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Artykuły w czasopismach na temat „Dynamic Financial Market Model”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Liu, Yirou. "Positive Affect of Financial Derivatives onThe Stock Market." Advances in Economics, Management and Political Sciences 7, no. 1 (2023): 163–70. http://dx.doi.org/10.54254/2754-1169/7/20230229.

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The derivatives market is widely recognized in financial markets, and it has developed rapidly, but there is little evidence that it contributed to the development of financial and economic markets. This article investigates the dynamic relationship between the Indian derivatives market and the stock market to determine how it affects market pricing. This paper use the DCC-garCH model to examine the dynamics of India from the third quarter of 2018 to 2022, I find that the financial derivatives market is more financially contagious than the equity market. Therefore, I can analyse that the devel

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Bassi, Francesca. "Longitudinal models for dynamic segmentation in financial markets." International Journal of Bank Marketing 35, no. 3 (2017): 431–46. http://dx.doi.org/10.1108/ijbm-05-2016-0068.

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Purpose Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, custo

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Miahkyi, Mykhailo. "Dynamic model of currency exchange based on investor behavior." Information, Computing and Intelligent systems, no. 5 (December 26, 2024): 137–49. https://doi.org/10.20535/2786-8729.5.2024.316456.

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In the modern financial environment, cryptocurrencies have gained significant popularity, becoming an important element of the global economy and financial markets. The dynamic development of blockchain technologies and decentralized financial instruments fosters increased interest from both private investors and institutional players. However, the high volatility of cryptocurrencies and the complexity of the mechanisms behind their price formation necessitate a detailed study of these processes. This paper models cryptocurrency exchange operations, analyzing price formation influenced by buyi

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4

Enow, Samuel Tabot. "Investigating mean reversion in financial markets using Hurst Model." International Journal of Research in Business and Social Science (2147- 4478) 12, no. 6 (2023): 197–201. http://dx.doi.org/10.20525/ijrbs.v12i6.2664.

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In the dynamic world of financial markets, the prices of assets can exhibit dramatic fluctuations, sometimes soaring to dizzying heights or plummeting to alarming lows. However, amidst the chaos, a fascinating phenomenon emerges: a tendency for prices to revert back to their long-term average or mean level. This concept known as mean reversion has intrigued traders, investors, and researchers for decades. Understanding mean reversion provides valuable insights into market dynamics, investor behavior, and the potential for profitable trading strategies. The aim of this study was to empirically

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5

Chen, Fan. "Deep Neural Network Model Forecasting for Financial and Economic Market." Journal of Mathematics 2022 (March 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/8146555.

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Recently, the Internet financial market has developed rapidly both at home and abroad. Simultaneously, its study has also become the focus of academic circles. The financial markets have higher liquidity and volatility as compared to traditional financial markets. In view of the Internet financial market dynamic (volume and daily trading), it is proposed based on a deep neural network for fusion level time series prediction model. First, the proposed model processes the input of characteristic variables of multiple series (market macrodynamic series and multiseed series) and uses an attention

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6

Ji, Xiuping, Sujuan Wang, Honggen Xiao, Naipeng Bu, and Xiaonan Lin. "Contagion Effect of Financial Markets in Crisis: An Analysis Based on the DCC–MGARCH Model." Mathematics 10, no. 11 (2022): 1819. http://dx.doi.org/10.3390/math10111819.

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Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC–MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic

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Ruan, Lei. "Research on Sustainable Development of the Stock Market Based on VIX Index." Sustainability 10, no. 11 (2018): 4113. http://dx.doi.org/10.3390/su10114113.

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The frequent occurrence of financial crises has made the dynamic linkage between international financial markets an important research topic. In the past, scholars mostly studied the correlation between financial markets directly, however ignored the impact of exogenous financial variables on financial markets. The stock market is an important part of the financial market and plays an important role in the overall economy. Information asymmetry is common and has a certain degree of impact on investors’ returns. However, many scholars believe that the problem of information asymmetry in China h

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8

Liu, Fengshuo. "Risk Management in Derivatives Markets: Integrating Advanced Hedging Strategies with Empirical Analysis." SHS Web of Conferences 188 (2024): 01008. http://dx.doi.org/10.1051/shsconf/202418801008.

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This study provides a comprehensive analysis of hedging strategies in the Chinese 50ETF options market, emphasizing dynamic hedging, Value at Risk (VaR), and machine learning-based approaches. Each strategy is meticulously evaluated against various market conditions, revealing distinct strengths and weaknesses. Dynamic hedging proves cost-effective in stable markets but struggles in high volatility scenarios, limiting its risk reduction capacity. Conversely, the VaR model, while reducing risk effectively under extreme conditions, may lead to over-hedging in calmer markets due to high costs and

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9

Ding, Yanwen. "The Practicality of Vasicek Model in China’s Financial Market." SHS Web of Conferences 163 (2023): 01016. http://dx.doi.org/10.1051/shsconf/202316301016.

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In the changing financial market, the price of financial products fluctuates continuously over time. The study of the static term structure of the interest rate on the market can no longer satisfy the actual needs, and the dynamic model is imperative. Compared with the static term structure, the dynamic model introduces a stochastic differential term on the basis of the static term structure model of interest rate. This paper shows some relevant models including Vasicek Model, Single-Factor Dynamic Model, Multi-Factor Dynamic Model, and Kalman Filter method. To conclude, in the multi-factor dy

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10

Škrinjarić, Tihana, and Boško Šego. "Dynamic Portfolio Selection on Croatian Financial Markets: MGARCH Approach." Business Systems Research Journal 7, no. 2 (2016): 78–90. http://dx.doi.org/10.1515/bsrj-2016-0014.

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Abstract Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time. Objectives: This study utilizes MGARCH methodology on Croatian fina

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11

Morimoto, Takayuki, and Yoshinori Kawasaki. "Forecasting Financial Market Volatility Using a Dynamic Topic Model." Asia-Pacific Financial Markets 24, no. 3 (2017): 149–67. http://dx.doi.org/10.1007/s10690-017-9228-z.

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12

Wang, Xue. "The time-varying co-movements between energy market and global financial market." Journal of Computing and Electronic Information Management 10, no. 1 (2023): 88–95. http://dx.doi.org/10.54097/jceim.v10i1.5763.

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Since the global financial crisis in 2008, international energy markets have become more closely linked to financial markets and energy prices have exhibited more financial characteristics. Therefore, it is of great theoretical and practical significance to study the time-varying synergy between the energy market and the global financial market. This paper sets up a model for realizing the time-varying co-movements between energy markets and global financial markets: It uses the Diebold &Yilmaz spillover index method and its dynamic expansion model to test the spillover mechanism of market

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13

Lord Dordunoo. "Dynamic market disruptions." International Journal of Science and Research Archive 15, no. 1 (2025): 802–10. https://doi.org/10.30574/ijsra.2025.15.1.1066.

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Market disruptions have become increasingly dynamic, with external shocks such as tariffs, trade wars, and geopolitical tensions reshaping global economic landscapes. These disruptions often trigger industry volatility, influencing everything from global supply chains to pricing strategies and organizational resilience. This paper explores the financial effects of these disruptions through the PESTEL framework strategic model that examines the Political, Economic, Social, Technological, Environmental, and Legal dimensions of macro-level forces. Using an interdisciplinary literature review of o

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14

Yu, Chao, Jianmin He, Qianting Ma, and Xinyu Liu. "Dynamic Evolution Model of Internet Financial Public Opinion." Information 15, no. 8 (2024): 433. http://dx.doi.org/10.3390/info15080433.

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In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial public opinions, including financial institutions, governments, media, and investors, and models the behavioral characteristics of these entities in the diffusion process. On this basis, we comprehensively use the multi-agent model and the SIR model to construct a dynamic evolution model of inter

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15

Blahun, S. I. "Researching the Impact of Financial Innovations on the Main Financial Indicators of Ukraine." Business Inform 12, no. 527 (2021): 108–13. http://dx.doi.org/10.32983/2222-4459-2021-12-108-113.

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The article researches the impact of financial innovation on the yield of government bonds based on the models of ARCH family, it is proved that the introduction of financial innovations affects government bonds. It is determined that the dynamics of volatility of many financial variables is subordinated to stable regularities. The traditional pricing model for capital assets and its dynamic modification indicate a proportional relationship between the expected over-delivery of the market portfolio and its conditional standard deviation. The ARCH model is a natural instrument for studying this

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16

Chai, Yi Xian, Yan Li Xu, and Dan Liu. "Risk Management Research of Financial Market Based on Dynamic Copula Model." Applied Mechanics and Materials 380-384 (August 2013): 4472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4472.

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Copula model and the application of the model in financial market risk management are discussed in this paper. The paper establishes a dynamic Copula model to solve the financial market risk management problems on the basis of Copula research. Through the use of statistics and financial theories and Copula model, the thesis studies the applications of Copula model in the financial risk management and resolves the problem whether there exists financial crisis contagion or not. The results indicate that the applications of model in the financial market risk management are effective, and the rese

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17

Geng, Qing Feng. "Analysis of the Dynamic Correlation between China’s Second Board and SME Board Based on Different Methods." Applied Mechanics and Materials 687-691 (November 2014): 4938–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4938.

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This paper aims to study the dynamic correlation between the second board market and SME board market by building models to the return series of the two boards’ indexes and calculating dynamic correlation coefficient of the two markets on the basis of DCC-GARCH model and Copula model. The study results show as the following: a) there is positive correlation between the second board market and SME board market and the correlation is very strong; b) time-varying Copula model is better than constant correlation Copula model in describing the correlations among financial markets as it captures mar

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18

Wang, Xinran. "Dynamic changes in US Financial Markets under the COVID-19 Pandemic." BCP Business & Management 35 (December 31, 2022): 27–37. http://dx.doi.org/10.54691/bcpbm.v35i.3223.

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Covid-19 disrupted people’s lives and the world’s economic activities in major ways. The pandemic affected, businesses, companies, and investors in the stock market. This paper aims to how normalized Covid-19 affect the United States stock market by analyzing three major US stock markets: S&P500, NASDAQ, and DJIA. The aim was to examine the effect on stock market’s return and volatility. To analyze the impact of the pandemic, vector autoregression models (VAR) as well as the ARMA-GARCH-X model were used. Impulse response function graph from the VAR model revealed that the pandemic did incr

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19

Kler, Rajneesh. "The Interplay between Fiscal Deficits, Market Capitalization, and Turnover in Financial Markets: Evidence from India using VAR Model." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT & SOCIAL SCIENCE 08, no. 02(II) (2025): 141–51. https://doi.org/10.62823/ijarcmss/8.2(ii).7620.

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This article analyses the dynamic relationship between fiscal deficit, market capitalization, and equity derivatives turnover. Using Vector Autoregressive (VAR) modelling and Granger causality tests, we analyse the two-way dynamics between these variables over time. Our findings reveal that fiscal deficit levels reduce market capitalization, suggesting that high deficits reduce investors' confidence and reduce market values. On the other hand, turnover of equity is highly sensitive to past levels of market capitalization, suggesting that market activity tends to linger in the long term. Howeve

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20

Xu, Yan Li, and Ling Ling Wang. "Study on Financial Market Risk Management Based on the Dynamic Copula Model." Key Engineering Materials 467-469 (February 2011): 2072–77. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2072.

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This thesis mainly studies Copula model and the application of the model in financial market risk management. On the basis of studying copula, this thesis builds a dynamic Copula model to solve the financial market risk management problems. Using statistics and financial theories and Copula model, the thesis studies applications of Copula model in the financial risk management and resolves the problem that whether the financial contagion exists. The results indicate that the applications of model in the financial market risk management are effective, and should study in deep.

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21

Olatunji, Akinrinola, Afua Addy Wilhelmina, Olusola Ajayi-Nifise Adeola, Odeyemi Olubusola, and Falaiye Titilola. "Predicting stock market movements using neural networks: A review and application study." GSC Advanced Research and Reviews 18, no. 2 (2024): 297–311. https://doi.org/10.5281/zenodo.11216482.

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In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing predi

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22

Abed, Riadh El, Sahar Boukadida, and Warda Jaidane. "Financial Stress Transmission from Sovereign Credit Market to Financial Market: A Multivariate FIGARCH-DCC Approach." Global Business Review 20, no. 5 (2019): 1122–40. http://dx.doi.org/10.1177/0972150919846994.

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This study examines the interdependence between the daily eurozone sovereign credit default swaps (CDS) index and four financial market sectors such as banking CDS market (CDSb), underlying sovereign market (BONDs), stock market (BMI) and future interest rate benchmark of the bunds obligation (EUROBOBL). Focusing on different phases of the sovereign debt crises, the aim of this article is to examine how the dynamics of correlations between the CDSs and financial market indicators evolved from 20 September 2011 to 12 February 2016. To this end, we adopt a dynamic conditional correlation (DCC) m

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23

Chen, Yunjie, Junjie Liu, and Peize Gao. "Enhancing Stock Price Prediction Through Sentiment Analysis A FinBERT-LSTM Approach to Market Sentiment Integration." Advances in Economics, Management and Political Sciences 204, no. 1 (2025): 1–8. https://doi.org/10.54254/2754-1169/2025.25278.

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Stock price prediction remains a complex challenge in financial markets due to the dynamic interplay of economic indicators, global events, and investor sentiment. This study explores the integration of sentiment analysis into stock price forecasting using a FinBERT-LSTM model. By leveraging financial news data and market indicators, we aimed to enhance predictive accuracy. Sentiment features, such as sentiment intensity and daily sentiment ratios, were extracted using the FinBERT model and combined with traditional market data in an LSTM framework. Comparative analysis demonstrated that the s

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24

Yu, Hongming. "Algorithm-Driven Perspectives on Stochastic Dynamic Modeling and Financial Risk Management." Applied and Computational Engineering 138, no. 1 (2025): 98–103. https://doi.org/10.54254/2755-2721/2025.21362.

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With the rapid development and increasing complexity of global financial markets, market volatility has become highly unpredictable, posing significant challenges to traditional risk management methods. Stochastic dynamic modelling has emerged as a critical tool for financial risk management due to its theoretical rigor and practical applicability. This study systematically reviews the development and application of stochastic dynamic modelling in financial risk management. By analyzing classical models such as the Black-Scholes model and the Mean-Variance Optimization model, the study identif

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25

Tukur, Kabiru. "TIME-VARYING CORRELATION BETWEEN SEAFOOD AND MEAT INDEX IN THE PRESENCE OF OCEAN POLLUTION SHOCK." FUDMA JOURNAL OF SCIENCES 8, no. 3 (2024): 431–42. https://doi.org/10.33003/fjs-2024-0803-2486.

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This study examines the complex relationships between global meat and seafood markets, focusing on the time-varying correlation between the Meat Index Market and the Seafood Index after Japan's nuclear wastewater release. Employing a Bayesian technique combined with the Skewed Multivariate Generalized Error Distribution, the study efficiently captures the time-varying correlations, with causality tests determining directional influences between the indices. The results reveal significant disruptions in seafood markets, highlighting the geopolitical impact on market dynamics. By offering a fres

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26

Manuel Nogueira Reis, Pedro, and Carlos Pinho. "A dynamic factor model applied to investor sentiment in the European context." Investment Management and Financial Innovations 18, no. 1 (2021): 299–314. http://dx.doi.org/10.21511/imfi.18(1).2021.25.

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This paper proposes an Investor Sentiment Index for the European market and tests its predictability power over returns and volatility. The constructed Investor Sentiment Index for Europe draws upon three well-established and two recent individual sentiment proxies through a novel dynamic factor modeling addressed to behavioral finance. The index is obtained through an extended period of analysis and validated with other sentiment index measures. The work relies on individual sentiment proxies based on a dynamic factor model and tests it using a TGARCH model for volatility and returns. It carr

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27

Pankwaen, Kansuda, Sukrit Thongkairat, and Worrawat Saijai. "Global Cross-Market Trading Optimization Using Iterative Combined Algorithm: A Multi-Asset Approach with Stocks and Cryptocurrencies." Mathematics 13, no. 8 (2025): 1317. https://doi.org/10.3390/math13081317.

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This study presents an advanced adaptive trading framework that integrates Deep Reinforcement Learning (DRL) with the Iterative Model Combining Algorithm (IMCA) to overcome the critical limitations of static ensemble methods in global portfolio optimization. Using a diverse cross-market dataset of 39 stocks from the US, Australia, Europe, Thailand, and one cryptocurrency (BTC-USD), the research rigorously evaluates models’ adaptability under volatile market conditions. Volatile market conditions—such as COVID-19, SVB crisis, and the 2022 crypto crash—are captured via volatility metrics (e.g.,

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28

Kochorba, Valeriia Yu. "Model of Interaction of Structural Elements of the Financial Market of Ukraine." PROBLEMS OF ECONOMY 2, no. 60 (2024): 254–63. http://dx.doi.org/10.32983/2222-0712-2024-2-254-263.

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The aim of the article is to improve the methodology of studying the financial market of Ukraine, analyzing the interaction between the components of the financial market, studying the reaction of the components of the financial market to sudden changes and forecasting the behavior of the structural elements of the financial market. The financial market plays a decisive role in the modern market economy of Ukraine, providing a mechanism for the redistribution of capital between creditors and investors through intermediaries based on the principles of supply and demand. The development of the f

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29

M. Ezzat, Heba. "Behavioral agent-based framework for interacting financial markets." Review of Economics and Political Science 5, no. 2 (2020): 94–115. http://dx.doi.org/10.1108/reps-03-2019-0037.

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Purpose This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored. Design/methodology/approach The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step

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30

Lim, Qing Yang Eddy, Qi Cao, and Chai Quek. "Dynamic portfolio rebalancing through reinforcement learning." Neural Computing and Applications 34, no. 9 (2021): 7125–39. http://dx.doi.org/10.1007/s00521-021-06853-3.

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AbstractPortfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebala

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Brianzoni, Serena, Giovanni Campisi, and Graziella Pacelli. "Coexisting Attractors in a Heterogeneous Agent Model in Discrete Time." Mathematics 11, no. 10 (2023): 2348. http://dx.doi.org/10.3390/math11102348.

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In this paper, the discrete-time version of a continuous-time model with fundamentalists and momentum traders is presented. Our aim consists of studying the impact of cross-sectional momentum traders on the dynamics of the model. To this end, the continuous-time deterministic skeleton of the benchmark model is transformed using sophisticated discretization techniques. It is worth noting that the model does not always maintain the same characteristics after moving from continuous to discrete time. In spite of this, our discrete-time system preserves the dynamic properties of the continuous-time

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Kaufmann, Roger, Andreas Gadmer, and Ralf Klett. "Introduction to Dynamic Financial Analysis." ASTIN Bulletin 31, no. 1 (2001): 213–49. http://dx.doi.org/10.2143/ast.31.1.1003.

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AbstractIn the last few years we have witnessed growing interest in Dynamic Financial Analysis (DFA) in the nonlife insurance industry. DFA combines many economic and mathematical concepts and methods. It is almost impossible to identify and describe a unique DFA methodology. There are some DFA software products for nonlife companies available in the market, each of them relying on its own approach to DFA. Our goal is to give an introduction into this field by presenting a model framework comprising those components many DFA models have in common. By explicit reference to mathematical language

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Shakawi, A. M. H. A., and A. Shabri. "Dynamic learning rate adjustment using volatility in LSTM models for KLCI forecasting." Mathematical Modeling and Computing 12, no. 1 (2025): 158–67. https://doi.org/10.23939/mmc2025.01.158.

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The prediction of financial market behaviour constitutes a multifaceted challenge, attributable to the underlying volatility and non-linear characteristics inherent within market data. Long Short-Term Memory (LSTM) models have demonstrated efficacy in capturing these complexities. This study proposes a novel approach to enhance LSTM model performance by modulating the learning rate adaptively based on market volatility. We apply this method to forecast the Kuala Lumpur Composite Index (KLCI), leveraging volatility as a key input to adapt the learning rate during training. By integrating volati

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Akhiiezer, Olena, Halyna Holotaistrova, Yevgen Gomozov, Vladyslav Mats, and Anton Rogovyi. "STRATEGIC MANAGEMENT OF THE PORTFOLIO OF FINANCIAL ASSET." Bulletin of the National Technical University "KhPI". Series: Mathematical modeling in engineering and technologies, no. 1 (April 13, 2023): 11–17. http://dx.doi.org/10.20998/2222-0631.2022.01.02.

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Until now, three approaches to asset portfolio management have been used. The first approach is the classic one, based on the "Efficient Market Hypothesis" (EMH). The second and more modern approach is related to the "Fractal Market Hypothesis" (FMH). Modern economic practice is characterized by the presence of structurally unstable markets, included as nodes in the network of the world economy, which functions in real time. The structure of the available financial instruments is heterogeneous and non-Markovian processes arise in them. The third approach is the formation of a dynamic strategy

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Lê, Hải Trung. "Spillovers between credit growth and financial assets: Evidence from TVP-VAR connectedness model." Tạp chí Khoa học và Đào tạo Ngân hàng 260+261 (January 2024): 46–60. http://dx.doi.org/10.59276/tckhdt.2024.1.2.2609.

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The article assesses the spillover effects among credit growth, stock, and bond returns through a dynamic network model with time-varying parameters, TVP-VAR, from 2010 to 2023Q2. The findings reveal a dynamic interconnection among credit growth, stock price volatility, and bond prices over time. Notably, credit growth emerges as the primary shock spillover to the system. However, credit growth spillover effects are mainly toward stock returns dynamics, while the connection between credit growth and bond returns is much less clear. Conversely, stock return exhibits spillover effects on bond re

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Cheung, Ko To. "Application and Empirical Analysis of Random Volatility Model in Financial Markets." Highlights in Business, Economics and Management 41 (October 15, 2024): 620–24. http://dx.doi.org/10.54097/t8yke024.

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The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, S

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Popescu, Andrei-Dragos, and Cristi Spulbar. "FINANCIAL DIGITAL ASSETS AND THEIR INTERACTIONS WITH THE TRADITIONAL FINANCIAL MARKETS: A DSGE ANALYSIS." Social Sciences and Education Research Review 10, no. 1 (2023): 284–313. https://doi.org/10.5281/zenodo.8241416.

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We start from the premise that all markets, regardless of the shocks they experience, tend toward equilibrium. This is a characteristic that has been identified for any type of market, as the balance is required to be achieved in order for them to evolve. We are evolving a Dynamic General Stochastic Equilibrium model (DSGE) in order to assess and analysis the capital flows of shocks identified within different digital and traditional markets. The model is based on the fundamental theory of general equilibrium which attempts to describe the fluctuations based on supply, demand, and prices in a

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Shao, Shuai, Li-qun Yang, Yuan-biao Zhang, and Zhi-hui Meng. "A Modified Markowitz Multi-Period Dynamic Portfolio Selection Model Based on the LDIW-PSO." International Journal of Economics and Finance 8, no. 1 (2015): 90. http://dx.doi.org/10.5539/ijef.v8n1p90.

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<p>Modern financial market is an extremely complicated nonlinear system, while gaming and speculation in the market makes the returns and risks of financial assets a great deal of uncertainty. How to construct an effective portfolio, realize the maximization of portfolio returns and the minimization of risks, and optimize the investment capital allocation efficiency are becoming increasingly a hot topic. This paper discusses a revised Markowitz Multi-period Dynamic portfolio mode by introducing LDIW-PSO in the process of solving the optimal investment weight. The LDIW-PSO has greatly imp

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Liu, Linlin. "Assessing the Influence of China's WTO Accession on Global Stock Market Volatility, Cross-border Financial Policies, and Supply Chain Realignments." China and WTO Review 10, no. 1 (2024): 70–86. https://doi.org/10.52152/cwr.2024.10.1.06.

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This study examines how China's WTO membership affects global stock market volatility, FDI, and supply chain realignments to understand its effects on global economic patterns and financial flows. The 2001 WTO membership of China changed global trade, investment, production, and market behaviour. This mixed-method study examines China's WTO accession's many effects quantitatively and qualitatively. In a 2001–2022 dataset, a VAR model shows dynamic variable interdependencies. Stock market returns (SMR), volatility (VOL), interest rates (IR), and inflation rates spread economic shocks across int

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Chide, Ochimana1* Z.S. Saheed2 Ayodeji Salihu4 Alfa Yakubu5 Oyeniran Ishola Wasiu6. "STOCK MARKET DYNAMICS AND ECONOMIC GROWTH IN NIGERIA: EVIDENCE FROM STRUCTURAL VARIANCE AUTOREGRESSIVE MODEL." ISRG Journal of Economics, Business & Management (ISRGJEBM) III, no. II (2025): 146–57. https://doi.org/10.5281/zenodo.15117561.

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<em>The relationship between stock market volatility and Nigeria’s economic growth remains inadequately explored, particularly regarding its structural inefficiencies such as low liquidity, weak investor participation, and limited integration with the broader economy. This study investigates how key stock market indicators—market capitalization, the all-share index, securities trading value, and private sector investment—affect economic growth in Nigeria. Unlike previous studies that focus predominantly on monetary aggregates, this research employs Structural Vector Autoregre

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Joseph Ozigi Basiru, Chinelo Linda Ejiofor, Ekene Cynthia Onukwulu, and Rita Uchenna Attah. "Financial management strategies in emerging markets: A review of theoretical models and practical applications." Magna Scientia Advanced Research and Reviews 7, no. 2 (2023): 123–40. https://doi.org/10.30574/msarr.2023.7.2.0054.

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Financial management in emerging markets presents unique challenges and opportunities, driven by dynamic economic conditions, regulatory frameworks, and cultural nuances. This review explores theoretical models and practical applications of financial management strategies tailored to these contexts. Emerging markets often exhibit high volatility, limited access to capital, and underdeveloped financial systems, necessitating innovative approaches to resource allocation, risk management, and investment decision-making. Theoretical models such as agency theory, pecking order theory, and the trade

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Hussain, Iqra, Nazakat Ali, Hafiz Bilal Ahmad, and Suhail Ashraf. "Volatility spillover effect between cryptocurrency and stock market using MGARCH Bekk model." Natural and Applied Sciences International Journal (NASIJ) 5, no. 2 (2024): 32–55. http://dx.doi.org/10.47264/idea.nasij/5.2.3.

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This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significan

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Musin, Artur R. "Economic-mathematical model for predicting financial market dynamics." Statistics and Economics 15, no. 4 (2018): 61–69. http://dx.doi.org/10.21686/2500-3925-2018-4-61-69.

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Study purpose.Existing approaches to forecasting dynamics of financial markets, as a rule, reduce to econometric calculations or technical analysis techniques, which in turn is a consequence of preferences among specialists, engaged in theoretical research and professional market participants, respectively. The main study purpose is developing a predictive economic-mathematical model that allows combining both approaches. In other words, this model should be estimated using traditional methods of econometrics and, at the same time, take into account the impact on the pricing process of the eff

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Maliukov, Volodymyr, and Natalia Marynenko. "Model financial resources enterprise to adapt to dynamic market conditions environment." Skhid, no. 2(142) (June 3, 2016): 11–15. http://dx.doi.org/10.21847/1728-9343.2016.2(142).70438.

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Wang, Yichen. "Study on the Spillover Effect of Cryptocurrency Market on Chinese Market -- Based on VAR-DCC-GARCH Model." Advances in Economics, Management and Political Sciences 149, no. 1 (2025): 54–61. https://doi.org/10.54254/2754-1169/2024.19261.

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In recent years, the cryptocurrency market has seen explosive growth, with major assets like Bitcoin becoming integral parts of global financial portfolios. Despite their high volatility and speculative nature, cryptocurrencies have garnered significant attention from both institutional and retail investors. This study explores the impact of the digital currency market on Chinas financial sectors, including equities, commodities, bonds, and foreign exchange. Utilizing a VAR-DCC-GARCH model, the research analyzes daily data from 2017 to 2023, revealing significant dynamic correlations and volat

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Singh, Amanjot, and Manjit Singh. "Cross country co-movement in equity markets after the US financial crisis." Journal of Indian Business Research 8, no. 2 (2016): 98–121. http://dx.doi.org/10.1108/jibr-08-2015-0089.

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Purpose This paper aims to attempt to capture the co-movement of the Indian equity market with some of the major economic giants such as the USA, Europe, Japan and China after the occurrence of global financial crisis in a multivariate framework. Apart from these cross-country co-movements, the study also captures an intertemporal risk-return relationship in the Indian equity market, considering the covariance of the Indian equity market with the other countries as well. Design/methodology/approach To account for dynamic correlation coefficients and risk-return dynamics, vector autoregressive

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Cui, Jinyang. "The Relationship between the Gold Price, Crude Oil Price, Exchange Rate and Chinese Stock Market Indexes." Highlights in Business, Economics and Management 10 (May 9, 2023): 180–88. http://dx.doi.org/10.54097/hbem.v10i.8037.

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One of the trickiest problems for investors is how the financial and commodity markets interact with each other. The volatility in one market might affect the price of the other market. This essay aims to clarify the relationship between gold, crude oil, exchange rates, and Chinese stock market indices. In order to do this, the Shanghai Stock Exchange Index and the China Industrial Index, two indices that reflect the Chinese financial market, were subjected to the DCC-GARCH model (Generalized Autoregressive Conditional Heteroskedasticity Model). By capturing the dynamic correlations of the tim

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Mediansyah, Iski, Firza Septian, and Arief Zikry. "Penerapan Whale Optimization Algorithm dalam Pengoptimalan Portofolio Investasi Menggunakan Model Prediktif Artificial Intelligence." Jurnal Software Engineering and Computational Intelligence 2, no. 01 (2024): 50–58. http://dx.doi.org/10.36982/jseci.v2i01.4147.

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The optimization of investment portfolios has become a primary focus in the management of dynamic financial markets. The Whale Optimization Algorithm (WOA) and Artificial Intelligence (AI) have emerged as potential solutions to tackle market complexities. WOA offers an efficient approach to finding optimal solutions, while AI models such as Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms are effective in predicting market behaviors. The integration of WOA and AI holds promise for better outcomes in optimizing investment portfolios by considering complex factors and market

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Xu, Chuhuan, and Yang Sun. "Liquidity Tiered Navigation Strategy: A Time Series Approach to Financial Market Stability." Highlights in Business, Economics and Management 33 (May 9, 2024): 152–59. http://dx.doi.org/10.54097/9r2zfv75.

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This article investigates the dynamic changes in liquidity stratification in financial markets, emphasizing how time series analysis can be used to forecast market liquidity. In China's financial markets, since 2014, liquidity stratification has become crucial, leading to significant differences in borrowing costs and difficulties among financial firms. Focusing on small and medium-sized banks, this study explores the origins and consequences of liquidity stratification. Moreover, it provides a comprehensive analytical approach for predicting market liquidity. This framework includes Granger c

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Zhang, Weiran, Xinmeng Zhang, and Yixin Chen. "Quantitative Statistical Study of Financial Market Sentiment on Economic Cycles: An Analysis Based on the FinBERT Model and TVP-VAR." Transactions on Economics, Business and Management Research 9 (August 21, 2024): 294–302. http://dx.doi.org/10.62051/c7vskc54.

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Amid global financial market turmoil, the relationship between market sentiment and macroeconomic cycles has garnered significant attention. This study leverages big data from financial markets to quantitatively analyze market sentiment using the FinBERT model and investigates its impact on macroeconomic cycles with the TVP-VAR method. Based on textual data from the Shanghai Stock Exchange Index forums and Baidu Index online engagement metrics, the study employs GIS technology to analyze regional emotional responses to financial market fluctuations and economic activity trends.The research rev

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