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on Econometric Time Series |
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Issue of 2026–04–20
ten papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Anna Bykhovskaya; Nour Meddahi |
| Abstract: | This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics, accommodating nonlinearities, network interactions, and cross-sectional dependence in the multivariate case. Existence and uniqueness of a stationary solution is established via a coupling argument tailored to the discontinuities inherent in binary data. A key theoretical result, further supported by our empirical illustration on S&P 100 data, shows that, under a rare-events scaling, aggregates of such binary processes converge to a Poisson autoregression, providing a micro-foundation for this widely used count model. Maximum likelihood estimation is proposed and illustrated empirically. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.14394 |
| By: | Hilde C. Bjornland; Nicolas Hardy; Dimitris Korobilis |
| Abstract: | We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5\% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25\% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12927 |
| By: | Bidoia, M.; Harvey, A.; Palumbo, D. |
| Abstract: | Data on maxima and minima arise in climate and environment, as well as in economics and finance. Specific examples include rainfall, river level and air quality. This article proposes a new score-driven time series model for dealing with such data. A modification, called the composite score, is used to guarantee invertibility. The statistical properties of the maximum likelihood estimator are investigated and applications to river flow and temperature shows that the model works well in practice. The composite score technique may well prove useful in other situations. |
| Keywords: | Frechet Distribution, Gumbel Distribution, Invertibility, Maximum, River Flow, Score |
| JEL: | C22 |
| Date: | 2026–03–07 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2620 |
| By: | Fantazzini, Dean; Kurbatskii, Alexey |
| Abstract: | This paper investigates the utility of Google Trends data for nowcasting and forecasting regional Consumer Price Indices (CPIs) in Russia. For nowcasting, we compare random walk, ARIMA, and Autoregressive Distributed Lag (ARDL) models, with and without search data. For forecasting, we evaluate ten approaches, including Vector Autoregression (VAR) with Hierarchical Lasso (HLag), dynamic factor models, and shrinkage methods. Results show that for nowcasting, multivariate ARDL models with macroeconomic data consistently outperform simpler ones, while Google Trends adds positive but limited value. In forecasting, search data offers negligible average improvement due to a structural break in early 2022: its predictive power was significant before the geopolitical shift but degraded sharply afterward. Instead, the VAR model with HLag sparsity and comprehensive macroeconomic data consistently proves superior. A robustness check with random forests confirms the advantage of the sparse structured approach. The study highlights the nuanced role of online data and the importance of sparse models for robust forecasting in Russian regions. |
| Keywords: | Nowcasting and Forecasting; Google Trends; Russian Regions; ARDL; VAR; Hierarchical Lasso; Random Forests; Regional CPI; Nonparametric Shrinkage |
| JEL: | C14 C32 C53 C55 E31 E37 R11 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128456 |
| By: | Marco Brianti; Mario Forni; Luca Gambetti; Antonio Granese |
| Abstract: | Building on a frequency-domain identification within a nonlinear Structural Dynamic Factor Model, we study the nonlinear transmission of demand and supply shocks, the two shocks accounting for the bulk of fluctuations in U.S. macroeconomic variables. Supply shocks propagate symmetrically and are well described by linear dynamics. Demand shocks, by contrast, display strong sign asymmetries: contractionary shocks generate larger and more persistent declines in real activity, with limited adjustment of prices and nominal wages, an asymmetry amplified in booms. A New Keynesian model with downward nominal wage rigidity rationalizes these findings, highlighting the role of nominal rigidities as a source of nonlinearities. |
| JEL: | C32 C51 E12 E32 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:bol:bodewp:wp1221 |
| By: | Guo, Hongfei; Marín Díazaraque, Juan Miguel; Veiga, Helena |
| Abstract: | We propose target-driven Bayesian stacking for a fixed six-model ensemble of GARCH and stochastic-volatility forecasts with realised- and VIX-based extensions. Two rolling stacking rules target either log predictive density or QLIKE. In S&P 500, the objective changes the preferred information channel: LPD stacking remains centred on GARCH-RV, whereas QLIKE stacking shifts toward GARCH-VIX. Across 56 rolling windows, the QLIKE stack improves certainty-equivalent returns by roughly one to one-and-a-half percentage points per year, depending on the investor's risk aversion. In the 30 windows where the QLIKE stack assigns material weight to implied volatility models, the gain exceeds two percentage points per year with a 90% win rate. However, LPD stacking delivers tighter 5% Value-at-Risk calibration |
| Keywords: | Bayesian stacking; QLIKE; Implied volatility; Realised variance; Value-at-risk; Volatility forecasting |
| JEL: | C11 G17 C53 |
| Date: | 2026–04–15 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:49851 |
| By: | Tenghan Zhong |
| Abstract: | Volatility forecasting becomes challenging when market conditions change and model performance varies across regimes. Motivated by this instability, we develop a regime-aware specialist routing framework for ETF volatility forecasting. The framework uses online risk-sensitive evaluation and state-dependent gating to combine different forecasting specialists across calm and stressed market states. Using a daily panel of six ETFs under a rolling walk-forward design, we find that the strongest forecaster is regime-dependent rather than global. Relative to the rolling-best baseline, the proposed routing framework reduces high-volatility forecast loss by about 24\% and underprediction loss by about 22\%. These results suggest that specialist routing provides a practical adaptive forecasting architecture for changing market conditions. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10402 |
| By: | Younghoon Kim; Changryong Baek |
| Abstract: | This paper proposes a dynamic network framework for uncovering latent community paths in high-dimensional VAR-type models. By embedding a degree-corrected stochastic co-blockmodel (ScBM) into the transition matrices of VAR-type systems, we separate sending and receiving roles at the node level and summarize complex directional dependence in an interpretable low-dimensional form. Our method integrates directed spectral co-clustering with eigenvector smoothing to track how directional groups split, merge, or persist over time. This framework accommodates both periodic VAR (PVAR) models for cyclical seasonal evolution and generalized VHAR models for structural transitions across ordered dependence horizons. We establish non-asymptotic misclassification bounds for both procedures and provide supporting evidence through Monte Carlo experiments. Applications to U.S.\ nonfarm payrolls distinguish a recurrent business-centered core from more mobile, seasonally sensitive sectors. In global stock volatilities, the results reveal a compact U.S.-centered long-horizon block, a Europe-heavy developed core, and a more dynamic short-horizon reallocation of peripheral and bridge markets. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.12563 |
| By: | Oleg Kiriukhin |
| Abstract: | I formulate an entropy-rate maximization problem at the observable level for stochastic processes observed through an information-reducing observation map. For a visible stationary law, the map determines an observational fiber of hidden stationary laws generating that law. In the finite-state finite-memory setting, retained visible constraints determine a feasible class of stationary $(r+1)$-block laws, and the entropy maximizer is defined as the entropy-rate maximizer on this class. The paper formulates entropy-rate maximization on feasible classes induced by partial observability and develops a structural theory for the resulting maximizer. I prove existence and uniqueness of the maximizer, with uniqueness under a fixed-context-marginal hypothesis and, more generally, via a strict-concavity characterization by row proportionality. Two global characterization regimes are central: a fixed one-point marginal yields the i.i.d. maximizer, and a fixed $r$-block law yields the $(r-1)$-step Markov extension. The gap functional equals a conditional mutual information and vanishes exactly at the maximizing completion. I also derive optimality conditions, local geometry of the maximizer, a latent random-mapping realization that leaves the visible law unchanged, and a local empirical consistency theorem, and illustrate the framework by an aliased hidden-state example. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.10752 |
| By: | Onur Polat (Institute of Informatics, Hacettepe University, Beytepe Campus, 06800 Cankaya, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Dhanashree Somani (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA); Sayar Karmakar (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA) |
| Abstract: | This study examines the predictive power of multi-scale positive and negative speculative bubbles in equity and energy markets for S&P 500 realized variance across horizons from 1 to 24 months. Using a hierarchical modeling framework and machine learning estimators, the analysis evaluates whether stock and oil bubbles provide incremental information beyond macroeconomic variables and financial uncertainty. Applying Clark and West's (2007) tests for nested model comparisons, the results reveal a hierarchy in predictive content that varies by forecast horizon. At the 1-month horizon, neither stock nor oil bubbles improves forecast accuracy. At the 3-month horizon, oil bubbles emerge as the dominant predictor; the Bayesian Regularized Neural Network (BRNN) estimator achieves a statistically significant improvement when oil bubbles are included with stock bubbles, resulting in a 30.7 percent reduction in mean squared error (MSE). At the 6-month horizon, stock bubbles become more important, with both the Gradient Boosting Machine (GBM) and BRNN estimators showing significant improvements. For longer horizons, oil bubbles remain relevant, but their predictive value depends on the estimator: BRNN captures oil bubble effects at 12 months, while GBM does so at 24 months. These findings highlight the importance of horizonspecific model selection and indicate a complex transmission of speculative shocks across asset classes. |
| Keywords: | Stock Market Realized Variance, Stock and Oil Bubbles, Machine Learning, Forecasting |
| JEL: | C22 C53 G10 Q51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202611 |