— durations of enlargement and contraction in output or monetary markets — lie on the coronary heart of macroeconomic evaluation. When nations share a typical forex, as within the eurozone, synchronized cycles are crucial for a one-size-fits-all financial coverage to work. This concept was first put forth by Robert Mundell (1961), the daddy of Optimum Foreign money Space concept. If, for instance, Germany is in disaster and Spain is booming, as was the case after the flip of the millennium, the European Central Financial institution (ECB) can’t set the proper rate of interest for each nations. A decrease rate of interest would result in overheating the economic system in Spain, and a better rate of interest would exacerbate the disaster in Germany.
Conventional measures of cycle synchronization typically depend on easy correlations. However what occurs if two economies are following very comparable enterprise or monetary patterns, however one is simply “forward” or “behind” the opposite by a number of quarters?
Enter Dynamic Time Warping (DTW), a method initially developed for speech recognition however more and more well-liked in information science for evaluating time sequence with comparable shapes but completely different timings. In our paper, “Warpings in Time: Enterprise and Monetary Cycle Synchronization within the Euro Space” (Bugdalle & Pfeifer, 2025), we assemble composite indices of euro-area enterprise and monetary cycles after which use DTW to measure how intently these cycles align throughout nations. Our Optimum Foreign money Space (OCA) monitor that makes it potential to trace cycle divergence in actual time — and to identify part lags with out penalizing them as harshly as conventional metrics would.
Capturing part shifts and amplitude variations
Most current research of cycle synchronization do three issues that may be problematic:
- Static remedy of cycles: For instance, trend-extraction strategies (just like the HP filter) take away the cyclical parts from the info. Even in additional advanced frameworks — reminiscent of state-space fashions that do permit for cyclicality — the cycle frequency itself typically stays fastened.
- Use of the imply: Normal dispersion indicators like variance or normal deviation at all times misread the common because the “optimum”. In different phrases, distances will not be measured between pairs of cycles, however relative to a imply or reference cycle. This obscures multimodality. For example, if our cycles truly fall into two (or extra) well-separated clusters, the centroid will lie between them — in a area the place no actual information exist — and all of the cycle-to-mean distances will look average, although cycles from completely different clusters are literally extraordinarily distant.
- Part shifts: Most distance measures are Euclidean. For instance, two cycles could also be barely shifted in time but nonetheless completely synchronized. This level could also be notably necessary for financial coverage. Many OCA indicators find yourself overstating divergence, particularly in durations when economies are “almost” in sync however offset by a number of months or quarters.
Dynamic Time Warping (DTW) for cycle synchronization
DTW is a non-parametric algorithm that finds the optimum alignment (or “warping”) between two time sequence by permitting one sequence to stretch or compress in time to match the opposite. In our case, DTW is utilized to every kind of the smoothed cycle indices, which means one measure of similarity is estimated for every kind of cycle. Inside every cycle class, DTW computes the alignment path πij for every pair of nations i and j that minimizes the cumulative distance between two cycles:
[D(mathbf{x}_i, mathbf{x}_j) = min{pi_{ij}} sum_{(t, s) in pi_{ij}} left| mathbf{x}_{i,t} – mathbf{x}_{j,s} right|^2,]
the place xi and xj is the smoothed cycle values at time t and s for nations i and j, respectively. The ensuing distance D(xi, xj) captures the diploma of similarity, with smaller values indicating nearer alignment of the 2 cycles. To make sure that the DTW comparability displays the timing of cyclical actions, the alignment is carried out over an area window (Sakoe-Chiba Band) outlined by the common cycle period. Lastly, to combination all pairwise DTW distances into one euro‐space indicator, we compute a GDP‐weighted imply of D(xi, xj). This weighted common is the divergence index proven beneath (Determine 3).
Key advantages of DTW with Sakoe-Chiba Bands in an economic-cycle context:
- Part-invariance. Small lags or leads don’t robotically set off massive divergence scores. A one-quarter shift received’t severely penalize the gap if the underlying patterns stay nearly similar.
- Form sensitivity. DTW preserves details about amplitude, pattern reversals, and the relative “form” of booms and busts. Two nations that each expertise a pointy credit score growth — even when one is forward by 1 / 4 — will nonetheless be deemed extremely comparable.
- Time-varying flexibility. By making use of DTW over a rolling window (e.g., an area band of ±5 quarters for enterprise cycles, ±6 quarters for monetary cycles), the tactic adapts to altering cycle durations with out imposing a hard and fast frequency.
Constructing composite enterprise and monetary cycles
As an example DTW’s energy, we first assemble two composite cycle indices for every euro-area nation:
- Enterprise Cycle Index: Quarterly actual GDP progress, personal consumption progress, gross fastened capital formation progress, and unemployment progress.
- Monetary Cycle Index: Quarterly actual credit score progress (financial institution lending), house-price progress, stock-price progress, and authorities bond-price progress.
Utilizing a non-parametric strategy launched by Schüler et al. (2020), we extract every nation’s underlying cycle — an index that alternates between 0 and 1 to replicate expansionary versus contractionary phases, however with time-varying amplitude and period. This avoids inflexible detrending and retains turning factors intact.
Figures 1 and a pair of
Be aware: Indices of enterprise cycle and monetary cycles as deviations from their historic median progress — 0.5 corresponds to the long-term median progress price of every index. The composite monetary cycle combines quarter-on-quarter progress in credit score, home costs, fairness costs and bond costs — exhibiting each the uncooked (unfiltered) sequence and the band-pass-filtered sequence utilizing country-specific frequency bands. The filtered enterprise cycle combines quarter-on-quarter progress in GDP, consumption, funding, and unemployment
From pairwise DTW distances to an combination divergence monitor
As soon as every nation’s enterprise and monetary cycles are estimated, we compute pairwise DTW distances between each pair of nations (e.g., Germany vs. Spain, France vs. Italy, and so on.). To kind a single euro-area “divergence index,” they take a GDP-weighted common of all pairwise DTW distances. The next index worth means larger divergence throughout nationwide cycles; a decrease worth means tighter synchronization.
Determine 3
Be aware: The determine reveals quarterly measures of cycle divergence within the euro space from 1985Q1–2023Q4. The dashed purple line plots the imply dynamic-time-warping (DTW) distance throughout all pairwise comparisons of nationwide financial-cycle indices; the dashed blue line reveals the equal for business-cycle indices. The strong black line is the GDP-weighted common of those two sequence, our composite Divergence Monitor. Shaded gray bands mark OECD recession durations for the eurozone. Larger values point out larger divergence throughout member-state cycle
While you plot this sequence (Determine 3), a number of patterns emerge:
- Nineteen Nineties convergence: Enterprise-cycle divergence fell sharply as convergence standards underneath Maastricht took maintain.
- Pre-2008 monetary divergence: Monetary cycles truly diverged nicely earlier than the worldwide monetary disaster — that peak in divergence is sort of invisible to correlation or amplitude-based metrics.
- Submit-2010 re-alignment: ECB unconventional financial coverage (OMT, QE) coincided with convergence in each enterprise and monetary cycles.
- Late-2021 uptick: For the reason that COVID-19 shock, divergence has begun creeping again up, as some nations possible recovered quicker than others.
Takeaways for information scientists and economists
- Versatile sample matching: When evaluating financial (or any) time sequence which will share the identical “form” however are out of part, DTW is usually a greater similarity measure than Euclidean distance or easy correlation.
- Dealing with non-stationary frequencies: Enterprise and monetary cycles don’t are available in neat, fixed-length packages. DTW’s capacity to adapt to various cycle durations preserves real-world turning factors.
Excited by exploring the code or following the stay “Divergence Monitor” for the euro space? Take a look at https://github.com/Moritz-Pfeifer/Divergence_Monitor for information, Python notebooks, and https://moritz-pfeifer.github.io/eurozone-divergence-monitor/ for an interactive visualization that permits you to see how synchronization has advanced because the Eighties.
References:
Bugdalle, T., Pfeifer, M. (2025). Warpings in time: Enterprise and monetary cycle synchronization within the euro space. SSRN preprint. Link to working paper
Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken phrase recognition. IEEE Transactions on Acoustics, Speech, and Sign Processing, 26(1), 43–49. Link to paper
Schüler, Y. S., P. P. Hiebert, and T. A. Peltonen (2020). Monetary cycles: Characterisation and real-time measurement. Journal of Worldwide Cash and Finance 100. Link to paper
Mundell, R. (1961). A concept of optimum forex areas. American Financial Evaluation, 51(4), 657–665. Link to paper
