Modeling high frequency stock market data by using stochastic models

Mariani, Maria C.; Tweneboah, Osei K.

doi:10.1080/07362994.2021.1942046

Cited by 5 publications

(12 citation statements)

References 18 publications

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“…The solution to this system was obtained in [5] with a clear step-by-step proof, and hence the proof is omitted in this work. The solution is thus given as…”

Section: Modelmentioning

confidence: 99%

“…Since it was proposed in the 1930s, the Ornstein-Uhlenbeck model has been used in many areas of application, including, but not limited to, fields such as health care [1], nanotechnology/thermodynamics [2], geophysics [3] and finance [4][5][6]. Unlike its original proposition, which involved a Brownian motion as its background driving process, there have been many extensions or modifications to it in order to truly capture the behavior of data sets, which otherwise could not be modeled rightly with Brownian motions [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike its original proposition, which involved a Brownian motion as its background driving process, there have been many extensions or modifications to it in order to truly capture the behavior of data sets, which otherwise could not be modeled rightly with Brownian motions [7,8]. Empirical results have shown evidence of non-Brownian behavior in many real-world complex systems [5,9,10]. In fact, according to [9], statistics of the Lévy type is a ubiquitous phenomenon observed in a wide variety of areas, including physics, seismology, engineering to mention a few.…”

Section: Introductionmentioning

confidence: 99%

“…Being able to understand and predict future behaviors of stock markets will, without any doubt, be beneficial to individual investors and economic policymakers. It is for this reason that there is always ongoing research into improving the forecasting models of financial stock markets [3,5,10,11]. It is also of little surprise that there arise some dependencies within stock markets.…”

Section: Introductionmentioning

confidence: 99%

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Data Analysis Using a Coupled System of Ornstein–Uhlenbeck Equations Driven by Lévy Processes

Mariani

Asante

Kubin

et al. 2022

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In this work, we have analyzed data sets from various fields using a coupled Ornstein–Uhlenbeck (OU) system of equations driven by Lévy processes. The Ornstein–Uhlenbeck model is well known for its ability to capture stochastic behaviors when used as a predictive model. There’s empirical evidence showing that there exist dependencies or correlations between events; thus, we may be able to model them together. Here we show such correlation between data from finance, geophysics and health as well as show the predictive performance when they are modeled with a coupled Ornstein–Uhlenbeck system of equations. The results show that the solution to the stochastic system provides a good fit to the data sets analyzed. In addition by comparing the results obtained when the BDLP is a Γ(a,b) process or an IG(a,b) process, we are able to deduce the best choice out of the two to model our data sets.

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“…The solution to this system was obtained in [5] with a clear step-by-step proof, and hence the proof is omitted in this work. The solution is thus given as…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Analysis Using a Coupled System of Ornstein–Uhlenbeck Equations Driven by Lévy Processes

Mariani

Asante

Kubin

et al. 2022

Axioms

Self Cite

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“…Along the way, stochastic methods have been used extensively for index and volatility estimation (Liu et al 2022;Ammar et al 2020;Bershova and Rakhlin 2013;Andersen et al 1999). Stochastic methods have also been consistently studied as a measurement tool for intraday trading (Zhang and Wang 2023;Mariani and Tweneboah 2022;Bekierman and Gribisch 2021;Fu et al 2018). However, insufficient studies have linked the index estimation approach to actual trading while considering ways to eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Paik,

Choi,

Vaquero

2024

JRFM

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Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

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Testing of Portfolio Optimization by Timor-Leste Portfolio Investment Strategy on the Stock Market

Anuno,

Madaleno,

Vieira

2024

JRFM

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An efficient and effective portfolio provides maximum return potential with minimum risk by choosing an optimal balance among assets. Therefore, the objective of this study is to analyze the performance of optimized portfolios in minimizing risk and achieving maximum returns in the dynamics of Timor-Leste’s equity portfolio in the international capital market for the period from January 2006 to December 2019. The empirical findings of this study indicate that the correlation matrix showed that JPM has a very strong positive correlation with one of the twenty assets, namely BAC (0.80). Moreover, the optimal portfolio of the twenty stocks exceeding 10% consists of four consecutive stocks, namely DGE.L (10.69%), NSRGY (10.37%), JPM (10.04%), and T (10.03%). In addition, the minimum portfolio consists of two stocks with a minimum variance of more than 10%, namely SAP.DE (11.20%) and DGE.L (10.39%). The evaluation of the optimal portfolio using Markowitz parameters also showed that the highest expected return and the lowest risk were 1.22% and 3.12%, respectively.

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Modeling high frequency stock market data by using stochastic models

Cited by 5 publications

References 18 publications

Data Analysis Using a Coupled System of Ornstein–Uhlenbeck Equations Driven by Lévy Processes

Data Analysis Using a Coupled System of Ornstein–Uhlenbeck Equations Driven by Lévy Processes

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Testing of Portfolio Optimization by Timor-Leste Portfolio Investment Strategy on the Stock Market

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