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on Econometric Time Series |
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Issue of 2025–11–17
eleven papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Anmar Kareem; Alexander Aue |
| Abstract: | This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.06224 |
| By: | Hafner, C. M.; Linton, O. B.; Wang, L. |
| Abstract: | We propose MARSLiQ (Multivariate AutoRegressive Smooth Liquidity), a new multivariate model for daily liquidity that combines slowly evolving trends with short-run dynamics to capture both persistent and transitory liquidity movements. In our framework, each asset's liquidity is decomposed into a smooth time-varying trend component and a stationary short-run component, allowing us to separate long-term liquidity levels from short-term fluctuations. The trend for each asset is estimated nonparametrically and further decomposed into a common market trend and idiosyncratic (asset-specific) trends, and seasonal trends, facilitating interpretation of market-wide liquidity shifts versus firm-level effects. We introduce a novel dynamic structure in which an asset's short-run liquidity is driven by its own past liquidity as well as by lagged liquidity of a broad liquidity index (constructed from all assets). This parsimonious specification-combining asset-specific autoregressive feedback with index-based spillovers-makes the model tractable even for high-dimensional systems, while capturing rich liquidity spillover effects across assets. Our model's structure enables a clear analysis of permanent vs. transitory liquidity shocks and their propagation throughout the market. Using the model's Vector MA representation, we perform forecast error variance decompositions to quantify how shocks to one asset's liquidity affect others over time, and we interpret these results through network connectedness measures that map out the web of liquidity interdependence across assets. |
| Keywords: | Forecast Error Decomposition, Liquidity Spillovers, Multiplicative Error Model, Network Connectedness, Nonparametric Trends |
| JEL: | C12 C14 C32 C53 C58 |
| Date: | 2025–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2569 |
| By: | Martins, Igor F. B. Martins (Örebro University School of Business); Virbickaitè, Audronè (CUNEF University, Madrid, Spain); Nguyen, Hoang (Linköping University); Hedibert, Freitas Lopes (Insper Institute of Education and Research) |
| Abstract: | This paper proposes a mixed-frequency stochastic volatility model for intraday returns that captures fast and slow level shifts in the volatility level induced by news from both low-frequency variables and scheduled announcements. A MIDAS component describes slow-moving changes in volatility driven by daily variables, while an announcement component captures fast eventdriven volatility bursts. Using 5-minute crude oil futures returns, we show that accounting for both fast and slow level shifts significantly improves volatility forecasts at intraday and daily horizons. The superior forecasts also translate into higher Sharpe ratios using the volatilitymanaged portfolio strategy. |
| Keywords: | Intraday volatility; high-frequency; announcements; MIDAS; oil; sparsity. |
| JEL: | C22 C52 C58 G32 |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:oruesi:2025_012 |
| By: | Efrem Castelnuovo; Giovanni Pellegrino; Laust L. Særkjær |
| Abstract: | Imposing restrictions on policy rule coefficients in vector autoregressive (VAR) models enhances the identification of monetary policy shocks obtained with sign and narrative restrictions. Monte Carlo simulations and empirical analyses for the United States and the Euro area support this result. For the U.S., adding policy coefficient restrictions yields a larger and more precise short-run output response and more stable Phillips multiplier estimates. Heterogeneity in output responses reflects variation in systematic policy reactions to output. In the Euro area, policy coefficient restrictions sharpen the identification of corporate bond spread responses to monetary policy shocks. |
| Keywords: | monetary policy shocks, narrative restrictions, policy coefficient restrictions, vector autoregressive models, Monte Carlo simulations, DSGE models |
| JEL: | C32 E32 E52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12246 |
| By: | RJ Waken; Fengxian Wang; Sarah A. Eisenstein; Tim McBride; Kim Johnson; Karen Joynt-Maddox |
| Abstract: | Recent advances in interrupted time series analysis permit characterization of a typical non-linear interruption effect through use of generalized additive models. Concurrently, advances in latent time series modeling allow efficient Bayesian multilevel time series models. We propose to combine these concepts with a hierarchical model selection prior to characterize interruption effects with a multilevel structure, encouraging parsimony and partial pooling while incorporating meaningful variability in causal effects across subpopulations of interest, while allowing poststratification. These models are demonstrated with three applications: 1) the effect of the introduction of the prostate specific antigen test on prostate cancer diagnosis rates by race and age group, 2) the change in stroke or trans-ischemic attack hospitalization rates across Medicare beneficiaries by rurality in the months after the start of the COVID-19 pandemic, and 3) the effect of Medicaid expansion in Missouri on the proportion of inpatient hospitalizations discharged with Medicaid as a primary payer by key age groupings and sex. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05725 |
| By: | Mahdi Goldani |
| Abstract: | The policy environment of countries changes rapidly, influencing macro-level indicators such as the Energy Security Index. However, this index is only reported annually, limiting its responsiveness to short-term fluctuations. To address this gap, the present study introduces a daily proxy for the Energy Security Index and applies it to forecast energy security at a daily frequency.The study employs a two stage approach first, a suitable daily proxy for the annual Energy Security Index is identified by applying six time series similarity measures to key energy related variables. Second, the selected proxy is modeled using the XGBoost algorithm to generate 15 day ahead forecasts, enabling high frequency monitoring of energy security dynamics.As the result of proxy choosing, Volume Brent consistently emerged as the most suitable proxy across the majority of methods. The model demonstrated strong performance, with an R squared of 0.981 on the training set and 0.945 on the test set, and acceptable error metrics . The 15 day forecast of Brent volume indicates short term fluctuations, with a peak around day 4, a decline until day 8, a rise near day 10, and a downward trend toward day 15, accompanied by prediction intervals.By integrating time series similarity measures with machine learning based forecasting, this study provides a novel framework for converting low frequency macroeconomic indicators into high frequency, actionable signals. The approach enables real time monitoring of the Energy Security Index, offering policymakers and analysts a scalable and practical tool to respond more rapidly to fast changing policy and market conditions, especially in data scarce environments. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.05556 |
| By: | Dmitrii Vlasiuk; Mikhail Smirnov |
| Abstract: | We test the hypothesis that consecutive intraday price changes in the most liquid U.S. equity ETF (SPY) are conditionally nonrandom. Using NBBO event-time data for about 1, 500 regular trading days, we form for every lag L ordered pairs of a backward price increment ("push") and a forward price increment ("response"), standardize them, and estimate the expected responses on a fine grid of push magnitudes. The resulting lag-by-magnitude maps reveal a persistent structural shift: for short lags (1-5, 000 ticks), expected responses cluster near zero across most push magnitudes, suggesting high short-term efficiency; beyond that range, pronounced tails emerge, indicating that larger historical pushes increasingly correlate with nonzero conditional responses. We also find that large negative pushes are followed by stronger positive responses than equally large positive pushes, consistent with asymmetric liquidity replenishment after sell-side shocks. Decomposition into symmetric and antisymmetric components and the associated dominance curves confirm that short-horizon efficiency is restored only partially. The evidence points to an intraday, lag-resolved anomaly that is invisible in unconditional returns and that can be used to define tradable pockets and risk controls. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.06177 |
| By: | Barrales-Ruiz, Jose; Mendieta-Munoz, Ivan |
| Abstract: | This paper investigates the importance of time-varying parameters in US macro-financial linkages. To do so, we adopt a flexible hybrid time-varying parameter Bayesian vector autoregression with stochastic volatility empirical framework. We find that, first, macro-financial linkages are mainly characterized as hybrid time-varying interactions, adequately captured by a combination of stochastic volatility, constant parameters on most lagged effects, and time-varying parameters that mainly capture the contemporaneous effects of macroeconomic variables on financial variables. Second, the relative change in the size of financial shocks, captured by their respective stochastic volatility components, is the main driver of the observed time-varying effects of financial variables on macroeconomic outcomes during periods of financial stress. Third, the combined contribution of credit spread, house and stock prices shocks to unemployment (GDP growth and inflation) fluctuates from approximately 20% (5%) in normal times to 60% (30%) during the Global Financial Crisis, thus indicating that financial shocks affect more importantly labor market outcomes. Fourth, macroeconomic variables respond more significantly to credit spread and house price shocks. Fifth, GDP growth and inflation react differently to financial shocks: while house price shocks and stock price shocks act as demand-type shocks by moving both variables in the same direction; credit spread shocks act as supply-type shocks by moving both variables in opposite directions. |
| Keywords: | financial shocks, macro-financial linkages, model selection, time-varying parameter vector autoregressions, stochastic volatility |
| JEL: | C11 C32 C52 E30 E44 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:330707 |
| By: | M. Cadoni; R. Melis; A. Trudda |
| Abstract: | Volatility estimation has become one of the core activities of financial analysts. At present, the majority of buy and sell operations are run by "computer traders" that use algorithms mainly based on volatility levels in the market. Several analyses argue that the recent "flash crash crisis" are the amplified consequence of volatility variations. Among the various methodologies proposed in literature, fractals are playing a major role in modeling financial series and, in particular, in analysing volatility characteristics. Following this line, we propose a stochastic approach using a random variable to represent the Hurst Exponent H. We adopt an iterative procedure to model H with a mixture of n Beta distributions, where the number of components will depend on the required modeling accuracy. We choose several types of financial market indexes and assets to evaluate the model and show that the proposed methodology can provide a deep insight into the volatility characteristics associated to each one of them. |
| Keywords: | volatility;Investment Decisions;multifractional brownian motion |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202515 |
| By: | So-Yoon Cho; Jin-Young Kim; Kayoung Ban; Hyeng Keun Koo; Hyun-Gyoon Kim |
| Abstract: | Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers. Furthermore, to better reflect cross-sectional correlations, we introduce a correlation-guided regularizer informed by a stable estimate of the target correlation matrix. This structure effectively extracts salient features not only from historical returns but also from asset-specific and systematic covariates, significantly enhancing the performance of forecasts and portfolios. Experimental results on the daily excess returns of 12 industry portfolios show that Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance. Moreover, in portfolio experiments, portfolios constructed from Diffolio's forecasts show consistently robust performance, thereby outperforming those from benchmarks by achieving higher Sharpe ratios for the mean-variance tangency portfolio and higher certainty equivalents for the growth-optimal portfolio. These results demonstrate the superiority of our proposed Diffolio in terms of not only statistical accuracy but also economic significance. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.07014 |
| By: | Lampe, Max; Adalid, Ramón |
| Abstract: | Monetary aggregates provide valuable information about the monetary policy transmission and the business cycle. This paper applies machine learning methods, namely Learning Vector Quantisation (LVQ) and its distinction-sensitive extension (DSLVQ), to identify turning points in euro area M1 and M3. We benchmark performance against the Bry–Boschan algorithm and standard classifiers. Our results show that LVQ detects M1 turning points with only a three-month delay, halving the six-month confirmation lag of Bry–Boschan dating. DSLVQ delivers comparable accuracy while offering interpretability: it assigns weights to the sources of broad money growth, showing that lending to households and firms, as well as Eurosystem asset purchases when present, are the main drivers of turning points in M3. The findings are robust across parameter choices, bootstrap designs, alternative performance metrics, and comparator models. These results demonstrate that machine learning can yield more timely and interpretable signals from monetary aggregates. For policymakers, this approach enhances the information set available for assessing near-term economic dynamics and understanding the transmission of monetary policy. JEL Classification: E32, E51, C63 |
| Keywords: | machine learning, monetary aggregates, turning points |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253148 |