When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

We present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes.

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method.

SINCRONIZACIÓN DE LOS CICLOS ECONÓMICOS DE MÉXICO Y ESTADOS UNIDOS: UN ENFOQUE DE CORRELACIÓN DINÁMICA

<p>El objetivo de este documento es analizar la sincronización de los ciclos económicos de México y Estados Unidos durante el periodo 1981-2017 mediante la estimación de un coeficiente de correlación condicional dinámica que permite tener una estimación para cada periodo de tiempo. Los resultados, obtenidos a partir de distintos indicadores de producción y métodos de eliminación de tendencia, muestran un aumento desde la apertura de la economía mexicana a mediados de la década de 1980, especialmente durante las recesiones de 2001-2002 y 2008-2009 y también una serie de descensos aislados, explicados por diferencias en los ritmos de crecimiento de ambas economías, y una declinación sostenida en la fase pos-Gran Recesión que se explica principalmente por reducciones en el comercio exterior.</p><p> </p><p align="center">SYNCHRONIZATION OF THE BUSINESS CYCLES OF MEXICO AND THE UNITED STATES: A DYNAMIC CORRELATION APPROACH</p><p align="center"><strong>ABSTRACT</strong></p><p>The objective of this paper is to analyze the business cycle synchronization of Mexico and the United States over the period 1981-2017 by estimating a dynamic conditional correlation coefficient that allows us to have an estimate for each time period. The results, obtained from different production indicators and different de-trending methods, show an increase in this synchronization after the opening of the Mexican economy in the mid-eighties, especially during the common recessions of 2001-2002 and 2008-2009, and some isolated drops explained by differences in the growth rates of both economies as well as a sustained decline in the post-Great Recession phase resulting from the decline of international trade.</p>

Is Bitcoin a safe haven? Application of FinTech to safeguard Australian stock markets

Purpose This study aims to address the timely question of whether Bitcoin exhibited a safe haven property against the major Australian stock indices during the first and second waves of the COVID-19 pandemic in Australia and whether such property is similar or different in one year time from the first wave of the COVID-19. Design/methodology/approach The authors used the bivariate Dynamic Conditional Correlation, Generalized Autoregressive Conditional Heteroskedasticity model, on the five-day returns of Bitcoin and Australian stock indices for the sample period between 23 April, 2011 and 19 April, 2021. Findings The results show that Bitcoin offered weak safe haven and hedging benefits when combined in a portfolio with S&P/ASX 200 Financials index, S&P/ASX 200 Banks index or S&P/ASX 300 Banks index. In regard to the S&P/ASX All Ordinaries Gold index, the authors found Bitcoin a risky candidate with inconsistent safe haven and hedging benefits. Against S&P/ASX 50 index, S&P/ASX 200 index and S&P/ASX 300 index, Bitcoin was nothing more than a diversifier. The outset of the second COVID-19 wave, which was comparatively more severe than the first, is also reflected in the results with considerably higher correlations. Originality/value There is a lack of in-depth empirical evidence on the safe haven capabilities of Bitcoins for various Australian stock indices during the first and second waves of the COVID-19 pandemic. The study bridges this void in research.

POSSIBILITY OF RUSSIAN BONDSPOTFOLIO DIVERSIFICATION THROUGH THE USE OF ISLAMIC BONDS DURING THE COVID-19 PANDEMIC

Диверсификация - один из важнейших элементов в инвестиционной деятельности. Инвесторы пытаются найти баланс при формировании портфеля и его реструктуризации, стремясь одновременно максимизировать доходность и минимизировать риски. Целью данной работы является оценка возможности диверсификации портфеля облигаций российского рынка с помощью включения альтернативной традиционным облигациям формы - сукук в условиях пандемии COVID-19. Представленный в статье анализ такой возможности составляет определенный элемент новизны. В качестве наиболее подходящей модели для корреляционного анализ выбрана «DCC-MGARCH» модель (динамическая модель авторегрессионной условной гетероскедастичности). Результаты исследования показывают, что инвесторы, предпочитающие долговые суверенные ценные бумаги России и корпоративные облигации российских компаний, имеют возможность диверсифицировать портфель путем включения исламских облигаций. Данный вывод объясняется наличием отрицательной корреляционной связи между индексом сукук и индексами российских облигаций, как корпоративных, так и суверенных. Diversification is one of key elements in investment management. Investors strive to find a balance in the formation of a portfolio and its restructuring, simultaneously maximizing profitability and minimizing risks. The purpose of this work is to assess the possibility of diversification of the Russian bonds portfolioby including an alternative to traditional bonds-sukuk. The DCC-MGARCH model (Dynamic Conditional Correlation Multivariate General Autoregressive Conditional Heteroscedasticity Model) was chosen as the most suitable model for correlation analysis. The results of the study show that investors who prefer Russian sovereign debt securities or corporate bonds of Russian companies couldeffectively diversify their portfolio by including Islamic bonds during the COVID-19 pandemic. This conclusion is explained by the presence of a negative correlation between the Dow Jones Sukuk Index as a proxy for sukuk market and the indices of Russian bonds, both corporate and sovereign.

Price Volatility Transmission in China’s Hardwood Lumber Imports

Hardwood lumber is the principal part of the global hardwood timber trade. China has become the largest importer of hardwood lumber in the world. However, China’s hardwood lumber imports are affected by price volatility. Thus, we investigated the price volatility transmission of China’s hardwood lumber imports. We aimed to detect the source, path, and intensity of the volatility transmission in China’s hardwood lumber imports, and reveal the intrinsic interactions between price volatilities. To date, there is little research on the price fluctuations of forest products. This paper provides an empirical analysis on the volatility transmission in China’s forest product imports. We selected four types of major hardwood lumber imports to China; that is, teak (Tectona grandis L.F.), merbau (Merbau), sapele (Entandrophragma), and casla (Terminalia spp.) (The Latin names of tree species are given in parentheses), and used their daily prices from 4 August 2010 to 15 April 2020. The Baba–Engle–Kraft–Kroner (BEKK) multivariate models and dynamic conditional correlation (DCC) models were employed. The empirical results indicate that there is an intrinsic relationship between the price fluctuations in China’s hardwood lumber imports. The volatility transmission chain originates from casla; it is transmitted along the casla→sapele→merbau→teak pathway. The direction of transmission is from lower prices to higher prices. The dynamic conditional correlation of each link in the chain does not exhibit any particular time trend. This suggests that volatility transmission is a crucial price mechanism in China’s hardwood lumber imports. Our findings have important policy implications for hedging timber price risks and designing timber trade policies.

Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach

This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data Sampling (MIDAS) methods. We compare our results to those obtained by employing two well-known models that do not account for the COVID-19 low-frequency variable, namely the Dynamic EquiCorrelation (DECO) and corrected Dynamic Conditional Correlation (cDCC). Moreover, we consider four possible specifications of the volatility: GARCH, GJR, GARCH-MIDAS, and Double-Asymmetric GARCH-MIDAS. The empirical results show that our approach is statistically superior to other models and represents a valuable methodology that can be used for risk managers, investors, and policy makers to assess the effects of the pandemic on spillovers effects in energy markets.