Russia–Ukraine Conflict, Commodities and Stock Market: A Quantile VAR Analysis

The Russia–Ukrainian war, which began in 2014 and exploded with the invasion of the Russian army on 24 February 2022, has profoundly destabilized the political, economic and financial balance of Europe and beyond. To the humanitarian emergency associated with every war has been added the deep crisis generated by the strong energy and food dependence that many European countries, and not only European, have developed over decades on Ukraine (especially for wheat) and Russia (especially for natural gas). The aim of this article is to verify the existence of a link between the performance of the Eurostoxx index and the price of wheat futures and TTF natural gas, from 25 February 2019 to 28 September 2023. Through a quantile VAR analysis, a link is sought between the Eurostoxx 50 index, and wheat and TTF gas futures prices. Furthermore, the analysis intends to understand whether the presence of such relationship only manifested itself following the war events, or whether it was already present in the market. The analysis carried out also shows that the relationship between the stock market and raw material prices was present even before the conflict

Farm Capital Structure Choice under Credit Constraint: Theory and Application

This study proposed a theoretical framework for analyzing farm capital structure choice. The theoretical model recognizes that the costs of debt are endogenously determined which in turn reflect the degree of credit constraint faced by individual borrowers. Based on the proposed model, we derived the impacts of different determinants on capital structure choice analytically. The theoretical inferences are further tested with empirical data. Methodologically, we proposed a fixed-effect quantile regression procedure to estimate the impacts of determinants at different ranges of leverage. The effects of determinants are discussed in the empirical application.Capital Structure, Cost of Debt, Credit Constraint, Quantile Regression, Agricultural Finance,

A Comparison of Two Scaling Techniques to Reduce Uncertainty in Predictive Models

This research examines the use of two scaling techniques to accurately transfer information from small-scale data to large-scale predictions in a handful of nonlinear functions. The two techniques are (1) using random draws from distributions that represent smaller time scales and (2) using a single draw from a distribution representing the mean over all time represented by the model. This research used simulation to create the underlying distributions for the variable and parameters of the chosen functions which were then scaled accordingly. Once scaled, the variable and parameters were plugged into our chosen functions to give an output value. Using simulation, output distributions were created for each combination of scaling technique, underlying distribution, variable bounds, and parameter bounds. These distributions were then compared using a variety of statistical tests, measures, and graphical plots

Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP

Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low- frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation. The proposed methods allow us to analyse conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to detect the vulnerability in the US economy and then to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk, the Expected Shortfall and distance among percentiles of real GDP nowcasts

Impact of climate policy uncertainty on return spillover among green assets and portfolio implications

Peer reviewe

Evaluating the Precision of Estimators of Quantile-Based Risk Measures

This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return volatilities under usual market conditions. Moreover, analysis of realised tail outcomes supports seasonality for extraordinary market conditions across the trading day.Value at Risk, Expected Shortfall, Spectral Risk Measures, Moments, Precision.