Bitcoin and money supply relationship: An analysis of selected country economies

Bitcoin has become quite known after the 2008 economic crisis and the COVID-19 health crisis. For some, these cryptocurrencies constitute rebellion against the existing system as governments encourage uncontrolled expansions in the money supply;for some others, it is a quick source of income. Undeniably, the volume of the crypto money market has grown considerably in recent years, regardless of the reasoning of the people who invest and trade in this field. At this point, one of the most important questions to be investigated is "what variables have caused the tremendous growth in the crypto money quantities in recent years?" This study tests the assumption that changes in cryptocurrencies are affected by changes in national currencies. Thus, the Bitcoin price is the dependent variable, and M1 monetary supply changes in the USA, European Union and Japanese economies are considered independent variables. The variables in this study were tested using the time-varying Granger causality method. The results obtained from this study confirm the philosophy of Bitcoin's emergence and the possibility that it can be a hedge against the inflationary effects of money, especially after the COVID-19 pandemic.

Scenario design for macrofinancial stress testing

This paper provides a novel empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables. The scenario design framework is composed of several building blocks. First, multiple scenarios on the risk factors are generated by simulating a multi-country large Bayesian vector autoregression. Second, we take the perspective of a representative investor who aims to select a severe-yet-plausible scenario for a set of systematic risk factors following a factor-investing strategy. Moreover, we compare the stress scenarios selected under different approaches to measure plausibility (the Mahalanobis distance and entropy pooling under subjective views with a clear economic narrative). Finally, we compare our scenario design approach with a historical scenario approach in terms of its ability to select a stress scenario in the run-up to a rare adverse event such as the Covid-19 pandemic. We give evidence that our framework is suitable for the selection of a proper forward-looking severe-yet-plausible macrofinancial stress scenario.

Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach

This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely;Gold and Silver from the category of precious metals;Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals;Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations. © 2023 Elsevier Ltd

Frequency connectedness and cross-quantile dependence among medicare, medicine prices and health-tech equity

We investigate the dynamic connectedness among health-tech equity and medicine prices (producer and consumer) and Medicare cost indices for the US market. In doing so, we apply Cross-Quantilogram Dynamic Connectedness based on Time-Varying Parameter Vector Autoregression (TVP-VAR) approaches to analyse historical high-frequency time-series data. TVP-VAR results show that health-tech equity is the highest volatility transmitter while Medicare price is the highest volatility receiver. We also find medicine producer price is the net volatility contributor while the retail price of medicine is the net volatility receiver. The Cross-Quantilogram analysis confirms a strong bivariate quantile dependence between respective markets at a higher quantile of each market. Cross-quantilogram demonstrates a higher level of connectedness among the markets when considering medium and long memory. We observe health-tech equity turned to be a profound volatility contributor, while medicine price (both producer and retail prices) and Medicare appeared to net volatility receiver during the time of COVID19 Pandemic. The financial performance of health-tech equity returns elevates the price volatility of medicine and eventually Medicare cost, which imply that equity return should be incorporated forming medicine prices. © 2022 Elsevier Ltd

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

Nowcasting in a pandemic using non-parametric mixed frequency VARs

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. © 2020 The Author(s)

Fiscal deficits and the socioeconomic consequences of rebalancing: Insights from a TVP‐VAR with stochastic volatility

This article connects two salient economic features: (i) Fiscal shocks have asymmetric effects across business cycle phases (Gechert, Horn, & Paetz, 2019);(ii) the unemployment-output trade-off is time varying and may be unstable. The intertwined dynamic behaviour of fiscal deficit shocks and the unemployment-output trade-off is studied in this article using a time-varying parameter (TVP) vector autoregression (VAR) with stochastic volatility techniques applied to the analysis of data from Canada, France, Germany, Japan, Spain, Sweden, United Kingdom and the United States of America. We confirm the trade-off heterogeneity across country, and its time-varying nature across time, showing in addition its fluctuation around a long-run reference value. We document significant short-run impacts of fiscal shocks on the unemployment-output trade-off which, based on the experience of the Global Financial Crisis, becomes larger in periods of economic turmoil. Policy-wise, the rebalancing of public finances may have unexpected adverse effects on job creation if implemented during slumps, precisely when the labour market sensitivity with respect to the performance of the product market is likely to be more acute. This message is particularly relevant in the aftermath of the Covid-19 pandemic.

Forecasting in the absence of precedent

We survey approaches to macroeconomic forecasting during the COVID-19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real-time forecasts and updating forecasts as new data were observed. We revisit these themes with a time-varying parameter (TVP) vector autoregression (VAR), which attributes the large jumps primarily to increased volatility rather than changes in the type or propagation of shocks.

UNDERSTANDING ECONOMIC INSTABILITY DURING THE PANDEMIC: A FACTOR MODEL APPROACH

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model’s forecasting performance and extract some instability measures based on the factor model’s eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability. © 2022 by Emerald Publishing Limited.

Forecasting Vaccination Growth for COVID-19 using Machine Learning

In this digital era, machine learning (ML) is becoming more common in the healthcare industry. It plays many essential roles in the medical field including clinical forecasting, visualization, and even automated diagnostics. This paper focuses on the future prediction of COVID-19 vaccination rates in different countries. Considering how destructive the novel Coronavirus has been and its continuous mutation and spread, clinical interventions such as vaccines serve as a ray of hope for many individuals. As of 2021, an estimated total of 8,687,201,202 vaccine doses by numerous biopharmaceutical manufacturers have been administered worldwide [1]. This study intends to estimate the probable increase or decrease in global vaccination rates, as well as analyze the correlation between future trends of daily vaccinations and new COVID-19 cases, along with deaths and reproduction rates. Three models were utilized in forecasting and comparing the overall prediction toward the COVID19 vaccine rates;Auto-Regressive Integrated Moving Average (ARIMA), an ML approach, Long-Short Term Memory (LSTM), an artificial Recurrent Neural Networks (RNN), and Prophet which is based on an additive model. The Vector Autoregression (VAR) model will also be utilized to compare COVID-19 cases, deaths and reproduction rates to that of COVID-19 vaccine growth. ARIMA resulted to be the best model, while Prophet turned out to be the worst-performing model. In general, our comparison of employing the ARIMA model vs the other three results in the conclusion that adopting this method shows to be a more effective approach in projecting vaccination growth in the future. Furthermore, a visible increase in future daily vaccinations can be seen to be correlated with the increase in COVID-19 cases, deaths reproduction rates, and a fluctuating trend in COVID-19 deaths. © 2022 IEEE.

Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.

The Study of Multivariable Autoregression Methods to Forecast Infectious Diseases

Infectious diseases can have an enormous impact on the public because they negatively affect not only mortality but also unemployment and other social impacts. It is crucial to anticipate additional resources to counter infectious diseases mathematical and statistical tools that can be used to generate forecasts of reported cases. In this paper, the multivariable autoregression methods were compared for forecasting infectious diseases. We discuss the methods and use them to forecast infectious diseases. In this case, we used several COVID-19 cases as the object of forecasting. We used three prediction methods as Vector Autoregression (VAR), Vector Autoregression Moving Average (VARMA), and Autoregression Moving Average with exogenous variable (VARMA-X). The results show that the models have different results, among three methods, VAR give the best result of forecasting daily covid case for both stationary and non-stationary data. While VARMA-X shows the lowest performance for forecasting the dataset. We suggest by combining the AR model with the ANN model can provide a better result for forecasting. © 2021 IEEE.

Measuring labor supply and demand shocks during COVID-19.

We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages. Most sectors were subject to large negative labor supply and demand shocks in March and April 2020, with substantial heterogeneity in the size of shocks across sectors. Our estimates suggest that two-thirds of the drop in the aggregate growth rate of hours in March and April 2020 are attributable to labor supply. We validate our estimates of supply shocks by showing that they are correlated with sectoral measures of telework.

The global effects of Covid-19-induced uncertainty.

We estimate a VAR with world-level variables to simulate the effects of the Covid-19 outbreak-related uncertainty shock. We find a peak (cumulative over one year) negative response of world output of 1.6% (14%).