Abstract
In the paper we consider the factors that determine the overnight interest rates in the Polish interbank market (measured by the POLONIA rate index). In 2008 the Polish central bank (NBP) adapted the policy similar to the European Central Bank (ECB) and since then it has been trying to place the POLONIA rate around the NBP reference rate, mainly by influencing the liquidity conditions through open market operations. We try to answer the question how effective this control was. We identify a set of factors that determine overnight rates, namely: liquidity, expectations, confidence in the banking sector and central bank operations. We analyze to what degree each of these factor has been influencing the POLONIA rate in the period from 2006 to 2016. To this end we have used a non-standard econometric method, namely dynamic model averaging, which allows to identify the set of variables that provide the best description of the explanatory variable. The results reveal that before the outbreak of financial crisis in 2008 the spread between POLONIA rate and reference rate could be explained mainly by liquidity conditions. After the crisis had begun, the importance of liquidity factor decreased and the expectations played a more important role in determining the spread. The liquidity situation has regained its importance in determining the spread since the beginning of 2012, after the central bank had undertook appropriate measures to normalize the situation on the interbank market.
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Notes
- 1.
See for example remarks of Nate Silver (2012), who successfully used it to predict the results of the US presidential elections in 2012 in all 50 states.
- 2.
The technical details are omitted here due to limitations on the length of article. They can be found in Raftery et al. (2012), where the method was developed. In the later computations, instead of using the full specification of the matrices \( {Q}_t^k \) the estimators of covariance matrix from the prediction phase is used (multiplied by some specified forgetting index). One should note that the parameters \( {h}_t^k \), the standard deviations of the error term in eqn. (1), are not constant, which allows to account for heteroscedascity. As in Koop and Korobilis (2012) we estimate it using moving average of lagged observations.
- 3.
Alternatively, one can make estimations using all 256 possible models which can be build using all variables (assuming that variables y(−1), d_reqRes and d_reqRes1 should be present in each model) and then checking the influence of each variable. Such analysis was done and the results were very similar to the results of the analysis based on the four models. The analysis presented here has this advantage that the results are much easier to interpret.
- 4.
For example one of the goals of operational framework of European Central Bank is to eliminate the effects of expectations on EONIA rate. See for example (Linzert and Schmidt 2008).
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Kliber, P. (2017). Determinants of the Spread Between POLONIA Rate and the Reference Rate: Dynamic Model Averaging Approach. In: Jajuga, K., Orlowski, L., Staehr, K. (eds) Contemporary Trends and Challenges in Finance. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-54885-2_3
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