Abstract
The article considers models that are the most commonly used to estimate the cash outflows of nonmaturity deposits: core level of deposit balances, Geometric Brownian Motion (GBM), and Cash Flow at Risk (CFaR). These models use a one-dimensional normal distribution of the probabilities of balances or cash flows. We generalize the above-mentioned cash outflow models in linear quantile regression. The properties of quantile regression are particularly attractive for assessing the liquidity risk of non-maturity deposits. Quantile regression determines quantile directly. It does not depend on the type of distributions of deposit balances or cash flows; it is resistant to outliers that are typical of outstanding deposits; captures fat tails of the underlying distribution of variables; does not assume that variance is constant. To construct a quantile regression, we use a conditional twodimensional empirical distribution of cash outflows and deposit balances for corporate non-maturity deposits denominated in hryvnias under both normal and crisis conditions. Statistical tests confirmed the satisfactory goodness-of-fit for the model and the hypothesis of linear dependence of cash flows on the current level of deposit balances. As a result, the constructed quantile regression has no areas of the values of deposit balances in which liquidity risk would be underestimated. The developed methodology for quantile measure of depositoutflow will be useful both for banking supervision and banks.
Highlights
Non-maturity deposits are an important and cheap funding source for traditional banking
This study aims to analyze empirical data regarding the behavior of banking non-maturity deposits and to construct a quantile regression to find the worst net cash outflows under normal and crisis conditions
Empirical data confirms the existence of a core level of deposit balances
Summary
Non-maturity deposits are an important and cheap funding source for traditional banking. That the net cash outflow means the same as a reduction in deposit balances below their current level over a given period: cft = (Bt+1 — Bt) /∆t,. This study aims to analyze empirical data regarding the behavior of banking non-maturity deposits and to construct a quantile regression to find the worst net cash outflows under normal and crisis conditions. Note that in more advanced approaches in order to estimate a cash outflow it is used a multivariate mean regression with relevant macro and market variables (Yan et al, 2011) Both indirect and direct methods usually utilize the univariate probability distributions. Quantile regression allows directly estimating a quantile of a dependent variable (in our case, cash outflow) conditional on an independent variable (in our case, the current level of deposit balances). It should be noted that the interception values of different models for normal and crisis conditions cannot be compared, as, in these periods, the bank had different deposit levels
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