Dollar Index Research Articles

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin’s pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.

An Analysis on the Divergence of Monetary Policies Between China and the United States and Its Impact on the Trend of RMB Exchange Rate

The monetary policies of China and the US have differed due to their differing economic circumstances and stages of growth, which has had a significant impact on the RMB exchange rate's trend. This paper uses empirical analysis to examine the three channelsmarket expectations, China's foreign trade, and international capital flowsthrough which the monetary policy divergence between the two countries influences the RMB exchange rate. It also provides a detailed introduction to the historical development and current state of the policy divergence between the two countries.The findings demonstrate how market expectations are impacted by China and the US's divergent monetary policies, which first alter the US dollar index and interest rate disparity before influencing the direction of the RMB exchange rate. Finally, relevant suggestions are put forward on how to keep the RMB stable while addressing the strain on China's monetary policy brought on by the US and China's divergent monetary policies.

Cryptocurrency hacking and trader behavior in bitcoin futures

We investigate the effect of cryptocurrency hacking on trader positions in CME bitcoin futures. Our results indicate that the occurrence of hacking and fraudulent incidents lead to more net-short positions and fewer participation ratio for leveraged funds in bitcoin futures. Additionally, U.S. dollar index, futures basis, and price efficiency also play significant roles in the trading of leveraged funds. Lastly, we find that the number of hacking incidents negatively affects dealers’ net-long positions. This study provides new insights into trader behaviors in bitcoin futures and offers practical implications for bitcoin investors and regulators.

Forecasting Crude Oil Price Using Multiple Factors

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models.

A novel approach to Predict WTI crude spot oil price: LSTM-based feature extraction with Xgboost Regressor

This paper presents a novel model based on LSTM to predict future prices of WTI crude oil. The WTI price forecasting utilizes data on spot gold price, US 10-year bond yield, global economic activity, and US dollar index from January 1986 to May 2023. The model's performance is assessed using measures such as MAE, MSE, RMSE, MAPE, and R2 metrics. The results generated by the proposed new model are compared to those of the existing machine and deep learning methods, and it is observed that the new model performs better than the existing models in all statistical tests. The study further examined the decision-making processes of the model using SHAP analysis and assessed the individual contribution of each feature to the model's predictions. The correlation between the US Dollar Index and Gold prices and WTI crude oil prices is evident. The SHAP research has demonstrated that the model effectively captures complicated economic linkages and enhances the accuracy of forecasts. The results of this study enhance the development of models that are capable of predicting results, even in times of significant instability, such as economic crises. Using sophisticated data analytics and AI methods would improve the efficiency of energy market oversight.

Dollar and government bond liquidity: Evidence from Korea

Using unique tick-by-tick data from an exchange, this paper examines the relationship between the US dollar and liquidity in the Korean government (Treasury) bond market. We find that a strong US dollar deteriorates the Treasury market's liquidity by increasing the bid-ask spread and the price impact and lowering market depth. The effects of fluctuations in the broad US dollar index on Treasury market liquidity become more pronounced when funding liquidity conditions are tighter, when banks' total capital ratio is lower with greater foreign currency risk, or when there is a larger sell-off of Korea Treasury bonds by foreign investors. The empirical evidence supports the financial channel of exchange rates affecting Treasury market liquidity. In particular, a strong dollar as a barometer of global financial conditions is likely to limit the market intermediation capacity of emerging market dealers and thus tighten emerging market conditions.

Currency market volatility during the COVID-19 pandemic

The currency market is one of the main markets of the world's modern financial and economic systems. With respect to the availability of information, this market is the most data-rich and public. These two features allow it to be viewed as volatile. This study aims to assess the dependence of the currency market's volatility on the conditions and factors that formed during the COVID-19 pandemic. The research used statistical and correlation analysis and general scientific methods. Correlation analysis revealed an insignificant effect of the dollar index on the change in currency market volatility during the COVID-19 pandemic (r = 0.09). The outcomes indicated the impact of the disease factor on currency trading at the 0.8% level (r2 = 0.0081). The research conclusions suggest that the effects of information on the currency market differed during 2015-2019 and deviated in response to various political and economic events. The study highlights that the COVID-19 pandemic and its aftermath did not significantly influence the currency pairs' volatility. In the future, if crisis trends re-emerge, the findings of this study may influence the behavior of investors and participants in global trade. This could affect their investment decisions, currency reserve diversification, insurance strategies, and capital migration.

Comparative Analysis of Gold, Art, and Wheat as Inflation Hedges

This study confirms gold’s role as a reliable inflation hedge while introducing new insights into lesser-explored assets like art and wheat. Using advanced methodologies such as the ARDL framework and LSTM deep learning, it conducts a detailed analysis of inflation-hedging dynamics, exploring non-linear relationships and unexpected inflation impacts across various asset classes. The findings reveal complex dynamics. Gold demonstrates strong long-term inflation hedging potential. The negative coefficient for the US dollar index suggests that gold acts as a hedge against currency depreciation. Furthermore, a positive relationship between gold returns and inflation during high inflation periods highlights its effectiveness in protecting purchasing power. Art presents a more intricate picture. Long-term analysis suggests a weak mean-reverting tendency, but a negative relationship with inflation, potentially linked to economic downturns. Interestingly, unexpected inflation positively correlates with art returns in the long run, hinting at its potential inflation-hedging abilities. No statistically significant connection between wheat prices and overall inflation was observed; the short-run analysis reveals a dynamic interplay between inflation, real GDP growth, and wheat prices at different time points.

The time-frequency-quantile causal impact of Cable News-based Economic Policy Uncertainty on major assets returns

ABSTRACT After the collapse of the equity market in the early 2000s, the question of the drivers of financial assets returns preoccupied the interest of investors and policymakers in financial markets. Thus, this study explores how newly developed Cable News-based Economic Policy Uncertainty (TVEPU) predicts major assets returns using daily data from 1 January 2014 to 30 September 2023. To achieve this objective, we introduced the Rolling Windows Wavelet Quantile Granger Causality (RWWQGC) test. The empirical results show that TVEPU tends to have predictive power for SP500 across time, frequency, and quantile. The results also show that TVEPU has a strong causal impact on major financial assets returns across time, frequency, and quantile. However, the predictive power of TVEPU for the US 10-year bond, US dollar index, and Bitcoin is weak across time, frequency, and quantile. Based on these results, policy recommendations are offered.

Prediction of values of Borsa Istanbul Forest, Paper, and Printing Index using machine learning methods

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results.

An AFD-based adaptive impulse response model of time series

In this paper, an adaptive impulse response model of time series is proposed based on adaptive Fourier decomposition (AFD), which characterizes the data-driven impulse response model with “energy-resolution” and is applied to the effect analysis of shocks on time series. First, according to the observed time series, the frequency points of impulse response function are estimated, and the approximate expression of impulse response function in frequency domain is given. Second, AFD is carried out on the approximate impulse response function level by level, and the poles and coefficients of basis function of each level are obtained adaptively by using the criterion of energy maximization. Third, when the goodness of fit is greater than a certain threshold, all decomposition levels are determined and the adaptive impulse response model is established. Each decomposition level depicts the energy-based resolution characteristics of the impulse response model. Based on gold price return and US dollar index return in daily frequency from 2017 to 2022, this paper carried out AFD-based effect analysis with energy-resolution year by year. Results show that the proposed AFD-based adaptive impulse response model in this paper can describe the persistence effect of shocks of gold price on the US dollar index, and further measure the plainness of linkage relationship between these two time series. Decomposing impulse response to each decomposition level reveals consistence, convergency, and sufficiency of the impulse response model. The AFD-based adaptive data-driven model provides a new way for the analysis of time series in time-frequency domain.

Predicting Bitcoin Price: Comparative Analysis of Machine Learning and Deep Learning Models

Bitcoin has become a prominent financial instrument in recent years, attracting increasing attention as a digital currency. Accurately forecasting the valuation of a financial asset carries substantial significance for both retail and institutional investors. The aim of this study is to evaluate and compare the predictive capabilities of various models, namely Support Vector Regression (SVR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a hybrid model combining CNN and Bidirectional LSTM (CNN-BiLSTM), and XGBoost, in the context of forecasting Bitcoin price. The main aim of this study is to ascertain the algorithm that demonstrates the most efficacy in forecasting the price of Bitcoin. This study utilizes the S&P500 index, Gold/Dollar exchange rate, West Texas Spot Oil Price, and Dollar Index as exogenous factors in order to forecast the price of Bitcoin. The dataset encompasses a consecutive time span of 2191 days, commencing on January 1, 2015 and concluding on September 18, 2023. The models outlined in the study undergo a two-stage procedure, including of training and testing. The assessment of the models' performance was carried out by utilizing several statistical measures, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). The results indicate that the XGBoost algorithm had greater performance in projecting the price of Bitcoin, as evidenced by its consistently higher performance metrics across all evaluated aspects. The XGBoost model was succeeded by the CNN-BiLSTM, CNN, and LSTM models, which are hybrid methodologies, resulting in the most advantageous results. The SVR model demonstrated the least favorable performance..

Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH

The objective of this paper is to assess the dynamic volatility connectedness between fossil energy, clean energy, and major assets i.e., Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500 from September 17, 2014 to October 11, 2022. The main motivation of the study relates to examining the dynamic volatility connectedness mentioned during periods of important events such as the recent coronavirus pandemic and the Russia–Ukraine conflict which has shown the vulnerability of economic and financial assets, energy commodities, and clean energy. The novel Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) approach is employed for the investigation of the sample period mentioned. Empirical analysis reveals that both the total and net volatility connectedness between assets is time-varying. The highest connectedness among the assets is observed with the onset of the coronavirus (COVID-19) pandemic, and it increases with some important international events, such as the Russia–Ukraine conflict, the referendum of Brexit, China–US trade war, and Brexit day. On average, the result shows that 32.8% of the volatility in one asset spills over to all other assets. The DCC-GARCH results also indicate that crude oil, bonds, and Bitcoin act as almost pure volatility transmitters, whereas the Dollar index, gold, and S&P500 act as volatility receivers. On the other hand, clean energy is found neutral to external shocks until the first quarter of 2020 and after that time, it starts to behave as a volatility transmitter. Based on the obtained results, we offer some specific policy implications that are beneficial to the US economy and other countries.Graphical Dynamic volatility connectedness between fossil energy, clean energy, and major assets (Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500)

Insights into the dynamics of market efficiency spillover of financial assets in different equity markets

This paper provides multidisciplinary insights into the dynamics of market efficiency spillover of financial assets, considering Bitcoin, DXY (U.S. Dollar Index), S&P 500, and crude oil in different equity markets. Given this, we employ the multifractal detrended fluctuation analysis (MF-DFA) and the time-varying parameter vector autoregressive models (TVP-VAR) to illuminate these complex interplays between these assets and other equity markets. Our findings display that Bitcoin acts as a net transmitter, whereas DXY functions as a net receiver. Additionally, Bitcoin, DXY, and S&P 500 clearly exhibit dynamics in information transmission, whereas crude oil does not. It means that the low connectedness of the oil market in terms of market efficiency spillover, suggesting that the oil market may play an effective role as a hedging instrument for other assets.

Can NFTs hedge the risk of traditional assets after the COVID-19 pandemic?

Our study analyzes the dynamic spillover effects between the NFT market, Bitcoin market, oil market, gold futures market, S&P 500 stock index, bond market, and US dollar index over three time periods: the full sample period (2017/6/23–2022/9/2), before the COVID-19 outbreak (2017/6/23–2020/1/12), and after the COVID-19 outbreak (2020/1/13–2022/9/2) by employing the DCC-GARCH-based connectedness model. Furthermore, we apply the DCC-GARCH-t-Copula model, Risk Parity Portfolio (RPP) model, and Minimum Connectedness Portfolio (MCoP) model to evaluate the bilateral dynamic hedge ratios, portfolio weights, and the multivariate portfolio performance of these financial assets, respectively. Our findings suggest that NFTs are a source of volatility spillovers during both periods and that the COVID-19 outbreak accelerates the speed and intensity of volatility spillovers from NFTs to traditional financial markets. Moreover, most of the volatility spillovers from NFTs are caused by own shocks, which implies that NFTs are not susceptible to financial risk contagion. The results of bilateral portfolio analysis show that NFTs can effectively hedge other traditional financial assets when used as long positions. The results of multivariate portfolio analysis demonstrate that NFTs can also reduce investment risk despite their smaller weighting. In the two months after the COVID-19 outbreak, MCoP slightly outperforms RPP, although RPP outperforms MCoP in general.

Diversification, hedging, and safe-haven characteristics of cryptocurrencies: A structural change approach

This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCC–ARMA–GARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-à-vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 Russia–Ukraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.

Volatility and return spillovers between private equity buyout, venture capital and major financial markets

ABSTRACT This study investigates the volatility and return spillovers between Private Equity Buyouts (PE) and Venture Capital (VC), the equity market, precious metals, real estate, and the Dollar index, which has been widely ignored in the literature. We analysed daily data from 2 January 2004 to 29 December 2022 using Diebold and Yılmaz's (2012) framework. Our data covers the global financial crisis and the COVID-19 period and is rich enough to examine the linkages between the mentioned financial markets thoroughly. PE's higher connectedness with the equity market than VC’s is the main finding of this research. Besides, PE and VC have low connectedness to precious metals, real estate, and the Dollar index. In the sample period, VC is a net receiver, while PE is a net transmitter of volatility and return spillover. An exciting finding is that during the COVID-19 pandemic, VC experienced lower volatility spillover than PE from the broad market proxied by S&P 500. Investors and portfolio managers can benefit from our results to better diversify their portfolios.

Investor Behavior in Gold, US Dollars and Cryptocurrency during Global Pandemics

COVID-19 and SARS are epidemics which have influenced the largest global economic crisis in recent years. This research reveals that both SARS and COVID-19 have led to fluctuations in the prices of gold and the US dollar index; however, there is no direct causal relationship be-tween COVID-19 and the price of bitcoin. The USD index saw a significant increase during the SARS outbreak, while gold prices surged during the COVID-19 pandemic. The notion that cryptocurrency will surpass the value of gold or traditional currencies seems improbable, given the lack of evidence linking bitcoin prices to COVID-19. Gold is expected to maintain its value in the long term, offering lower risk compared to other currencies.

Agricultural commodities market reaction to COVID-19

This study examines the volatility transmission mechanism in agricultural futures returns during crisis periods, with a specific focus on the COVID-19 pandemic. The research employs the Markov Switching model to analyze the market dynamics of key agricultural commodities—cotton, sugar, rice, wheat, and corn. The study period is divided into three phases: Pre-COVID, COVID, and Post-COVID, allowing for a comprehensive analysis of market trends and shifts in response to the pandemic. Key findings reveal significant disruptions and heightened volatility in these markets during the COVID-19 period, with notable deviations from traditional market behaviors. The research reveals how various macroeconomic factors, such as the S&P 500, US 10-year Treasury bills, Brent crude oil prices, and the US Dollar Index, distinctly affect these commodity markets. Crucially, it sheds light on the dual nature of these markets, demonstrating both their resilience and susceptibility to worldwide disruptions, along with their recovery patterns following the pandemic. The policy implications of these findings are crucial for investors and policymakers. The study provides insights into managing and mitigating the impacts of global crises on essential commodities, emphasizing the need for robust strategies to navigate future market uncertainties. Additionally, the research contributes to the broader discourse on the interplay between macroeconomic conditions and agricultural market dynamics, offering valuable perspectives for informed decision-making in these sectors.

Creating a cutting-edge neurocomputing model with high precision

The prediction of oil prices has a significant impact on the economies of countries, particularly in oil-rich nations like Iraq, and affects the labor market. Prediction techniques are vital tools for extracting knowledge from complex databases, such as oil prices. This study aims to develop a prediction model that accurately determines oil prices based on seven fundamental characteristics, including Date, WTI, GOLD, SP 500, US DOLLAR INDEX, US 10YR BOND, and DJU. The proposed model utilizes advanced neurocomputing techniques that analyze the seven features over a ten-year period. The model comprises three main stages: preprocessing, determining feature importance through computing correlation, entropy, and information gain, and splitting the dataset into training and testing. The first part of the dataset builds the predictor called Hybrid Model to Oil Price based on Neurocomputing Techniques, while the second part evaluates model using three error measures: R2, MSE, and MAE. The model proves its ability to provide accurate predictions with low error rates. Multivariate analysis shows that WTI, GOLD, and US DOLLAR INDEX have a more significant impact on oil prices, with information gain values of WTI = 11.272, GOLD = 11.227, and DJU = 11.614. The Gate Recurrent Unit neurocomputing technique demonstrates its ability to handle datasets with features that behave differently over multiple years and provides accurate predictions with low errors in a short time, withR2 = 0.945, MSE = 0.0505, and MAE = 0.1948. This study provides valuable insights into the prediction of oil prices and highlights the efficacy of advanced neurocomputing techniques for extracting knowledge from complex databases.