تغییرات توزیعی بازده دارایی‌های مالی در دوره‌های قبل و بعد از کووید 19 بر پایه قانون توانی، تابع نمایی کشیده و توابع q-گوسی
محورهای موضوعی : بورس اوراق بهادار
رسول رضوانی
1
,
غلامرضا عسکرزاده
2
1 - گروه مهندسی مالی، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران
2 - گروه مدیریت مالی، واحد یزد، دانشگاه ازاد اسلامی، یزد، ایران
کلید واژه: توزیع بازده, قانون توانی, دارایی‌های ریسکی,
چکیده مقاله :
شناسایی رفتار توزیعی بازده دارایی‌های ریسکی از ضروریاتی است که توجه بسیاری از محققان را به خود جلب کرده است. چرا که آگاهی و شناخت دقیق‌تر رفتار توزیعی بازده در انها، امکان انجام پیش بینی‌های دقیق‌تر از وضعیت آتی بازار را فراهم می‌کند، به خصوص در تعیین ارزش در معرض ریسک این دارایی‌ها که وابستگی مستقیم با شکل توزیعی بازده دارد.هدف پژوهش حاضر بررسی تغییرات توزیعی بازده دارایی‌های مالی در دوره‌های قبل و بعد از کووید 19 بر پایه قانون توانی، تابع نمایی کشیده و توابع q-گوسی است. در این راستا، 3 متغیر شاخص کل بورس، قیمت طلا و نرخ ارز مورد بررسی و اطلاعات مربوط به آنها در هریک از روزهای معاملاتی طی دوره 07/01/1395 تا 29/10/1401 جمع آوری شد. به منظور آزمون فرضیات، بااستفاده از آزمون کلموگروف-اسمیرنوف، به مقایسه توزیع تجربی بازده‌ها با هریک از توزیع‌های مذکور پرداخته شد. نتایج نشان داد که توزیع‌های لگاریتمی این دارایی‌ها از هیچ یک از توزیع‌های احتمال حاصل از قانون توانی، نمایی کشیده و q-گاوسی تبعیت نمی‌کنند.
Identifying the distributional behavior of returns of risky assets is one of the necessities that has attracted the attention of many researchers. Because a more accurate knowledge and understanding of the distribution behavior of returns in them allows for more accurate predictions of the future state of the market, especially in determining the risk-exposed value of these assets, which has a direct relationship with the distribution form of returns. The aim of the current research is to investigate the distributional changes of financial asset returns in the periods before and after covid-19 based on power law, stretched exponential function and Gaussian q-functions.In this regard, 3 variables: stock market index, gold price and exchange rate were investigated and their related Information was collected in each of the trading days during the period of 2016-03-26 to 2023-01-19 .In order to test the hypotheses, by using the Kolmogorov-Smirnov test, the empirical distribution of returns was compared with each of the mentioned distributions. The results showed that the logarithmic distributions of these assets do not follow any of the probability distributions obtained from the power law, stretched exponential and q-Gaussian.
_|1.Ahmadi, A. (2019). Estimation of Brent Crude Oil Price Volatility Using risk Metric, Standard GARCH, Asymmetric GARCH, FigARCH and Markov Switching Models in Three Modes of Normal Distribution, t-Student and Generalized Errors, Master Thesis, Shahid Beheshti University, Tehran, Iran. (In Persian)
2.Haghighat, M. (2016). Investigating the Effect of High-Order Moments on Future Stock Returns Using the Fama-Macbeth Model (case study: Tehran Stock Exchange), Master Thesis, Allameh Tabatabaei University, Tehran, Iran. (In Persian)
3.Shams Safa, F., Daman keshideh, M., Afsharirad, M., HadiNejad, M., & Daghighi Asl, A. (2022). The Effects of Exchange Rate Volatility and Entry of Real Shareholders on the Return on Assets in the Food and Drink Companies of Tehran Stock Exchange (Dynamic Panel Data Approach). Financial Management Perspective, 12(39), 121-145. (In persian)
4.Raei, R., Nabizade, A. (2013). Testing Stock Return Distribution in the Tehran Stock Exchange, Journal of Financial Management Strategy, 1(1), 1-15. (In Persian)
5.Rostami, M., Makiyan, S. N., & Roozegar, R. (2021). Stock return volatility using Bayesian symmetric and asymmetric GARCH. The Journal of Economic Policy, 12(24), 171-206. (In persian)
6.Safarzadeh, M. H., & Amini, A. (2022). The Information Content of Covid 19 Outbreak Announcement in Tehran Stock Exchange. Financial Management Perspective, 12(40), 119-143. (In persian)
7.Abdoh Tabrizi, H., Ahmadpour, K., Karimi, P. (2012). Investigating the Effect of Reference Price Distribution Variables on Expected Stock Return, Financial Management Perspective, 1(4), 25. (In Persian
8.Moghimi Kandeloos, P. (2012). Stable GARCH Models and Their Application in Stock Return Modeling, Master Thesis, Science and Culture University, Tehran, Iran. (In Persian)
9.Mohamadi, M., Azimi Yancheshme, M., Fouladi, M., & Farhadi, M. (2022). Designing a model to explain the impact of investors' emotions on financial decisions, stock returns and economic volatility. Financial Management Perspective, 12(40), 145-171. (In persian)
10. Bachelier, L.(1900). Théorie de spéculation. Ann. Sci. l’Ecole Norm. Supér. 3, 21–86.
11. Barnea, A.; Downes, D.H. (1973). A reexamination of the empirical distribution of stock price changes. J. Am. Stat. Assoc. 68, 348–350.
12.Baur, D.G., Dimpfl, T., Jung, R.C., (2012). Stock return autocorrelations revisited: A quantile regression approach. J. Empir. Finance 19 (2), 254–265.
13.Blattberg, R.C.; Gonedes, N.J. (1974). A comparison of the stable and Student distributions as statistical models for stock prices. J. Bus. 47, 245–280.
14. Blume, M.E. (1970). Portfolio theory: A step towards its practical application. J. Bus. 43, 152–173.
15.Borgards, O., Czudaj, R.L., Hoang, T.H.V., (2021). Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact.Resour. Policy 71, 101966.
16.Chen, R., Chen, H., Jin, C., Wei, B., Yu, L., (2020). Linkages and spillovers between internet finance and traditional finance: evidence from china. Emerg. Markets Finance Trade 56 (6), 1196–1210.
17.Chevapatrakul, T., Mascia, D.V., (2019). Detecting overreaction in the bitcoin market: A quantile autoregression approach. Finance Res. Lett. 30, 371–377
18.Clark P.K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica, 41, 135–155
19.de Montjoye Y-A, Radaelli L, Singh VK, Pentland A. (2015). Unique in the shopping mall: On the reidentifiability of credit card metadata. Science. 347: 536–539.
20.Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007
21.Fama, E.F. (1965). The behavior of stock-market prices. J. Bus. 38, 404–419.
22.Feng L, Baowen L, Podobnik B, Preis T, Stanley HE. (2012). Linking agent-based models and stochastic models of financial markets. Proc Natl Acad Sci USA. 109: 8388–8393.
23.Ghashghaie, S.; Breymann, W.; Peinke, J.; Talkner, P.; Dodge, Y. (1996). Turbulent cascades in foreign exchange markets. Nature, 381, 767–770.
24.Gopikrishnan, P.; Meyer, M.; Amaral, L.A.N.; Stanley, H.E. (1998). Inverse cubic law for the distribution of stock price variations. Eur. Phys. J. B, 3, 139–140.
25.Harris, D. (2016). Why Practitioners Should Use Bayesian Statistics. Working Paper at SSRN, Amsterdam.
26.Harris, D. (2017). The Distribution of Returns. Journal of Mathematical Finance, 7, 769-804.
27.Heyden, K.J., Heyden, T., (2021). Market reactions to the arrival and containment of COVID-19: An event study. Finance Res. Lett. 38, 101745.
28.Hou, X., Li, S., (2020). The price of official-business collusion evidence from the stock market reaction to ‘‘Hunting the Tigers’’ in China. China Finance Rev. Int. 10 (1), 52–74.
29.Jiang, G.J., Zhu, K.X., (2017). Information shocks and short-term market underreaction. J. Financial Econ. 124 (1), 43–64.
30.Jin C, Lu X, Zhang Y. (2022). Market reaction, COVID-19 pandemic and return distribution. Financ Res Lett. Feb 8:102701. doi: 10.1016/j.frl.2022.102701
31.Mandelbrot, B.B. (1963). The variation of certain speculative prices. J. Bus. 36, 394–419.
32.Mantegna, R.N.; Stanley, H.E. (1995). Scaling behaviour in the dynamics of an economic index. Nature, 376, 46–49.
33.Mezghani, T., Boujelbene, M., Elbayar, M., (2021). Impact of COVID-19 pandemic on risk transmission between googling investor’s sentiment, the Chinese stock and bond markets. China Finance Rev. Int. 11 (3), 322–348.
34.Moat HS, Preis T, Olivola CY, Liu C, Chater N. (2014). Using big data to predict collective behavior in the real world. Behav Brain Sci. 37: 92–93
35.Naidu, D., Ranjeeni, K., (2021). Effect of coronavirus fear on the performance of Australian stock returns: Evidence from an event study. Pac.-Basin Finance J. 66, 101520.
36.Nguyen, L.T.M., Dinh, P.H., (2021). Ex-ante risk management and financial stability during the COVID-19 pandemic: a study of Vietnamese firms. China Finance Rev. Int. 11 (3), 349–371
37.Officer, R.R. (1972). The distribution of stock returns. J. Am. Stat. Assoc. 67, 807–812.
38.Paul W, Baschnagel J. (2013). Stochastic processes: from physics to finance. Switzerland: Springer International Publishing.
39.Pisarenko, V.F.; Sornette, D. (2006). New statistic for financial return distributions: Power-law or exponential? Phys. A Stat. Mech. Its Appl., 366, 387–400.
40.Sun, Y., Wu, M., Zeng, X., Peng, Z., (2021). The impact of COVID-19 on the Chinese stock market: Sentimental or substantial? Finance Res. Lett. 38, 101838.
41.Tsallis, C. (1988). Possible generalization of the Boltzmann-Gibbs statistics. J. Stat. Phys. 52, 479–487.
42.Tsallis, C. (2009). Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World; Springer: Berlin/Heidelberg, Germany.
43.Wątorek M, Kwapień J, Drożdż S. (2021). Financial Return Distributions: Past, Present, and COVID-19. Entropy (Basel). 12;23(7):884.
44.Xu, L., (2021). Stock return and the COVID-19 pandemic: Evidence from Canada and the us. Finance Res. Lett. 38, 101872.
45.Young, M.S; Graff, R.A. (1995). Real estate is not normal: A fresh look at real estate return distributions. J. Real Estate Financ. Econ.10, 225–259.
46.Zhang, Y., Lu, X., Yin, H., Zhao, R., (2021). Pandemic, risk-adaptation and household saving: evidence from china. China Finance Rev. Int.
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