Preview

Voprosy Ekonomiki

Advanced search
Open Access Open Access  Restricted Access Subscription Access

Lombard List formation as a distorting signal of the Bank of Russia

https://doi.org/10.32609/0042-8736-2022-10-37-65

Abstract

How do Russian market participants react to the decisions of the Bank of Russia on changes to the Lombard List? This paper examines the relationship between the inclusion of securities in the list and changes in returns and volatility of shares in Moscow Stock Exchange companies. To model the behavior of the volatility of stocks of companies, modified HAR-models were used, to model returns — a market model; the study was carried out for 5-minute, hourly and daily time intervals. As a result, it was found that in the period from 2014 to 2020, the addition of a security to the Lombard List, which occurred faster than 3 weeks from the date of issue, led to a significant increase in returns of stocks of companies and to a significant decrease in their volatility and the effects might be observed during several hours. Thus, market participants perceive such news as significant signals about the state of affairs in private companies, despite the initial goal of the regulator. Based on the results of the analysis, recommendations were also formulated for the Bank of Russia to change the mechanism of including securities in the Lombard List.

About the Author

O. V. Telegin
https://www.hse.ru/org/persons/14298691
HSE University
Russian Federation

Oleg V. Telegin

Moscow



References

1. Aganin A.D. (2017). Forecast comparison of volatility models on Russian stock market. Applied Econometrics, Vol. 48, pp. 63—84. (In Russian).

2. Zhemkov M. I., Kuznetsova O. S. (2019). Verbal interventions as a factor of inflation expectations in Russia. Journal of the New Economic Association, Vol. 2, No. 42, pp. 49—69. (In Russian).] https://doi.org/10.31737/2221-2264-2019-42-2-3

3. Kuznetsova O. S., Ulyanova S. R. (2016). The impact of a central bank’s verbal interventions on stock exchange indices. Zhurnal Ekonomicheskoy Teorii, No. 4, pp. 18—27. (In Russian).

4. Kuznetsova O. S., Ulyanova S. R. (2018). The exchange rate and the verbal interventions by the government and the Bank of Russia. HSE Economic Journal, Vol. 22, No. 2, pp. 228—250. (In Russian).] https://doi.org/10.17323/1813-8691-2018-22-2-228-250

5. Merzlyakov S., Habibullin R. (2017). Information policy of the Bank of Russia: The influence of the press releases on the interbank rate. Voprosy Ekonomiki, No. 11, pp. 141—151. (In Russian).] https://doi.org/10.32609/0042-8736-2017-11-141-151

6. Musaev R. A., Kleshko D. V. (2015). Development of refinancing system of the Russian banking sector. Financial Journal, No. 2, pp. 42—51. (In Russian).

7. Telegin O. V., Merzlyakov S. A. (2019). Verbal interventions of the Bank of Russia and the interest rate structure. Zhurnal Ekonomicheskoy Teorii, Vol. 16, No. 4, pp. 654—672. (In Russian).] https://www.doi.org/10.31063/2073-6517/2019.16-4.5

8. Telegin O. V. (2022). Bank of Russia regular communications and volatility short-term effects in financial markets. Journal of the New Economic Association, No. 2, pp. 130—155. (In Russian). https://doi.org/10.31737/2221-2264-2022-54-2-7

9. Teplova T. V., Sokolova T. V. (2017). The non-parametric data envelopment analysis method for portfolio design in the Russian bond market. Economics and Mathematical Methods, Vol. 53, No. 3, pp. 110—128. (In Russian).

10. Chaykovskaya E. (2015). Red orange, or catch in 24 hours. Cbonds Review, Vol. 4, pp. 52—55. (In Russian).

11. Chirkova E. V., Petrov V. V. (2017). Testing for insider trading in the depositary receipts and common shares of the Russian public companies. HSE Economic Journal, Vol. 21, No. 3, pp. 482—514. (In Russian).

12. Andersen T., Bollerslev T., Diebold F., Vega C. (2003). Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review, Vol. 93, No. 1, pp. 38—62. https://doi.org/10.1257/000282803321455151

13. Bernanke B. S., Kuttner K. N. (2005). What explains the stock market’s reaction to Federal Reserve policy? Journal of Finance, Vol. 60, No. 3, pp. 1221—1257. https://doi.org/10.1111/j.1540-6261.2005.00760.x

14. Bollerslev T., Hood B., Huss J., Pedersen L. H. (2018). Risk everywhere: Modeling and managing volatility. Review of Financial Studies, Vol. 31, No. 7, pp. 2729—2773. https://doi.org/10.1093/rfs/hhy041

15. Bullard J. B., Schaling E. (2002). Why the Fed should ignore the stock market. Review, Vol. 84, No. 2, pp. 35—42. Federal Reserve Bank of St. Louis. https://doi.org/10.20955/r.84.35-42

16. Cassola N., Koulischer F. (2019). The collateral channel of open market operations. Journal of Financial Stability, Vol. 41, pp. 73—90. https://doi.org/10.1016/j.jfs.2019.03.002

17. Chailloux A., Gray S. T., McCaughrin R. (2008). Central bank collateral frameworks: Principles and policies. IMF Working Paper, No. 222.

18. Clements A., Preve D. P. A. (2021). A practical guide to harnessing the HAR volatility model. Journal of Banking & Finance, Vol. 133, article 106285. https://doi.org/10.1016/j.jbankfin.2021.106285

19. Corradin S., Rodriguez-Moreno M. (2016). Violating the law of one price: The role of non-conventional monetary policy. European Central Bank Working Paper Series, No. 1927. https://doi.org/10.2866/585712

20. Corsi F., Audrino F., Renó R. (2012). HAR modeling for realized volatility forecasting. In: L. Bauwens, C. Hafner, S. Laurent (eds.). Handbook of volatility models and their applications. Hoboken, NJ: John Wiley & Sons, pp. 363—382. https://doi.org/10.1002/9781118272039.ch15

21. Enikolopov R., Petrova M., Sonin K. (2018). Social media and corruption. American Economic Journal: Applied Economics, Vol. 10, No. 1, pp. 150—174. https://doi.org/10.1257/app.20160089

22. Fama E. F., French K. R. (2015) A five-factor asset pricing model. Journal of Financial Economics, Vol. 116, No. 1, pp. 1—22. https://doi.org/10.1016/j.jfineco.2014.10.010

23. Fiordelisi F., Galloppo G., Ricci O. (2014). The effect of monetary policy interventions on interbank markets, equity indices and G-SIFIs during financial crisis. Journal of Financial Stability, Vol. 11, pp. 49—61. https://doi.org/10.1016/j.jfs.2013.12.002

24. Forsberg L., Ghysels E. (2007). Why do absolute returns predict volatility so well? Journal of Financial Econometrics, Vol. 5, No. 1, pp. 31—67. https://doi.org/10.1093/jjfinec/nbl010

25. Haitsma R., Unalmis D., de Haan J. (2016). The impact of the ECB’s conventional and unconventional monetary policies on stock markets. Journal of Macroeconomics, Vol. 48, pp. 101—116. https://doi.org/10.1016/j.jmacro.2016.02.004

26. Li Y., Khashanah K. M. (2015). The predictive power of volatility pattern recognition in stock market. 2015 IEEE Symposium Series on Computational Intelligence, pp. 742—748. https://doi.org/10.1109/SSCI.2015.112

27. McCredie B., Docherty P., Easton S., Uylangco K. (2016). The channels of monetary policy triggered by central bank actions and statements in the Australian equity market. International Review of Financial Analysis, Vol. 46, pp. 46—61. https://doi.org/10.1016/j.irfa.2016.04.008

28. Nyborg K. G. (2017). Central bank collateral frameworks. Journal of Banking & Finance, Vol. 76, pp. 198—214. https://doi.org/10.1016/j.jbankfin.2016.12.010

29. Pinho C., Sousa C. F. F., Maldonado I. (2017). The impact of ECB announcements on the Eurozone financial markets. Unpublished manuscript.

30. Rosa C. (2011). Words that shake traders: The stock market’s reaction to central bank communication in real time. Journal of Empirical Finance, Vol. 18, No. 5, pp. 915—934. https://doi.org/10.1016/j.jempfin.2011.07.005

31. Sorescu A., Warren N. L., Ertekin L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, Vol. 45, No. 2, pp. 186—207. https://doi.org/10.1007/s11747-017-0516-y

32. Tian F., Yang K., Chen L. (2017). Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity. International Journal of Forecasting, Vol. 33, No. 1, pp. 132—152. https://doi.org/10.1016/j.ijforecast.2016.08.002


Supplementary files

Review

For citations:


Telegin O.V. Lombard List formation as a distorting signal of the Bank of Russia. Voprosy Ekonomiki. 2022;(10):37-65. (In Russ.) https://doi.org/10.32609/0042-8736-2022-10-37-65

Views: 410


ISSN 0042-8736 (Print)