An econometric model based on moments of high orders of a time series for detecting the crisis in stock markets of USA, Germany and Hong Kong

N.B.A. Yousif, Diana Stepanova, Gulnar Astaubayeva, Mafura Uandykova, Alexey Mikhaylov

Article ID: 6533
Vol 9, Issue 1, 2025

VIEWS - 618 (Abstract)

Abstract


Many financial crises have occurred in recent decades, such as the International Debt Crisis of 1982, the East Asian Economic Crisis of 1997–2001, the Russian economic crisis of 1992–1997, the Latin American debt Crisis of 1994–2002, the Global Economic Recession of 2007–2009, which had a strong impact on international relations. The aim of this article is to create an econometric model of the indicator for identifying crisis situations arising in stock markets. The approach under consideration includes data for preprocessing and assessing the stability of the trend of time series using higher-order moments. The results obtained are compared with specific practical situations. To test the proposed indicator, real data of the stock indices of the USA, Germany and Hong Kong in the period World Financial Crisis are used. The scientific novelty of the results of the article consists in the analysis of the initial and given initial moments of high order, as well as the central and reduced central moments of high order. The econometric model of the indicator for identifying crisis situations arising considered in the work, based on high-order moments plays a pivotal role in crisis detection in stock markets, influencing financial innovations in managing the national economy. The findings contribute to the resilience and adaptability of the financial system, ultimately shaping the trajectory of the national economy. By facilitating timely crisis detection, the model supports efforts to maintain economic stability, thereby fostering sustainable growth and resilience in the face of financial disruptions. The model’s insights can shape the national innovation ecosystem by guiding the development and adoption of monetary and financial innovations that are aligned with the economy’s specific needs and challenges.


Keywords


time series; statistical moment; point valuation; assets

Full Text:

PDF


References


An, J., & Mikhaylov, A. (2020). Russian energy projects in South Africa. Journal of Energy in Southern Africa, 31(3), 58–64. https://doi.org/10.17159/2413-3051/2020/v31i3a7809 An, J., Mikhaylov, A., & Chang, T. (2024). Relationship between the popularity of a platform and the price of NFT assets. Finance Research Letters, 61, 105057. https://doi.org/10.1016/j.frl.2024.105057 An, J., Mikhaylov, A., & Jung, S.-U. (2020). The Strategy of South Korea in the Global Oil Market. Energies, 13(10), 2491. https://doi.org/10.3390/en13102491 Batten, J. A., Kinateder, H., Szilagyi, P. G., et al. (2019). Hedging stocks with oil. Energy Economics, 93, 104422. https://doi.org/10.1016/j.eneco.2019.06.007 Bonato, M., Gupta, R., Lau, C. K. M., et al. (2020). Moments-based spillovers across gold and oil markets. Energy Economics, 89, 104799. https://doi.org/10.1016/j.eneco.2020.104799 Bouri, E. (2023). Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks. Renewable Energy, 210, 507–523. https://doi.org/10.1016/j.renene.2023.04.006 Chatzis, S. P., Siakoulis, V., Petropoulos, A., et al. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112, 353–371. https://doi.org/10.1016/j.eswa.2018.06.032 Cui, J., & Maghyereh, A. (2023). Time-frequency dependence and connectedness among global oil markets: Fresh evidence from higher-order moment perspective. Journal of Commodity Markets, 30, 100323. https://doi.org/10.1016/j.jcomm.2023.100323 Dabrowski, J. J., Beyers, C., & de Villiers, J. P. (2016). Systemic banking crisis early warning systems using dynamic Bayesian networks. Expert Systems with Applications, 62, 225–242. https://doi.org/10.1016/j.eswa.2016.06.024 De Clerk, L., & Savel’ev, S. (2021). An investigation of higher order moments of empirical financial data series. arXiv, 2021, arXiv:2103.13199. Doern, R. (2016). Entrepreneurship and crisis management: The experiences of small businesses during the London 2011 riots. International Small Business Journal: Researching Entrepreneurship, 34(3), 276–302. https://doi.org/10.1177/0266242614553863 Finta, M. A., & Aboura, S. (2020). Risk premium spillovers among stock markets: Evidence from higher-order moments. Journal of Financial Markets, 49, 100533. https://doi.org/10.1016/j.finmar.2020.100533 Jiang, J., Shang, P., Zhang, Z., et al. (2018). The multi-scale high-order statistical moments of financial time series. Physica A: Statistical Mechanics and Its Applications, 512, 474–488. https://doi.org/10.1016/j.physa.2018.08.101 Jiang, X., Han, L., & Yin, L. (2019). Can skewness predict currency excess returns? The North American Journal of Economics and Finance, 48, 628–641. https://doi.org/10.1016/j.najef.2018.07.018 Jun, D., Ahn, C., & Kim, G. (2017). Analysis of the global financial crisis using statistical moments. Finance Research Letters, 21, 47–52. https://doi.org/10.1016/j.frl.2016.11.004 Kinateder, H., & Papavassiliou, V. G. (2019). Sovereign bond return prediction with realized higher moments. Journal of International Financial Markets, Institutions and Money, 62, 53–73. https://doi.org/10.1016/j.intfin.2019.05.002 Kratz, M. (2019). Introduction to extreme value theory: Applications to risk analysis & management. In: 2017 MATRIX Annals. Springer. Krugman, P. (1979). Increasing returns, monopolistic competition, and international trade. Journal of International Economics, 9, 469–479. https://doi.org/10.1016/0022-1996(79)90017-5 Linnenluecke, M. K., & McKnight, B. (2017). Community resilience to natural disasters: the role of disaster entrepreneurship. Journal of Enterprising Communities: People and Places in the Global Economy, 11(1), 166–185. https://doi.org/10.1108/jec-01-2015-0005 Majeed, N., & Jamshed, S. (2023). Heightening citizenship behaviours of academicians through transformational leadership: Evidence based interventions. Quality & Quantity, 57(S4), 575–606. https://doi.org/10.1007/s11135-021-01146-2 Mandal, P. K., & Thakur, M. (2024). Higher-order moments in portfolio selection problems: A comprehensive literature review. Expert Systems with Applications, 238, 121625. https://doi.org/10.1016/j.eswa.2023.121625 Mei, D., Liu, J., Ma, F., et al. (2017). Forecasting stock market volatility: Do realized skewness and kurtosis help? Physica A: Statistical Mechanics and Its Applications, 481, 153–159. https://doi.org/10.1016/j.physa.2017.04.020 Mikhaylov, A. (2022). Efficiency of renewable energy plants in Russia. Anais Da Academia Brasileira de Ciências, 94(4). https://doi.org/10.1590/0001-3765202220191226 Mikhaylov, A. (2023). Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space. Journal of Infrastructure, Policy and Development, 7(2), 2223. Mikhaylov, A. Yu. (2021). Development of Friedrich von Hayekʼs theory of private money and economic implications for digital currencies. Terra Economicus, 19(1), 53–62. https://doi.org/10.18522/2073-6606-2021-19-1-53-62 Mikhaylov, A., Dinçer, H., & Yüksel, S. (2023). Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-022-00399-6 Mikhaylov, A., Dinçer, H., Yüksel, S., et al. (2023). Bitcoin mempool growth and trading volumes: Integrated approach based on QROF Multi-SWARA and aggregation operators. Journal of Innovation & Knowledge, 8(3), 100378. https://doi.org/10.1016/j.jik.2023.100378 Moiseev, N., Mikhaylov, A., Dinçer, H., et al. (2023). Market capitalization shock effects on open innovation models in e-commerce: golden cut q-rung orthopair fuzzy multicriteria decision-making analysis. Financial Innovation, 9(1). https://doi.org/10.1186/s40854-023-00461-x Mutalimov, V., Kovaleva, I., Mikhaylov, A., et al. (2021). Assessing regional growth of small business in Russia. Entrepreneurial Business and Economics Review, 9(3), 119–133. https://doi.org/10.15678/eber.2021.090308 Osiyevskyy, O., & Dewald, J. (2018). The pressure cooker: When crisis stimulates explorative business model change intentions. Long Range Planning, 51(4), 540–560. https://doi.org/10.1016/j.lrp.2017.09.003 Pan, G. G., Shiu, Y. M., & Wu, T. C. (2020). Can risk-neutral skewness and kurtosis subsume the information content of historical jumps? Journal of Financial Markets. Rapposelli, A., Birindelli, G., & Modina, M. (2023). The relationship between firm size and efficiency: Why does default on bank loans matter? Quality & Quantity. https://doi.org/10.1007/s11135-023-01810-9 Schulze, N. (2004). Applied Quantile Regression: Microeconometric, Financial, and Environmental Analyses. Universitat Tübingen. Singh, R. K., Neuert, C. E., & Raykov, T. (2023). Assessing conceptual comparability of single-item survey instruments with a mixed-methods approach. Quality & Quantity. https://doi.org/10.1007/s11135-023-01801-w Siregar, R., Pontines, V., & Rajan, R. (2004). Extreme value theory and the incidence of currency crises. In: Econometric Society 2004 Australasian Meetings. Econometric Society. Stepanova, D., Yousif, N.B.A., Karlibaeva, R., Mikhaylov, A. (2024). Current analysis of cryptocurrency mining industry. Journal of Infrastructure, Policy and Development, 8(7), 4803. Taleb, N. (1998). Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley. Tang, Y., & Chen, P. (2014). Time varying moments, regime switch, and crisis warning: The birth–death process with changing transition probability. Physica A: Statistical Mechanics and Its Applications, 404, 56–64. https://doi.org/10.1016/j.physa.2014.02.038 Teng, Y., & Shang, P. (2018). Detrended fluctuation analysis based on higher-order moments of financial time series. Physica A: Statistical Mechanics and Its Applications, 490, 311–322. https://doi.org/10.1016/j.physa.2017.08.062 Werner, F., & Sotskov, Y. N. (2006). Mathematics of Economics and Business. Routledge. Xu, M., & Shang, P. (2018). Analysis of financial time series using multiscale entropy based on skewness and kurtosis. Physica A., 490, 1543–1550. Xu, S., Shao, M., Qiao, W., et al. (2018). Generalized AIC method based on higher-order moments and entropy of financial time series. Physica A: Statistical Mechanics and Its Applications, 505, 1127–1138. https://doi.org/10.1016/j.physa.2018.04.048 Yumashev, A., & Mikhaylov, A. (2020). Development of polymer film coatings with high adhesion to steel alloys and high wear resistance. Polymer Composites, 41(7), 2875–2880. https://doi.org/10.1002/pc.25583 Zhang, H., Jin, C., Bouri, E., et al. (2023). Realized higher-order moments spillovers between commodity and stock markets: Evidence from China. Journal of Commodity Markets, 30, 100275. https://doi.org/10.1016/j.jcomm.2022.100275



DOI: https://doi.org/10.24294/jipd6533

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 N.B.A. Yousif, Diana Stepanova, Gulnar Astaubayeva, Mafura Uandykova, Alexey Mikhaylov

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.