Intraday Prediction of Operating-Rate Deviations from the Policy Rate: Evidence from Peru

This paper develops an intraday early-warning framework to predict deviations of the volumeweighted overnight interbank rate from the BCRP policy rate after the close of the Central Bank's second intervention window. We study both upward deviations, associated with liquidity-scarcity episodes, and downward deviations, associated with liquidity-abundance episodes. Using a unique high-frequency dataset spanning 2015-2025, we evaluate whether morning liquidity indicators can anticipate rate deviations exceeding 5 basis points. We compare a regularized logistic regression with a nonlinear artificial neural network (ANN), estimating separate models for each direction of deviation. Both models are calibrated on a chronological development sample and evaluated on a held-out test period. The logit model outperforms the ANN in both cases, with a statistically significant ranking advantage (ROC-AUC of 0.95 vs. 0.88 for upward deviations; 0.77 vs. 0.75 for downward deviations). Average marginal effects reveal an economically coherent asymmetry. Market concentration, measured by the HHI, and cross-bank dispersion in reserve requirement compliance reduce the probability of upward deviations and increase the probability of downward deviations. We interpret this as reflecting the presence of a small number of large, readily identifiable liquidity providers: their visibility reduces search frictions and prevents rate spikes when the market is short, while the same concentration shifts bargaining power toward borrowers, who become the scarce side of the negotiation, when the market is long. Overall, the findings support the feasibility of a simple, interpretable early-warning tool for BCRP money market operators.