This study proposes a regression-based estimation method in difference-in-differences settings in the presence of time-varying covariates - a scenario commonly encountered in applications. We impose only a conditional parallel trends assumption with time-varying covariates and plausible...

There is a growing need to provide AI risk management models that can assess whether AI applications are safe and trustworthy, to make them responsible. To date, there are a few research papers on this topic. To fill the gap, in this paper we extend the recently proposed SAFE framework, a...

This study examines how oil supply, demand, and risk shocks, along with geopolitical risks, impact the performance of clean and fossil fuel stocks. Using daily data from March 2014 to January 2022, advanced methods like wavelet quantile correlation (WQC), cross-quantilogram (QC), and...

This paper shows that, in a setup 'a la Kyle (1985), correlated retail trading opens up new profit opportunities for professional investors at the expense of retail investors. Additionally, it demonstrates that market quality can benefit through higher market liquidity and higher price...

Soybeans play a critical role in China's food system and hold significant importance in ensuring national food security. This study explores the correlation between soybean futures prices and international soybean import prices. It employs a time-series model to analyse the volatility of the...

The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the "dual carbon" goals, the Chinese government has actively implemented measures to reduce carbon emissions,...

A group of agents with a common prior receive informative signals about an unknown state repeatedly over time. If these signals were public, agents' beliefs would be identical and commonly known. This suggests that if signals were private, then the more correlated these are, the greater the...

We examine technology ETF and uncertainty index (VIX, GVZ, and OVZ) spillover dynamics and quantile frequency interconnectedness across market states. This study is the first to use quantile-frequency spillover, quadruple wavelet coherence, and wavelet quantile correlation methodologies to...

We develop a data-driven procedure to identify which correlations in high-dimensional dynamic systems should be time-varying, constant, or zero. The method integrates a vine-based multivariate partial correlation model with sequential penalized estimation. Applied to 50 US equities and...

This paper proposes a nonparametric estimator of the spot volatility matrix with high-frequency data. Our newly proposed Positive Definite Fourier (PDF) estimator produces symmetric positive semi-definite estimates and is consistent with a suitable choice of the localizing kernel. The PDF...

This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression...

Amid the growing debate over how cryptocurrencies are reshaping global finance, this study explores the nexus between Bitcoin, Brent Crude Oil, Gold and the U.S. Dollar Index. We used a time-varying vector autoregressive (tvVAR) model to examine the connection among these four assets during the...

This paper studies how a forward-looking decision maker experiments on unknown alternatives of correlated utilities. The utilities are modeled by a Brownian motion such that similar alternatives yield similar utilities. Experimentation trades off between the continuation value of exploration and...

This paper takes a deep dive into mesh routing protocols, unraveling how they hold up under the pressures of varying node densities and the hustle and bustle of mobility. This paper included robust and advanced non-parametric statistical tests-think Kruskal-Wallis and Mann-Whitney-to figure out...

The popular Wishart (WI) processes, first introduced by Bru in 1991, exhibit convenient analytical properties for modeling asset prices, particularly a closed-form characteristic function, and the ability to jointly model stochastic volatility and correlation. These features tend to increase...

There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates...

This paper examines the extent of stock-returns' co-movements among firms in different countries and explores how various measures of closeness affect those co-movements by estimating a spatial autoregressive (SAR) convex combination model that merges four weight matrices-geographical distance,...

Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic...

Different methods of eliciting long-run expectations yield data that predict economic choices differently well. We ask members of a wide population sample to make a 10-year investment decision and to forecast stock market returns in one of two formats: they either predict the average of annual...