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on Econometric Time Series |
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Issue of 2025–11–03
fifteen papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | Jean-Marie Dufour; Purevdorj Tuvaandorj |
| Abstract: | This paper introduces a likelihood ratio (LR)-type test that possesses the robustness properties of \(C(\alpha)\)-type procedures in an extremum estimation setting. The test statistic is constructed by applying separate adjustments to the restricted and unrestricted criterion functions, and is shown to be asymptotically pivotal under minimal conditions. It features two main robustness properties. First, unlike standard LR-type statistics, its null asymptotic distribution remains chi-square even under model misspecification, where the information matrix equality fails. Second, it accommodates irregular hypotheses involving constrained parameter spaces, such as boundary parameters, relying solely on root-\(n\)-consistent estimators for nuisance parameters. When the model is correctly specified, no boundary constraints are present, and parameters are estimated by extremum estimators, the proposed test reduces to the standard LR-type statistic. Simulations with ARCH models, where volatility parameters are constrained to be nonnegative, and parametric survival regressions with potentially monotone increasing hazard functions, demonstrate that our test maintains accurate size and exhibits good power. An empirical application to a two-way error components model shows that the proposed test can provide more informative inference than the conventional \(t\)-test. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.17070 |
| By: | Harrison Katz |
| Abstract: | Observation-driven Dirichlet models for compositional time series often use the additive log-ratio (ALR) link and include a moving-average (MA) term built from ALR residuals. In the standard B--DARMA recursion, the usual MA regressor $\alr(\mathbf{Y}_t)-\boldsymbol{\eta}_t$ has nonzero conditional mean under the Dirichlet likelihood, which biases the mean path and blurs the interpretation of MA coefficients. We propose a minimal change: replace the raw regressor with a \emph{centered} innovation $\boldsymbol{\epsilon}_t^{\circ}=\alr(\mathbf{Y}_t)-\mathbb{E}\{\alr(\mathbf{Y}_t)\mid \boldsymbol{\eta}_t, \phi_t\}$, computable in closed form via digamma functions. Centering restores mean-zero innovations for the MA block without altering either the likelihood or the ALR link. We provide simple identities for the conditional mean and the forecast recursion, show first-order equivalence to a digamma-link DARMA while retaining a closed-form inverse to $\boldsymbol{\mu}_t$, and give ready-to-use code. A weekly application to the Federal Reserve H.8 bank-asset composition compares the original (raw-MA) and centered specifications under a fixed holdout and rolling one-step origins. The centered formulation improves log predictive scores with essentially identical point error and markedly cleaner Hamiltonian Monte Carlo diagnostics. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.18903 |
| By: | Mr. Paul Cashin; Mr. Fei Han; Ivy Sabuga; Jing Xie; Fan Zhang |
| Abstract: | This paper evaluates three approaches to address parameter proliferation issue in nowcasting: (i) variable selection using adjusted stepwise autoregressive integrated moving average with exogenous variables (AS-ARIMAX); (ii) regularization in machine learning (ML); and (iii) dimensionality reduction via principal component analysis (PCA). Utilizing 166 variables, we estimate our models from 2007Q2 to 2019Q4 using rolling-window regression, while applying these three approaches. We then conduct a pseudo out-of-sample performance comparison of various nowcasting models—including Bridge, MIDAS, U-MIDAS, dynamic factor model (DFM), and machine learning techniques including Ridge Regression, LASSO, and Elastic Net to predict China's annualized real GDP growth rate from 2020Q1 to 2023Q1. Our findings suggest that the LASSO method outperform all other models, but only when guided by economic judgment and sign restrictions in variable selection. Notably, simpler models like Bridge with AS-ARIMAX variable selection yield reliable estimates nearly comparable to those from LASSO, underscoring the importance of effective variable selection in capturing strong signals. |
| Keywords: | China; GDP; Nowcasting |
| Date: | 2025–10–24 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/217 |
| By: | Federico Gatta; Fabrizio Lillo; Piero Mazzarisi |
| Abstract: | We propose a new approach, termed Realized Risk Measures (RRM), to estimate Value-at-Risk (VaR) and Expected Shortfall (ES) using high-frequency financial data. It extends the Realized Quantile (RQ) approach proposed by Dimitriadis and Halbleib by lifting the assumption of return self-similarity, which displays some limitations in describing empirical data. More specifically, as the RQ, the RRM method transforms intra-day returns in intrinsic time using a subordinator process, in order to capture the inhomogeneity of trading activity and/or volatility clustering. Then, microstructural effects resulting in non-zero autocorrelation are filtered out using a suitable moving average process. Finally, a fat-tailed distribution is fitted on the cleaned intra-day returns. The return distribution at low frequency (daily) is then extrapolated via either a characteristic function approach or Monte Carlo simulations. VaR and ES are estimated as the quantile and the tail mean of the distribution, respectively. The proposed approach is benchmarked against the RQ through several experiments. Extensive numerical simulations and an empirical study on 18 US stocks show the outperformance of our method, both in terms of the in-sample estimated risk measures and in the out-of-sample risk forecasting |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16526 |
| By: | Semere Gebresilassie; Mulue Gebreslasie; Minglian Lin |
| Abstract: | This paper develops a novel framework for modeling the variance swap of multi-asset portfolios by employing the generalized variance approach, which utilizes the determinant of the covariance matrix of the underlying assets. By specifying the distribution of the log returns of the underlying assets under the Heston and Barndorff-Nielsen and Shephard (BNS) stochastic volatility frameworks, we derive closed-form solutions for the realized variance through the computation of the covariance generalization of multi-assets. To evaluate the robustness of the proposed model, we conduct simulations using nine different assets generated via the quantmod package. For a three-asset portfolio, analytical expressions for the multivariate variance swap are obtained under both the Heston and BNS models. Numerical experiments further demonstrate the effectiveness of the proposed model through parameter testing, calibration, and validation. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.20047 |
| By: | Pattravadee de Favereau de Jeneret; Ioannis Diamantis |
| Abstract: | This study investigates whether Topological Data Analysis (TDA) can provide additional insights beyond traditional statistical methods in clustering currency behaviours. We focus on the foreign exchange (FX) market, which is a complex system often exhibiting non-linear and high-dimensional dynamics that classical techniques may not fully capture. We compare clustering results based on TDA-derived features versus classical statistical features using monthly logarithmic returns of 13 major currency exchange rates (all against the euro). Two widely-used clustering algorithms, \(k\)-means and Hierarchical clustering, are applied on both types of features, and cluster quality is evaluated via the Silhouette score and the Calinski-Harabasz index. Our findings show that TDA-based feature clustering produces more compact and well-separated clusters than clustering on traditional statistical features, particularly achieving substantially higher Calinski-Harabasz scores. However, all clustering approaches yield modest Silhouette scores, underscoring the inherent difficulty of grouping FX time series. The differing cluster compositions under TDA vs. classical features suggest that TDA captures structural patterns in currency co-movements that conventional methods might overlook. These results highlight TDA as a valuable complementary tool for analysing financial time series, with potential applications in risk management where understanding structural co-movements is crucial. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.19306 |
| By: | Alahmad, Ahmad; Mínguez Solana, Roberto; Porras Soriano, Rocío; Lozano Galant, José Antonio; Turmo, José |
| Abstract: | Building on previous work that introduced an observability analysis (OA) based on static stateestimation (SSE) for structural system identification (SSI), this study extends SSE to performparameterinference by augmenting the state vector with structural parameters in addition to conventionalstate variables. Performing OA beforehand ensures that the selected measurements enable uniqueand robust parameter recovery. The estimation problem is formulated as a weighted nonlinearleast-squares optimization and solved through an iterative nonlinear process, with both structuralanalysis and parameter derivatives of the stiffness matrix obtained numerically using third-partyfinite element software.The framework unifies state and parameter estimation in a single formulation, enables the use ofhigh-fidelity models for response and sensitivity calculations, and incorporates robust handling ofheterogeneous measurements and faulty data. Numerical studies showaccurate recovery of stiffnessparameters under varied measurement layouts, uncertainty levels, and data quality, confirming themethod's robustness for model updating and structural health monitoring. |
| Keywords: | Structural health monitoring; Structural system identification; State estimation; Parameter estimation; Observability analysis |
| Date: | 2025–10–21 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:48232 |
| By: | Rahul Billakanti; Minchul Shin |
| Abstract: | We propose a simple binarization of predictors—an “at-risk” transformation—as an alternative to the standard practice of using continuous, standardized variables in recession forecasting models. By converting predictors into indicators of unusually weak states, we demonstrate their ability to capture the discrete nature of rare events such as U.S. recessions. Using a large panel of monthly U.S. macroeconomic and financial data, we show that binarized predictors consistently improve out-of-sample forecasting performance—often making linear models competitive with flexible machine learning methods—and that the gains are particularly pronounced around the onset of recessions |
| Keywords: | Recession Forecasting; Machine Learning; Feature Engineering; At-Risk Transformation; Binarized Predictors; Diffusion Index |
| JEL: | C25 C53 E32 E37 |
| Date: | 2025–10–30 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:102004 |
| By: | Zhongjun Qu; Wendun Wang; Xiaomeng Zhang |
| Abstract: | A rich set of frequentist model averaging methods has been developed, but their applications have largely been limited to point prediction, as measuring prediction uncertainty in general settings remains an open problem. In this paper we propose prediction intervals for model averaging based on conformal inference. These intervals cover out-of-sample realizations of the outcome variable with a pre-specified probability, providing a way to assess predictive uncertainty beyond point prediction. The framework allows general model misspecification and applies to averaging across multiple models that can be nested, disjoint, overlapping, or any combination thereof, with weights that may depend on the estimation sample. We establish coverage guarantees under two sets of assumptions: exact finite-sample validity under exchangeability, relevant for cross-sectional data, and asymptotic validity under stationarity, relevant for time-series data. We first present a benchmark algorithm and then introduce a locally adaptive refinement and split-sample procedures that broaden applicability. The methods are illustrated with a cross-sectional application to real estate appraisal and a time-series application to equity premium forecasting. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.16224 |
| By: | Emmanuel Boadi |
| Abstract: | This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.15900 |
| By: | ., Kaustubh; Gopalakrishnan, Pawan Gopalakrishnan; Ranjan, Abhishek Ranjan |
| Abstract: | This paper provides estimation of the New Keynesian Phillips curve accounting for the unexpected large shocks such as Covid-19. The recent pandemic distorted the estimates of the output gap derived using the regular trend cycle decomposition of GDP (HP Filter, BP Filter, Kalman Filter). We propose a modified unobserved components model (UCM) by introducing an additional Student-t distributed irregular component in the trend cycle decomposition of GDP, which successfully isolates transitory shocks like COVID-19 from trend and cycle estimates. We also construct a model-based measure of inflation expectations that captures adaptive learning from a long inflation history and real-time updating during the pandemic. For India, we find a stable linear NKPC. Our results demonstrate that accounting for fat-tailed events is crucial for obtaining reliable Phillips curve estimates in emerging markets. |
| Keywords: | Philips Curve, Potential Growth, Output Gap, Inflation Expectations, Unobserved Components Model, Kalman Filter, Almon Lag |
| JEL: | C51 C60 E32 |
| Date: | 2025–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:126329 |
| By: | WATANABE, Toshiaki |
| Abstract: | This paper analyzes business cycles in Japan by applying Markov switching (MS) models to monthly data on the coincident indicator of composite index (CI) during the period of 1985/01-2025/05 calculated by Economic and Social Research Institute (ESRI), Cabinet Office, the Government of Japan. During the latter half of the sample period, the Japanese economy experienced major shocks such as the global financial crisis in 2008, the Great East Japan Earthquake in 2011 and the COVID-19 pandemic in 2020. CI fell sharply during these periods, which make it difficult to estimate business cycle turning points using the simple MS model. In this paper, the MS model is extended by incorporating Student's t-error and stochastic volatility (SV). Since it is difficult to evaluate the likelihood once SV is introduced, a Bayesian method via Markov chain Monte Carlo (MCMC) is employed. The MS model with t-error or SV is shown to provide the estimates of the business cycle turning points close to those published by ESRI. A new method for evaluating marginal likelihood is evaluated. Bayesian model comparison based on marginal likelihood provides evidence that t-error is not needed once SV is introduced. Using the MS model with normal error and SV, structural changes in CI's mean growth rates during booms and recessions are also analyezed and two break points are found in the both mean growth rates. One is 2008/10 and the other is 2010/02, during which the mean growth rate during recession falls and that during boom rises due to the global financial crisis. |
| JEL: | C11 C22 C51 C52 E32 |
| Date: | 2025–08–24 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-148 |
| By: | Diana Barro (Ca’ Foscari University of Venice); Antonella Basso (Ca’ Foscari University of Venice); Marco Corazza (Ca’ Foscari University of Venice); Guglielmo Alessandro Visentin (Henley Business School, University of Reading) |
| Abstract: | We propose a hybrid approach that combines Neural Networks with a Vector Autoregression (VAR) model to generate long-term forecasts of time series. We apply this methodology to forecast the impact of shifts in monetary policies within the Euro area on a comprehensive set of macroeconomic variables. Our analysis begins with a standard (linear) VAR model, which is then enhanced by incorporating Neural Networks to generate long-term forecasts for key variables such as the interest rate, inflation, real output, narrow money, exchange rate, and corporate bond spread. The results suggest that a Neural Network-VAR model offers improvements over the traditional linear VAR for forecasting certain macroeconomic variables in the long run. However, due to the limited sample size, the nonlinear model does not consistently outperform the linear VAR. |
| Keywords: | Forecasting; VAR; Neural Networks; Monetary policies; Euro area |
| JEL: | C32 C45 C53 E52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:24 |
| By: | Keyuan Wu; Tenghan Zhong; Yuxuan Ouyang |
| Abstract: | We present a fast and robust calibration method for stochastic volatility models that admit Fourier-analytic transform-based pricing via characteristic functions. The design is structure-preserving: we keep the original pricing transform and (i) split the pricing formula into data-independent inte- grals and a market-dependent remainder; (ii) precompute those data-independent integrals with GPU acceleration; and (iii) approximate only the remaining, market-dependent pricing map with a small neural network. We instantiate the workflow on a rough volatility model with tempered-stable jumps tailored to power-type volatility derivatives and calibrate it to VIX options with a global-to-local search. We verify that a pure-jump rough volatility model adequately captures the VIX dynamics, consistent with prior empirical findings, and demonstrate that our calibration method achieves high accuracy and speed. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.19126 |
| By: | Yaxuan Kong; Yoontae Hwang; Marcus Kaiser; Chris Vryonides; Roel Oomen; Stefan Zohren |
| Abstract: | We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured news data. M2VN leverages the representational power of deep neural networks to address two key challenges in this domain: (i) aligning and fusing heterogeneous data modalities, numerical financial data and textual information, and (ii) mitigating look-ahead bias that can undermine the validity of financial models. To achieve this, M2VN combines open-source market features with news embeddings generated by Time Machine GPT, a recently introduced point-in-time LLM, ensuring temporal integrity. An auxiliary alignment loss is introduced to enhance the integration of structured and unstructured data within the deep learning architecture. Extensive experiments demonstrate that M2VN consistently outperforms existing baselines, underscoring its practical value for risk management and financial decision-making in dynamic markets. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.20699 |