This paper explores the relationship between news media sentiment and spillover effects in cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of specific to individual cryptocurrencies. Subsequently, employ unique data on cryptocurrency-specific assess its impact these using panel fixed regression analysis. Our findings indicate that plays significant role explaining dynamics within Unlike traditional assets, it appears...

We examine whether the occurrence of jumps in return major cryptocurrencies increases likelihood stock returns blockchain and crypto-exposed US companies. use two criteria to identify stocks with cryptocurrency exposure; i) text search ii) membership indices. first detect that both asset classes are subject jump behaviour. Then, we employ logistic regressions show some probability several The co-jumping behaviour is not affected by COVID-19 outbreak.

We examine how banks adjust credit supply in areas with higher exposure to climate risks by utilizing the province-level air pollution and loan growth data of a large emerging market, Turkey, following Paris Agreement 2015. Our results show that limit their extension more polluted provinces post-agreement interval, implying consider change-related provisioning accordingly. baseline findings are intact against myriad robustness checks. also find shift risk-credit nexus is asymmetric depending...

Abstract In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting nowcasting gross domestic product (GDP) using factor‐type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, Turkey; carry out a horse race in which predictions from various different models are compared. These may (or not) contain constructed both local global datasets. The set that examine...

Recent theoretical developments tend to suggest that rare disaster risks enhance the persistence of uncertainty. Given this, we analyse impact climate (temperature growth or its volatility), as proxies for such unusual events, on economic and policy-related uncertainty 50 US states in a panel data set-up, over monthly period 1984:03 2019:12. Using impulse response functions (IRFs) from regime-based local projections (LPs) model, show an shock itself is not only bigger magnitude when economy...

ABSTRACT The aim of this paper is to utilize the generalized autoregressive conditional heteroscedasticity–mixed data sampling (GARCH‐MIDAS) framework predict daily volatility state‐level stock returns in United States (US), based on weekly metrics from corresponding broad economic conditions indexes (ECIs). In light importance a common factor explaining large proportion total variability conditions, we first apply dynamic model with stochastic (DFM‐SV) filter out national local components...

Abstract We use an international dataset on 5‐min interval intraday data covering nine leading markets and regions to construct measures of realized volatility, jumps, skewness, kurtosis returns Real Estate Investment Trusts (REITs) over the daily period September 2008 August 2020. study out‐of‐sample predictive value skewness for volatility above where we also differentiate between “good” “bad” volatility. find that significantly improve forecasting performance at a daily, weekly, monthly...

Abstract We analyze the out‐of‐sample predictive power of sentiment for realized volatility agricultural commodity price returns. use high‐frequency intra‐day data covering period from 2009 to 2020 estimate volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend include sentiment. further enhance this by incorporating various key moments such as leverage, skewness, kurtosis, upside (“good”) volatility, downside (“bad”) jumps, tail risk, and...

We consider whether inflation is a 'global phenomenon' for European emerging market economies, as has been claimed advanced or high-income countries. find that global factor accounts more than half of the variance in national rates, and show forecasting models headline rates include factors generally produce accurate path forecasts Phillips curve-type with local factors. Our results are qualitatively unaffected by allowing sparsity non-linearity models. also provide some insight to why an...

This study analyzes how monthly stock returns in the United States react to conventional and unconventional shadow rates from February 1994 April 2023. The uses a nonstationary heterogeneous panel data technique appropriate for analyzing large cross-sections long periods. analysis is separated into turbulent tranquil findings suggest that, although rate expected align with long-term rate, its ability boost economic activity markets only applicable short term. Despite Federal Funds Rate (FFR)...

This paper investigates the effects of COVID-19 pandemic-related uncertainty focusing on US tourism subsectors, including airlines, hotels, restaurants, and travel companies. Using daily stock price data, we compute connectedness indices that quantify financial distress in hospitality industry link these with a measure COVID-19-induced uncertainty. Our empirical results show some subsectors are affected more than others. The companies has severely increased after March 2020. Restaurants most...

Abstract We use a vector autoregressive model with functional shocks, capturing the shift of entire term structure interest rates on monetary policy announcement dates, to empirically evaluate effects conventional and unconventional decisions Real Estate Investment Trusts (REITs) markets United States (US). Using 5-min interval intraday data, we analyze not only impact REITs returns, but also its realized variance (RV), jumps (RJ), skewness (RSK), kurtosis (RKU) over daily period September...

Given recent debates about the financialization of commodity markets, we analyze predictive power financial stress for realized volatility agricultural price returns. We estimate from high-frequency intra-day data, where sample period ranges 2009 to 2020. study in-sample and out-of-sample predictability using variants popular heterogeneous autoregressive (HAR) model volatility. value by region origin source, also control various moments (leverage, skewness, kurtosis, jumps, upside tail risk,...