the
 result of descriptive statistics. The code files
 plot_conditional_variance_omega and plot_residual_QQ are using for the
 conditional variance figs and residual QQ plots of four Chinese indices,
 respectively. And the folders Conditional Variance Fig and residual_QQ
 contain the results. In the folder RealRECH_norm, the folder out_sample
 contains 2 folders: one step for lstm and one step for realrech, 3 code
 files: forecasted_volatility_predictive_scores, plot_forecasted_volatility
 and residuals_analysis, and 2 result files: residual_analysis and
 performance. The code file residuals_analysis is and the result file
 residual_analysis are the same. The code file plot_forecasted_volatility
 is using for plot generation of the one-step volatility forecasting of the
 four Chinese indices through the two models we used, and the plots are all
 in the folder one step for lstm, and the folder one step for realrech is
 empty. The code file forecasted_volatility_predictive_scores is using for
 out-of-sample volatility predicting and the result file is the file
 performance, it shows 6 different indicators: MSE1, MSE2, MAE1 , MAE2,
 QLIKE and R2LOG. We conducted them to measure the predicting effect of the
 two models. The lower the indicator is, the better the effect will be. In
 the folder RealRECH_norm, the folder SMC_for_LSTM_RealGARCH contains 9
 different code files. The code file LSTM_RealGARCH_DataAnneal,
 LSTM_RealGARCH_LikAnneal and LSTM_RealGARCH_LikDataAnneal are using for
 Sequential Monte Carlo (SMC) sampling with likelihood annealing or data
 annealing methods. The code file LSTM_RealGARCH_llh and
 LSTM_RealGARCH_llh_conditional are using for the calculation of the
 indicator llh we conducted to measure whether the model is better than the
 other on in_sample analysis. The code file LSTM_RealGARCH_logPriors is
 using for the calculation of other in_sample indicators such as beta and
 gamma. The code file LSTM_RealGARCH_one_step_forecast is using for one
 step out-of-sample volatility predicting and can generate the 6
 out-of-sample indicators we conducted: MSE1, MSE2, MAE1 , MAE2, QLIKE and
 R2LOG. The code file LSTM_RealGARCH_residual_analysis is using for
 descriptive statistics and the code file SMC_LSTM_RealGARCH_run combines
 the 3 code files of SMC sampling. All the code files are using for
 analysis through the LSTM-RealGARCH(RealRECH) model. In the folder
 RealRECH_norm, the folder SMC_for_RealGARCH contains 9 different code
 files. The code file RealGARCH_DataAnneal, RealGARCH_LikAnneal and
 RealGARCH_LikDataAnneal are using for Sequential Monte Carlo (SMC)
 sampling with likelihood annealing or data annealing methods. The code
 file RealGARCH_llh and RealGARCH_llh_conditional are using for the
 calculation of the indicator llh we conducted to measure whether the model
 is better than the other on in_sample analysis. The code file
 RealGARCH_logPriors is using for the calculation of other in_sample
 indicators such as beta and gamma. The code file
 RealGARCH_one_step_forecast is using for one step out-of-sample volatility
 predicting and can generate the 6 out-of-sample indicators we conducted:
 MSE1, MSE2, MAE1 , MAE2, QLIKE and R2LOG. The code file
 RealGARCH_residual_analysis is using for descriptive statistics and the
 code file SMC_RealGARCH_run combines the 3 code files of SMC sampling. All
 the code files are using for analysis through the RealGARCH model. The
 code files analysis and analysis_llhs contain the code for in-sample
 analysis, and the code file run_func contains the code for out-of-sample
 predicting. We combined the two in-sample analysis code files with other
 in-sample codes in the folders SMC_for_LSTM_RealGARCH and
 SMC_for_RealGARCH to conduct our in-sample analysis, and we combined the
 out-of-sample analysis code file with other out-of-sample codes in the
 folders SMC_for_LSTM_RealGARCH and SMC_for_RealGARCH to conduct our
 out-of-sample analysis. All analyses were conducted using MATLAB software.