IDEAS home Printed from https://ideas.repec.org/a/agr/journl/v3(628)y2021i3(628)p33-44.html
   My bibliography  Save this article

Multifactorial analysis of the price formation in the terms of a risk-free rate

Author

Listed:
  • Constantin ANGHELACHE

    (Bucharest University of Economic Studies, Romania)

  • Mădălina-Gabriela ANGHEL

    (“Artifex” University of Bucharest, Romania)

  • Iulian RADU

    (Bucharest University of Economic Studies, Romania)

Abstract

The prices being studied on the market must be tested in such a way as to identify the risks that exist, to identify the influencing factors and according to them to be able to assess whether the diversification of some prices on the market is real or is a momentary situation. The expected return of macro factors is not restricted by the null hypothesis and in this respect it is shown that this null hypothesis indicates to the investor the conditions to be taken into account in analysing the prices at which they place portfolios on the stock market, capital market or not. The factors can be grouped and they must be tested from a statistical point of view, to see if the parameters we have calculated can be a decision criterion in making the decision to place by purchase, to place by sale or to buy shares or other assets constituted in other types of portfolios. The models usually used are regression models that ensure the estimation and inference on the market, so to draw a definite conclusion on how it can be appreciated that prices are realistic, are those that have the level pursued by the investor in the sense of increase or decrease. Always, an analysis using the regression model is supplemented with a spectral analysis model to find the seasonal variation that could occur in the capital market. The purpose of this article is to test prices influenced by several factors and to identify a rate and a time when these prices are not fully risky.

Suggested Citation

  • Constantin ANGHELACHE & Mădălina-Gabriela ANGHEL & Iulian RADU, 2021. "Multifactorial analysis of the price formation in the terms of a risk-free rate," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(628), A), pages 33-44, Autumn.
    • Handle: RePEc:agr:journl:v:3(628):y:2021:i:3(628):p:33-44
    as

    Download full text from publisher

    File URL: http://store.ectap.ro/articole/1553.pdf
    Download Restriction: no
    File URL: http://www.ectap.ro/articol.php?id=1553&rid=144
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin Martens & Jason Zein, 2004. "Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1005-1028, November.
    2. Kewei Hou & Chen Xue & Lu Zhang, 2015. "Editor's Choice Digesting Anomalies: An Investment Approach," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 650-705.
    3. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    4. Amit Goyal & Pedro Santa‐Clara, 2003. "Idiosyncratic Risk Matters!," Journal of Finance, American Finance Association, vol. 58(3), pages 975-1007, June.
    5. Kasman, Adnan & Kasman, Saadet & Torun, Erdost, 2009. "Dual long memory property in returns and volatility: Evidence from the CEE countries' stock markets," Emerging Markets Review, Elsevier, vol. 10(2), pages 122-139, June.
    6. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    7. Bardsen, Gunnar & Eitrheim, Oyvind & Jansen, Eilev S. & Nymoen, Ragnar, 2005. "The Econometrics of Macroeconomic Modelling," OUP Catalogue, Oxford University Press, number 9780199246502.
    8. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    9. Amit Goyal & Pedro Santa-Clara, 2003. "Idiosyncratic Risk Matters!," Journal of Finance, American Finance Association, vol. 58(3), pages 975-1008, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, September.
    2. Bao, Jack & Hou, Kewei & Zhang, Shaojun, 2023. "Systematic default and return predictability in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 149(3), pages 349-377.
    3. Lee, Suzanne S., 2023. "The role of idiosyncratic jumps in stock markets," Journal of Financial Markets, Elsevier, vol. 64(C).
    4. Ilan Cooper & Paulo Maio, 2019. "Asset Growth, Profitability, and Investment Opportunities," Management Science, INFORMS, vol. 65(9), pages 3988-4010, September.
    5. Lanfear, Matthew G. & Lioui, Abraham & Siebert, Mark G., 2019. "Market anomalies and disaster risk: Evidence from extreme weather events," Journal of Financial Markets, Elsevier, vol. 46(C).
    6. Bai, Jennie & Bali, Turan G. & Wen, Quan, 2021. "Is there a risk-return tradeoff in the corporate bond market? Time-series and cross-sectional evidence," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1017-1037.
    7. Edmond, Chris & Weill, Pierre-Olivier, 2012. "Aggregate implications of micro asset market segmentation," Journal of Monetary Economics, Elsevier, vol. 59(4), pages 319-335.
    8. Czapkiewicz, Anna & Wójtowicz, Tomasz & Zaremba, Adam, 2023. "Idiosyncratic risk and cross-section of stock returns in emerging European markets," Economic Modelling, Elsevier, vol. 124(C).
    9. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je & Gau, Yin-Feng, 2022. "Risk-return trade-off in the Australian Securities Exchange: Accounting for overnight effects, realized higher moments, long-run relations, and fractional cointegration," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 384-401.
    10. Tsung-Yu Hsieh & Huai-I Lee & Ying-Ru Tsai, 2018. "Idiosyncratic Risk, Stock Returns and Investor Sentiment," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(7), pages 914-924, July.
    11. Janis Becker & Christian Leschinski, 2021. "Estimating the volatility of asset pricing factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 269-278, March.
    12. S. Garg & Vipul, 2014. "Volatility forecasting performance of two-scale realized volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1111-1121, September.
    13. Ayadi, Mohamed A. & Cao, Xu & Lazrak, Skander & Wang, Yan, 2019. "Do idiosyncratic skewness and kurtosis really matter?," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    14. Bin Liu & Amalia Di Iorio, 2016. "The pricing of idiosyncratic volatility: An Australian study," Australian Journal of Management, Australian School of Business, vol. 41(2), pages 353-375, May.
    15. Shahzad, Farrukh & Fareed, Zeeshan & Wang, Zhenkun & Shah, Syed Ghulam Meran, 2020. "Do idiosyncratic risk, market risk, and total risk matter during different firm life cycle stages?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    16. Son, Nguyen Truong & Nguyen, Nhat Minh, 2019. "Prospect theory value and idiosyncratic volatility: Evidence from the Korean stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 21(C), pages 113-122.
    17. Pi‐Hsia Hung & Donald Lien & Yun‐Ju Chien, 2020. "Portfolio concentration and fund manager performance," Review of Financial Economics, John Wiley & Sons, vol. 38(3), pages 423-451, July.
    18. Benjamin M Blau & Ryan J Whitby, 2017. "Range-based volatility, expected stock returns, and the low volatility anomaly," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    19. Paolo Capelli & Federica Ielasi & Angeloantonio Russo, 2021. "Forecasting volatility by integrating financial risk with environmental, social, and governance risk," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(5), pages 1483-1495, September.
    20. Mantilla-Garcia, Daniel & Malagon, Juliana & Aldana-Galindo, Julian R., 2022. "Can the portfolio excess growth rate explain the predictive power of idiosyncratic volatility?," Finance Research Letters, Elsevier, vol. 47(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:agr:journl:v:3(628):y:2021:i:3(628):p:33-44. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marin Dinu (email available below). General contact details of provider: https://edirc.repec.org/data/agerrea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.