imely assessment of regional economic activity in Ukraine is severely constrained by institutional and data-related limitations. Official regional gross regional product (GRP) statistics are available only at low frequency, are published with substantial delays, and, in the post-2022 period, are...

Sequence-space models are becoming increasingly popular in macroeconomics, especially in the heterogeneous-agent literature. However, the econometric toolkit for users of these models remains less developed than that available for traditional state-space methods. This note introduces an...

Forecasts that minimize mean squared forecast error (MSE) often exhibit excessive volatility, limiting their practical applicability. We address this accuracy smoothness trade-off by introducing a Multivariate Smooth Sign Accuracy (M-SSA) framework, which extracts smoothed components from...

We propose a nonparametric framework for estimating the extremal index that captures the persistence of extreme observations. The framework provides unified and simple procedures for verifying the well-known local dependence condition D(ᵈ) (un), which characterizes the extremal index yet is...

The yield curve is widely regarded as a powerful descriptor of the economy and market expectations. A common approach to its statistical representation relies on a small number of factors summarizing the curve, which can then be used to forecast real economic activity. We argue that optimal...

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for...

This paper investigates persistence in the MINT (Mexico, Indonesia, Nigeria, Turkey) stock markets applying fractional integration methods to daily data from 1 January 2022 to 31 October 2025. Different model specifications are estimated for prices, log prices and log returns under the...

This paper analyses aggregated data (5 days period) on total solar irradiance (TSI) based on the 3D MHD numerical simulations of solar surface and atmosphere (Yeo et al., 2017), from 30 April 2010 to 8 July 2023. It improves upon earlier studies by including in the sample the most recent...

We develop a model to estimate price-adjustment behavior when prices are observed more frequently than key explanatory variables such as wage costs. We propose a mixed-frequency stochastic (S, s)-model that accommodates infrequently observed costs and allows for plant-, product-, and...

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study...

A dynamic modelling for heavy-tailed cylindrical time series is developed by combining score-driven models with a generalised Pareto-type cylindrical distribution. The proposed specification extends existing cylindrical models by allowing location, scale, concentration, and crucially, the tail...

We assess asymmetries, nonlinearities and state dependencies in dynamic responses of the euro area to monetary policy shocks. The dataset includes macroeconomic, financial, and survey-based variables measuring credit conditions and bank lending transmission channels. These data are observed at...

Official statistics increasingly make use of higher-frequency time series. But when users ultimately are interested in a seasonally adjusted temporal aggregate of these data, we have to decide whether to perform seasonal adjustment or aggregation first. Consequently, we must weigh up the...

The classical X-11 seasonal adjustment method for monthly and quarterly time series is equipped with routines for data-driven selections of both Henderson trendcycle filters and 3 × k seasonal moving averages, currently involving up to three candidate filters in either case. Although these...

This study applies the Log-Periodic Power-Law (LPPL) framework to three major equity markets-Mexico (IPC), Brazil (IBOVESPA), and the United States (NYSE Composite)-using daily closes from 8 November 1991-30 January 2025 for IPC and NYSE, and 3 May 1993-30 January 2025 for IBOVESPA. Multi-window...

This study examines the dynamics of economic freedom in nine advanced democracies in comparison to China over the 1970-2022 period. Using data from the Fraser Institute and the Manifesto Project Database, we apply a Bayesian time-series methodology to identify three key patterns. First, economic...

This paper examines long-run linkages and possible instabilities in the gold–silver price relationship using daily futures prices over the period from 4 January 2010 to 28 November 2025. The empirical analysis includes unit-root and cointegration tests as well as endogenous structural break...

This study investigates the long-memory properties of the prices of three Kenyan commodities (tea, coffee and horticultural products) by applying fractional integration methods to monthly data spanning the period from August 1998 to December 2024. The empirical results provide evidence of...

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...

This paper examines the impact on crude oil prices of the trade tariffs announced by the Trump administration on 2 April 2025 ("Liberation Day"). More specifically, it uses fractional integration methods to analyse daily data on WTI, Brent and Murban oil prices spanning the period from 3 June...

This paper uses fractional integration methods to analyse the long-memory and persistence properties of inflation in the Shanghai Cooperation Organization (SCO) countries over the period from 1 January 1997 to 8 January 2025. This approach is more general than standard models based on the I(0)...

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to...

The COVID-19 pandemic constitutes one of the most significant exogenous shocks to global financial markets in recent history, raising questions about the robustness of market efficiency under extreme uncertainty. This study examines whether the pandemic affected the weak-form efficiency of the...

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently,...

We examine whether aggregate "music mood" derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors...