High Frequency Financial Time Series Research Articles

Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Yt and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US.

Retracted: Measurement of Economic Fluctuations Based on High-Frequency Financial Time Series

Retracted: Measurement of Economic Fluctuations Based on High-Frequency Financial Time Series

The Estimation of Heavy Tails in Non-linear Models

A generalized student t distribution technique based on estimation of bilinear generalized autoregressive conditional heteroskedasticity (BL-GARCH) model is introduced. The paper investigates from empirical perspective, aspects of the model related to the economic and financial risk management and its impacts on volatility forecasting. The purposive sampling technique was applied to select four banks for the study, namely First Bank of Nigeria (FBN), Guaranty Trust Bank (GTB), United Bank for Africa (UBA) and Zenith Bank (ZEB). The four banks are selected, because their daily stock prices are considered to be more susceptible to volatility than those of other banks within the sampled period (January 2007–May 2022). The data collected were analyzed using MATLAB R2008b Software. The results show that the newly introduced generalized student t distribution is the most general of all the useful distributions applied in the BL-GARCH model parameter estimation. It serves as a general distribution for obtaining empirical characteristics such as volatility clustering, leptokurtosis and leverage effects between returns and conditional variances as well as capturing heavier and lighter tails in high frequency financial time series data.

Measurement of Economic Fluctuations Based on High-Frequency Financial Time Series

In order to improve the analysis and forecasting effect of economic fluctuations, this paper combines the high-frequency financial sequence algorithm to conduct measured analysis of economic fluctuations. Under the continuous jump-diffusion price model, this paper considers the jump part and the continuous part in the process of asset pricing. Moreover, for the jump part, this paper uses the wavelet method to analyze the observation data, and obtains the estimator of the second-order jump covariation difference matrix and its convergence speed. For the continuous part, this paper adopts a two-scale realized volatility model under the continuous price model. In addition, this paper verifies the effect of the intelligent model proposed in this paper through simulation experiments. The simulation data shows that the economic fluctuation analysis system based on high-frequency financial time series proposed in this paper has good economic analysis and economic forecasting effects.

A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

The use of mixed frequency data is now common in many applications, ranging from the analysis of high frequency financial time series to large cross-sections of macroeconomic time series. In this article, we show how state space methods can easily facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward.

Modeling and assessing systematic risk in stock markets in major oil exporting countries

Introduction. This paper aims to assess time variability of beta coefficients (systematic risk) of Capital Asset Pricing Model (CAPM) using data from five key sectors in Saudi Arabia and Kuwait stock markets. Material and methods. To assess time – varying systematic risk we employed symmetric as well as asymmetric conditional volatility specifications to account for skewness and leptkurtosis of high frequency financial time series to better specify conditional higher moments. Results & discussions. The results of the paper support significant evidence of time-varying beta coefficients for all sectors included in the study, in particular the banking sector, and relatively with a lesser degree ,the food, and the service sectors in both countries. For the banking sector in Saudi Arabia, the beta coefficients variability during the sample period estimated between (0.18 to 22.1), and also for Kuwait stock market the beta coefficient of the banking sector variability estimated between (0.16 to 22.1). This result invalidates, at least in the context of the sample country’s banking sectors, the standard application of (CAPM) that assumes constant beta coefficients. Also indicated in the paper, time-varying beta estimates are consistent with a modified version of CAPM prediction that is portfolios with wider range of beta variations expected to yield higher return values and those with lower range of beta variations yield lower returns. Conclusion. In this new context, risk is no longer is a point estimate as implied by the standard CAPM model, but it is a range of values. Our findings also show the size and the range of beta variations are sensitive to skewness and fat tailedness that characterize asset returns distribution.

Local Scaling Analysis with Continuous DFA in High Frequency Financial Time Series

Local Scaling Analysis with Continuous DFA in High Frequency Financial Time Series

A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

The use of mixed frequency data is now common in many applications, ranging from the analysis of high frequency financial time series to large cross-sections of macroeconomic time series. In this article, we show how state space methods can easily facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward.

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

Identifying financial instability conditions using high frequency data

We study an indicator of financial instability based on the computation of the decay rate for the propagation of a given market shock. The rate of variation through time of an initial perturbation of the price process enables us to understand if such a shock will be rapidly absorbed or, on the contrary, it will be amplified by the market. The indicator combines non-linearly volatility, leverage and covariance between leverage and price and is model-free. It provides an early warning indicator of instability for a given high frequency financial time series. A new consistency theorem for the estimator of each component of the proposed indicator is proved. The properties of the indicator are investigated numerically under the CEV model and empirically using tick-by-tick data of the S&P 500 index futures.

Stylized Facts of High-frequency Financial Time Series Data

High-frequency financial time series data have an ability to define market microstructure and are helpful in making rational real-time decisions. These data sets carry unique characteristics and properties which are not available in low-frequency data; with that high-frequency data also create more challenges and opportunities for econometric modelling and financial data analysis. So it is essential to know the features and the facts related to the high-frequency time series data. In this article, we provide the characteristics and stylized facts exhibited by the high-frequency financial time series data of the S&P CNX Nifty futures index. Stylized facts are mostly related to the empirical observed behaviours, distributional properties, autocorrelation function and seasonality of the high-frequency data. Also, it illustrates the importance of stationarity in financial time series analysis. The knowledge of such facts and concepts is helpful to establish better empirical models and to produce reliable forecasts.

Fast and Adaptive Cointegration Based Model for Forecasting High Frequency Financial Time Series

Cointegration is a long-run property of some non-stationary time series where a linear combination of those time series is stationary. This behaviour has been studied in finance because cointegration restrictions often improve forecasting. The vector error correction model (VECM) is a well-known econometric technique that characterises short-run variations of a set of cointegrated time series incorporating long-run relationships as an error correction term. VECM has been broadly used with low frequency time series. We aimed to adapt VECM to be used in finance with high frequency stream data. Cointegration relations change in time and therefore VECM parameters must be updated when new data is available. We studied how forecasting performance is affected when VECM parameters and the length of historical data used change in time. We observed that the number of cointegration relationships varies with the length of historical data used. Moreover, parameters that increased these relationships in time led to better forecasting performance. Our proposal, called an Adaptive VECM (AVECM) is to make a parameters grid search that maximises the number of cointegration relationships in the near past. To ensure the search can be executed fast enough, we used a distributed environment. The methodology was tested using four 10-s frequency time series of the Foreign Exchange market. We compared our proposal with ARIMA and the naive forecast of the random walk model. Numerical experiments showed that on average AVECM performed better than ARIMA and random walk. Additionally, AVECM significantly improved execution times with respect to its serial version.

Applying Generalized Autoregressive Conditional Heteroscedasticity Models to Model Univariate Volatility

This paper aims to model volatility of daily index returns for four Asian markets namely; Kuala Lumpur Composite Index of Malaysia, Jakarta Stock Exchange Composite Index of Indonesia, Straits Times Index of Singapore and the Stock Index of Korea over the period 03/01/2007 – 31/07/2013 excluding the public holidays. We utilized generalized autoregressive conditional heteroskedasticity models as these models are particularly suitable for high frequency financial time series such as the daily stock returns which has a time-varying variance. Unlike the linear structural models, these models are found very useful in explaining the most stylized facts about index returns such as leptokurtosis, volatility clustering and asymmetric or leverage effects. The key results of this study are as follows. Firstly, the conditional variance process is highly persistent in all markets. Secondly, the relationship between the expected risk and the expected return is positive as expected but the relationship is not statistically significant for all markets except Indonesia. For Indonesian market which is found to be more volatile than the other three markets, the estimated coefficient of risk premium appeared to be statistically significant indicating that increased risk leads to a rise in the returns. The risk-premium coefficients for other three markets are although positive but not statistically significant suggesting that increased risk does not necessarily produce higher return for those three markets. Finally, the asymmetric effects is exist in all cases signifying that negative shock produces higher impact on future volatility than the positive shock of the same magnitude.

Jump detection with wavelets for high-frequency financial time series

This paper introduces a new nonparametric test to identify jump arrival times in high frequency financial time series data. The asymptotic distribution of the test is derived. We demonstrate that the test is robust for different specifications of price processes and the presence of the microstructure noise. A Monte Carlo simulation is conducted which shows that the test has good size and power. Further, we examine the multi-scale jump dynamics in US equity markets. The main findings are as follows. First, the jump dynamics of equities are sensitive to data sampling frequency with significant underestimation of jump intensities at lower frequencies. Second, although arrival densities of positive jumps and negative jumps are symmetric across different time scales, the magnitude of jumps is distributed asymmetrically at high frequencies. Third, only 20% of jumps occur in the trading session from 9:30 AM to 4:00 PM, suggesting that illiquidity during after-hours trading is a strong determinant of jumps.

Testing for Non-linear and Time Irreversible Probabilistic Structure in High Frequency Financial Time Series Data

Summary We present three non-parametric trispectrum tests that can establish whether the spectral decomposition of kurtosis of high frequency financial asset price time series is consistent with the assumptions of Gaussianity, linearity and time reversiblility. The detection of non-linear and time irreversible probabilistic structure has important implications for the choice and implementation of a range of models of the evolution of asset prices, including Black–Scholes–Merton option pricing model, auto-regressive conditional heteroscedastic or generalized auto-regressive conditional heteroscedastic and stochastic volatility models. We apply the tests to a selection of high frequency Australian stocks.

Some Statistical Models vs. Models Based on SC for High Frequency Financial Time Series Applied to Bonds of Commercial Banks

In neural networks modeling approach, a non-linear model is estimated based on machine learning methods. The study discusses, analytically and numerically demonstrates the quality and interpretability of the obtained prediction accuracy results from prediction models based on advanced statistical methods and models based on neural networks (intelligent methods). Both proposed approaches are applied to the financial time series of s of VUB bond prices. We found that it is possible to achieve significant risk reduction in managerial decision-making by applying intelligent forecasting models based on the latest information technologies. In a comparative study is shown, that both presented modeling approaches are able to model and predict high frequency data with reasonable accuracy, but the neural network approach is more effective.

Modeling Market Volatility with Mixed Exponential Power Asymmetric Conditional Heteroskedasticity: An Application to Shanghai Stock Exchange Composite Index Daily Returns

We utilize the mixed exponential power asymmetric GARCH model where each component exhibits asymmetric conditional heteroskedasticity to model Shanghai Stock Exchange Composite Index daily returns. Thanks to extra component-specific shape parameters, it can better capture the tail behavior and match the stylized facts of high frequency financial time series precisely and parsimoniously. The application to SSE Composite Index returns illustrates all the conditional variance processes become stationary. Good nature of the performance both in-sample and out-of-sample as well as the flexibility of the maximum likelihood estimation makes it more attractive in the applications of risk management of portfolio and VaR calculation.

Option pricing from wavelet-filtered financial series

We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most (≃99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.

Discrete sine transform for multi-scale realized volatility measures§

In this study we present a new realized volatility estimator based on a combination of the multi-scale regression and discrete sine transform (DST) approaches. Multi-scale estimators similar to that recently proposed by Zhang (2006) can, in fact, be constructed within a simple regression-based approach by exploiting the linear relation existing between the market microstructure bias and the realized volatilities computed at different frequencies. We show how such a powerful multi-scale regression approach can also be applied in the context of the Zhou [Nonlinear Modelling of High Frequency Financial Time Series, pp. 109–123, 1998] or DST orthogonalization of the observed tick-by-tick returns. Providing a natural orthonormal basis decomposition of observed returns, the DST permits the optimal disentanglement of the volatility signal of the underlying price process from the market microstructure noise. The robustness of the DST approach with respect to the more general dependent structure of the microstructure noise is also shown analytically. The combination of the multi-scale regression approach with DST gives a multi-scale DST realized volatility estimator similar in efficiency to the optimal Cramer–Rao bounds and robust against a wide class of noise contamination and model misspecification. Monte Carlo simulations based on realistic models for price dynamics and market microstructure effects show the superiority of DST estimators over alternative volatility proxies for a wide range of noise-to-signal ratios and different types of noise contamination. Empirical analysis based on six years of tick-by-tick data for the S&P 500 index future, FIB 30, and 30 year U.S. Treasury Bond future confirms the accuracy and robustness of DST estimators for different types of real data. §Earlier versions of this paper were circulated under the title ‘A discrete sine transform approach for realized volatility measurement’.

Long Memory and Fractional Integration in High Frequency Financial Time Series

This paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour. This holds for all four series examined, namely Open, High, Low and Last observations for the British pound/US dollar spot exchange rate.