In today’s financial landscape, dated February 2, 2026, relying exclusively on central bank statements for Euribor rate forecasts is an obsolete strategy. For MutuiperlaCasa clients seeking to optimize the entry point for a mortgage or refinancing, a more rigorous approach is necessary. In this article, we abandon discursive macroeconomics to embrace electronic engineering and data science. We will treat interest rate trends not as an opinion, but as a numerical signal to be processed, filtered, and projected into the future using advanced Machine Learning algorithms.

1. Beyond Classical Analysis: Why Stochastic Models Are Not Enough

Traditionally, quantitative finance has relied on stochastic models such as the Vasicek model or the Cox-Ingersoll-Ross (CIR) model to simulate the evolution of interest rates. While mathematically elegant, these models often assume that volatility is constant or follows predictable patterns (Brownian motion), failing to capture the “structural breaks” caused by exogenous shocks (pandemics, wars, sudden changes in monetary policy).

To obtain reliable Euribor rate forecasts in 2026, we must integrate the rigidity of financial mathematics with the flexibility of Artificial Intelligence. The goal is to move from a descriptive approach (what happened) to a predictive one (what will happen), reducing uncertainty for the borrower.

2. The Euribor Rate as a Signal: DSP Applications

From an electronic engineering perspective, the historical Euribor curve (whether 1-month, 3-month, or the IRS for fixed rates) can be viewed as a discrete time signal $x[n]$ affected by noise. This is where Digital Signal Processing (DSP) comes into play.

Filtering Market Noise

Markets react emotionally to daily news, creating high-frequency volatility that obscures the real trend. To identify the underlying direction, we can apply digital filters:

• Exponential Moving Average (EMA): Unlike the simple average, it gives more weight to recent data, reducing the lag in the signal response.
• Kalman Filter: A powerful tool for recursive estimation. The Kalman filter observes the historical series of rates and estimates the “real state” of the system, separating it from measurement noise. It is fundamental for understanding if a 0.1% rise is a trend reversal or simple statistical fluctuation.
• Fourier Transform (FFT): By analyzing the signal in the frequency domain, we can identify hidden cyclicities (e.g., quarterly seasonalities linked to tax deadlines or ECB meetings).

3. Machine Learning and Deep Learning for Forecasts

Once the signal is cleaned, the next step is forecasting. Here we abandon differential equations for neural networks.

Time Series Forecasting with LSTM

Recurrent Neural Networks (RNN), and specifically Long Short-Term Memory (LSTM), are the state of the art for time series analysis. Unlike linear models (such as ARIMA), LSTMs can learn long-term dependencies.

Imagine training an LSTM network with historical Euribor data from 2000 to 2025. The network learns not just the rate value, but the sequence of events. If historically a rapid rise in inflation was followed by a rate hike with a 3-month delay, the LSTM “memorizes” this non-linear pattern.

Integration of Exogenous Variables (Multivariate Analysis)

A robust model does not look only at the past of the rate itself (univariate), but integrates exogenous variables that influence the ECB’s decision. A modern training dataset for Euribor rate forecasts should include:

• Core Inflation Rate (HICP): The main driver of ECB decisions.
• BTP-Bund Spread: Indicator of systemic risk in the Eurozone.
• Sentiment Analysis (NLP): Using Natural Language Processing algorithms to analyze ECB minutes and governor speeches (e.g., classifying words as “Hawkish” or “Dovish”).

4. Step-by-Step Methodology: Building the Model

For programming and data science enthusiasts, here is the operational methodology to replicate a forecasting system:

Phase A: Data Ingestion & Preprocessing

Download official data from sources like Euribor-EBF or ECB Statistical Data Warehouse. Normalizing the data (scaling between 0 and 1) is crucial to allow neural networks to converge quickly.

Phase B: Model Architecture (Example in Python/Keras)

A typical architecture might be:

model = Sequential()
# First LSTM layer with Dropout to prevent overfitting
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
# Second LSTM layer
model.add(LSTM(units=50, return_sequences=False))
model.add(Dropout(0.2))
# Output Layer
model.add(Dense(units=1)) # Euribor rate prediction
model.compile(optimizer='adam', loss='mean_squared_error')

Phase C: Backtesting and Validation

Never trust a model without backtesting. Split the data into Training (80%) and Test (20%). Verify the Root Mean Square Error (RMSE) on “out-of-sample” forecasts. If the model correctly predicts 2024 rates using only data up to 2023, it has predictive value.

5. Practical Application: When to Choose the Mortgage?

How does all this translate for the MutuiperlaCasa client? The Euribor rate forecasts generated by AI allow us to define windows of opportunity.

The Concept of “Optimal Stopping”

In mathematics, optimal stopping theory seeks to determine the right moment to take an action to maximize the expected gain. Applied to mortgages:

1. The model predicts a decreasing Euribor trend for the next 6 months with 85% confidence.
2. The system suggests waiting to refinance, perhaps opting for a variable rate with a CAP in the short term.
3. If the model detects a trend reversal (imminent hike signal detected by the Kalman filter), an alert triggers to immediately lock in a fixed rate (IRS).

Conclusions

The application of predictive algorithms and machine learning to the mortgage market is no longer science fiction. By treating rates as signals and filtering out the emotional noise of the markets, we can obtain Euribor rate forecasts with a degree of accuracy superior to traditional estimates. For those who need to take out a mortgage today, ignoring technology means flying blind; using it means transforming uncertainty into strategy.

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