US Dollar Index Research Articles

The Impact of Trading Information Sets on Exchange Rate Change and Volatility: Evidence From Taiwan

This study explores the Taiwan Dollar (TWD) as the currency of a small island economy, uses the trading information sets from overseas and the market itself to examine the impacts on the adjustment of daily spot exchange rates. The daily USD/TWD is explained by the trading information sets, contain which the daily trading activities and the ratio of the real body on the daily candlestick chart of technical analysis on the Taipei Foreign Exchange Market, as well as the US-dollar index return to explain the USD/TWD spot rate change. The results showed that some of the USD/TWD changes were related to the US-dollar index return on overseas, and that the effect of the US-dollar index return was not limited to the adjustment rate from the previous closing rate to the opening rate on the day, which would affect the adjustment spot exchange rate in the intraday opening-to-closing period. There is a significant positive relationship between the real body ratio of the daily candlestick chart and the return of the exchange rate, supporting the real body ratio related to the change of the exchange rate. The study model can greatly improve the model interpretation ability of the change of exchange rate by about 50% after considering the trading activity factors. Finally, this study found that the volatility has a positive effect on Mondays and the 2008-financial crisis, and based on the shock that the news of depreciation was higher than the news of appreciation, so there exist asymmetry volatility.

An Analysis of Inter-Relationship among Commodities, Stock and Economic Indices

This research will concentrate primarily on commodity relationships, mainly, prices of oil (OP) and gold (GP), US stock market (S&P500), consumer confidence index (CCI), the US Dollar index (USDX), and the industrial production (IP). The purpose of the analysis is to study the dynamic interconnection between OP, USDX, CCI, GP, IP, and S&P500, by estimating the Vector Auto Regression (VAR) model. OP, CCI, GP, USDX, S&P500, and IP are the different variables used in this paper. Using monthly data from January 1971 to May 2020, this study applies the Granger causality test, Variance Decomposition (VDC) analysis, and Impulse Response Function (IRF). It can be inferred from the results that USDX has a significant relationship with GP and has a causal impact on GP. Industrial Production has also shown a significant relationship with S&P500 and has a causal impact on S&P500. The result also suggested that CCI and S&P500 share a unidirectional relationship; the volatility in CCI in the short run is due to the S&P500. Also, the variables do not have any other significant relationship. The findings also highlighted that USDX directly affected GP negatively. Industrial production directly impacted S&P500 in the short run, while a positive relationship is shared between CCI and S&P 500.

Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index

We find the number of initial public offerings (NIPO) a simple but efficient oil price predictor, and provide new evidence that U.S. dollar index (USDX) also has significant out-of-sample forecasting power on oil price. Furthermore, we reveal the highly complementary relation between NIPO and USDX in forecasting oil price and explain it from the points of linear correlation and economic transmission mechanism. With the help of multivariate prediction methods, both the statistical and economic performance of NIPO and USDX can be tremendously boosted. The oil predictabilities of NIPO and USDX partially stem from the forecasting power on oil market sentiment and inflation, respectively, and they can be further enhanced using appropriate nonlinear model.

Analysis of oil price fluctuation under the influence of crude oil stocks and US dollar index — Based on time series network model

This paper uses the high-frequency oil price after ensemble empirical mode decomposition, combines crude oil stocks and the US dollar index to construct co-movement threshold networks according to different periods divided by multiple structural breakpoint method. The results show that in the period of sharp fluctuations in oil prices, the inverse relationship between the US dollar index and high-frequency oil prices is more obvious, and the co-movement network of the three is closer to the “small world network”. After oil prices returned to stable fluctuations, coupled with the weakening role of the United States in leading the global economy, the reverse relationship between the US dollar index and high-frequency oil prices has gradually weakened, and the reverse relationship between stocks and high-frequency oil prices has become increasingly prominent. The research not only reveals the main co-movement characteristics of the three, but also provides a new idea for future research on oil price fluctuations and its influencing factors.

Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison

As the COVID-19 outbreak became a global pandemic, traditional financial market indicators were significantly affected. We examine the price efficiency and net cross-correlations among Bitcoin, gold, a US dollar index, and the Morgan Stanley Capital International World Index (MSCI World) during the four months after the World Health Organization officially designated COVID-19 as a global pandemic. Using intraday data, we find that Bitcoin prices were more efficient than the US dollar and MSCI World indices. Using a detrended partial-cross-correlation analysis, our results show that net cross-correlations vary across time scales. Our results suggest that when the time scale is greater than two months, gold can be considered as a safe haven for investors holding the MSCI World and US dollar indices and when the time scale exceeds three months, Bitcoin can be considered a safe haven for the MSCI World index.

How Vulnerable Are Financial Markets to COVID-19? A Comparative Study of the US and South Korea

In this study, we carry out a comparative analysis between the US and South Korea, with a special attention to three key areas, including the stock market, the currency market, and the bond market. By employing a composite model, VAR-GARCH-BEKK, we will attempt to capture both mean and volatility spillovers between the pandemic and financial markets, so as to explore the extent and ways in which the COVID-19 pandemic influences the financial sector. The empirical results provide substantial evidence in the following areas: (i) South Korea seems more vulnerable since all of its financial markets are seen to be statistically associated with the growth in infections. (ii) For the US, only the stock market is negatively impacted by the confirmed cases in terms of a conditional mean spillover model. (iii) According to the impulse response functions (IRFs), apart from the US dollar index, both the TED spread and stock returns respond significantly to innovations from the pandemic. (iv) There is little evidence to support the presence of volatility transmission from the pandemic to the financial markets in the two countries.

The content analysis of Russian finance

Subject. The article focuses on the Russian finance. Objectives. I determine the basics and conditions needed for the Russian finance. Methods. The study is based on the systems approach and the method of statistical, neural network and cluster analysis. Results. Having evaluated growth rates of prices for basic commodities and quotations of the Russian stocks, I determined what underlies the Russian finance as the prevailing trend in Rosneft’s stocks and Urals oil futures. Observing the movement of RTSI, IMOEX, S&P500, WTI oil future, USD/RUB rate, I discovered the gap between IMOEX and RTSI. RTSI remains with the WTI oil futures trend, while IMOEX joined the trend in S&P500. Having analyzed the importance of growth rates of global indices, I understood what is required for their maximum, i.e. the lowest growth rates of RTSI and the highest FTSE100. Considering the global indices and their growth rates, the Russian finance will be viable if RTSI indices are associated with DJIA and US Dollar Index. Structurally, the Russian economy cannot ensure the direct association of RTSI and DJIA. RTSI gets associated with DJIA through S&P500. US Dollar Index is a leading components in this correlation, as it determined the dynamics of USD/RUB and IMOEX. As for the trend in the rate of principal currencies, the basket with USD and CNY seems to be acceptable for the financial regulator. Conclusions and Relevance. The content analysis reveals the threatening intensification of adverse factors that make the Russian economy dependent on oil production, and outlines what can be done to eliminate them. The findings constitute new knowledge and advance the competence of the financial market regulator to make administrative decisions concerning the allocation, reallocation of the public product value and a part of national wealth so as to maintain the Russian finance in terms of form and substance.

Structural analysis and forecast of gold price returns

Gold has multiple attributes and its price is affected by various factors in the market. This paper studies the dynamic relationship between the gold price returns and its affecting factors. Then we use the STL-ETS, neural network and Bayesian structural time series model to predict the gold price returns, and compare their performance with the benchmark models. The results show that the shocks of crude oil returns and VIX have the positive effect on gold price returns, the shocks of the US dollar index have the negative effect on gold price returns. And the fluctuation of gold price returns mainly depends on crude oil price returns shocks. STL-ETS model can accurately fit the fluctuation trend of the gold price returns and improve prediction accuracy.

A Risk Measurement Model of China’s Non-Ferrous Metal Futures Market

With the impact of foreign exchange markets, risks in financial markets are becoming more complex and diversified, which underlines the importance of risk management in financial supervision. In this paper, China’s non-ferrous metal futures market is selected as the research object, and Shanghai Futures Exchange’s Industrial Metal Commodity Index (IMCI) data are used to measure risk using the conditional autoregressive value at risk (CAViaR) model. The US dollar index (USDX) is incorporated into the CAViaR model to study its impact on the risk. Through empirical analysis, we arrive at the following conclusions: First, the asymmetric slope CAViaR model (AS-CAViaR) is more suitable for measuring the risk in China’s non-ferrous metal futures market. Second, the risk is positively impacted by the lagged risk. Moreover, the impacts of positive and negative returns on the risk are asymmetric, with a negative return having a greater impact. Third, the positive and negative shock of USDX has significant and different impacts on the risk. These impacts can be caused by global capital flows. In addition, the impact of the vector of explanatory variables on the IMCI at different quantile levels is discussed based on the CAViaR-USDX model, which reflects the comprehensive advantages of the quantile regression method and the model’s applicability. The above conclusions verify the impact of USDX on China’s non-ferrous metal futures market and provide a theoretical basis and direction for risk monitoring.

Statistical properties of the detrended multiple cross-correlation coefficient

Cross-correlations are ubiquitous in nonstationary multivariable systems. By using detrended cross-correlation (DCCA) technique, recently a new multiple cross-correlation coefficient DMC was proposed to quantity the correlation between multivariate and a target variable. We studied the statistical properties of DMC in detail. More specifically, we first proved that 0≤DMC≤1, and then deduced that the DMC is approximately χ2 distributed for overlapping windows with different window size. In addition, the range of DMC was determined numerically by statistical test, where the multiple correlations can be regarded as statistically significant. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on DMC. Finally, by utilizing this statistical test on financial system and environment data, we found that the US dollar index affects international gold spot prices and West Texas Intermediate oil price greatly, and PM2.5 dominates in Beijing’s AQI system.

The impact of COVID-19 on the standard & poor 500 index sectors: a multivariate generalized autoregressive conditional heteroscedasticity model

Purpose This paper aims to examine the impact of the daily growth rate of COVID-19 cases in the USA (COVIDg), the Federal Fund Rate (FFR) and the trade-weighted US dollar index (USDX) on S&P500 index daily returns and its 11 constituent sectors’ indices for the time period between January 22, 2020, until June 30, 2020. Design/methodology/approach The study uses the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model to gauge the impacts over the whole period of study, as well as over two sub-periods; first, January 22, 2020, until March 30, 2020, reflecting uncertainty in the US markets and second, from April 1, 2020, until June 30, 2020, reflecting the lockdown. Findings Results of the MGARCH model reveal a negative and significant relation between COVIDg and S&P500 index daily returns over the first sub-period and the whole study period in the following sectors, namely, communications, consumer discretionary, consumer staples, health, technology and materials. Yet, COVIDg showed a positive and significant relation with S&P500 index daily returns during the second time period in the following sectors, namely, communication, consumer discretionary, financial, industrial, information technology (IT) and utilities. Besides, USDX showed a negative significant effect on S&P500 index daily returns and on the daily return on each of its 11 constituent sectors over the second sub-period and the whole period. Further, FFR showed a significant effect only in the second sub-period, specifically, a negative effect on the daily return of the financial sector and a positive effect on the daily return of the technology sector index. Nevertheless, FFR had a positive significant effect on the daily return of the utilities sector index for the whole period under study. Research limitations/implications The impact of the crisis on the S&P500 index can be assessed only with some limitations owing to available global data and the limited time frame of the lock-down. Practical implications The study proposes supporting a smooth, functioning and resilient financial system; increasing fiscal measures by the US Government to increase liquidity on constraints; measures by The Federal Reserve to alleviate US dollar funding shortages; support market integrity; ensure continuous transparency and sharing of information; support the health sector, as well as consumer-based sectors that faced demand shocks and facilitate investments in the technology sector. Originality/value The originality of this paper lies in the examination of the impact of the novel COVID-19 pandemic on each of the 11 sectors constituting the S&P500 index separately, reflecting how the main economic sectors formulating the US economy reacted to the shock during the peak time of the pandemic to observe a full picture of the economic consequences amid the pandemic.

COVID-19 and financial market efficiency: Evidence from an entropy-based analysis

This study assesses the market efficiency of S&P 500 Index, gold, Bitcoin and US Dollar Index during the extreme event of COVID-19 pandemic. Market efficiency is estimated by a multiscale entropy-based method for the scales of hourly and 1 to 30 business days. At all scales, four markets’ efficiency decreases sharply and persistently during February-March 2020. Market efficiency decreases the most in S&P 500 Index and the least in Bitcoin market. Bitcoin market efficiency is more resilient than others during the extreme event, which is an attractive feature to serve as a safe haven asset.

Analysis of the Russian finance connectivity

As a result of evaluation of growth rates of major commodity prices and Russian share quotes there is discovered dominating dynamics of shares of Rosneft and Urals oil futures. Assessment of dynamics of RTSI, IMOEX, S&P500, WTI futures, USD/RUB showed IMOEX breakdown from RTSI. RTSI remained with the WTI futures, while IMOEX joined S&P500 trend. As a result of neural network analysis of importance of global indices growth rates there is determined a condition of achievement of their maximum value – minimum growth rate of RTSI and maximum rate of FTSE100 growth. Cluster analysis of the global indices in terms of their growth rates revealed connectivity between RTSI, DJIA and US Dollar Index. Russian economy structure can’t ensure direct connectivity of RTSI and DJIA. RTSI is indirectly connected to DJIA via S&P500. The leading role in this connection belongs to US Dollar Index that largely determines the dynamics of USD/RUB and IMOEX. Cluster analysis in terms of major currencies exchange rates growth defined a USD and CNY currency basket that is acceptable for the Russian economy. Analysis of the Russian finance connectivity has sufficiently identified a basis and conditions of its existence in the context of the strengthening negative factors which bind and overburden the Russian economy with oil dependence.

Dynamic Spillover across Uncertainty, Financial and Commodity Markets

This paper investigates the dynamic spillover across uncertainty, financial and commodity markets. We measure the return spillover in US policy uncertainty, financial assets (S&P 500, Treasury bond and US dollar index) and WTI between January 2000 to July 2020. Applying Diebold and Yilmaz (2012), we perform both static and dynamic analysis to measure the total return spillover over our sample period. First, we find total spillover (21.9%) across the uncertainty, financial assets, and WTI. The magnitude of total spillover are intensified in the aftermath of financial crises (the 2008-2009 GFC and 2011-2012 EDC) as well as the COVID-19 outbreak, indicating a reduction in diversification benefits. This finding confirms that financial crises and uncertainty increase the contagion risk. Second, S&P 500 is the biggest net transmitter of spillovers, followed by USDX, while WTI, EPU, and TB are net recipients of spillovers. Finally, following the global financial crisis and the COVID-19 outbreak, the net spillover makes both positive and negative jumps, and the level of net spillovers is more intense. In the COVID-19 outbreak period, S&P 500, TB, and USDX become the transmitters of spillover, while EPU and WTI are the recipients of spillover.

Can We Reckon Bitcoin as a Hedge, a Safe Haven or a Diversifier for US Dollars?

This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.

Exchange Rate Flexibility of Marine Export Trade Based on Artificial Neural Network

Feng, S.S., 2020. Exchange rate flexibility of marine export trade based on artificial neural network. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 641–644. Coconut Creek (Florida), ISSN 0749-0208.In recent years, the issue of exchange rate flexibility has attracted extensive attention in academic sphere. The appreciation of RMB will inevitably lead to the price changes of Chinese seafood in the international market, resulting in an impact on the import and export of Chinese seafood, the growth rate of marine economy and domestic employment. On the basis of artificial neural network, this paper puts forward a research method of exchange rate flexibility of marine export trade and constructs the exchange rate prediction model. Through analyzing the change trend of exchange rate, it can be seen that although RMB is faced with the pressure of devaluation by the rise of the US dollar index and the gradual increase of the interest rate of the Federal Reserve, yet the possibility of sustained large-scale devaluation of RMB is low. The method proposed in this work can realize accurate prediction of exchange rate flexibility. According to the prediction results, the government should take effective measures to mitigate the harm of exchange rate flexibility to the national marine economy.

The impacts of the US dollar index and the investors’ expectations on the AH Premium – a macro perspective

ABSTRACT This paper aims to provide a macro perspective to explanations of the AH premium, that is, the A and H share price differences of cross-listed companies, particularly the surge of the AH Premium Index after 2015: after the initiation of Shanghai–Hong Kong Stock Connect (SH Connect) in November 2014, the AH Premium Index jumped from 115.8 to 126.4, with a significant decline in its variance. We find that the impacts of US Dollar Index shocks dominated the overall changes in the AH premium on an individual equity basis over 2007–2019. Moreover, the US Dollar Index explains 50%-70% of the changes in the AH Premium Index, and investors’ expectations of the Chinese economy add another 10% to the explanatory power. Segregating the impact of the US Dollar Index, the average AH Premium Index only increased 2.7 percentage points, from 98.8 to 101.5. Further, after the initiation of SH Connect, the prices of cross-listed shares have been more responsive to exchange rate information. Over a longer term, SH Connect may have reduced the impacts of the US Dollar Index and increased the impacts of effective prices on equity prices. These findings demonstrate that hedging foreign exchange risks is the main reason for investment in H shares of mainland firms and, as a result, enhancing RMB foreign exchange rate flexibility is the core component as well as precondition for financial market opening in China. When market conditions permit, policy makers and regulators should consider a pilot program allowing investors to arbitrage the H shares of mainland firms with the AH premium in a reasonable range. This may further reduce the AH premium and enable the share prices to converge in the long run.

Dynamic connectedness and portfolio strategies: Energy and metal markets

In this paper, we investigate the volatility spillover effect among the global commodity futures (including both energy and metal futures; global stock markets (covering both Developed and Emerging Markets); the US bond market and the US Dollar index by employing the dynamic connectedness approach of (Diebold and Yilmaz, 2012, 2014) based on the time-varying parameter vector autoregressive (TVP-VAR) model and using daily data for the period from January 3, 1992 to December 27, 2019. Our results indicate a moderate connectedness among the volatilities changing over time and approaching its peak level during 2007/08 global financial crises. In addition, we determine the optimal hedge ratios and portfolio weights for the commodity investors and portfolio managers. Our results indicate that for the equity market volatility investors, the highest hedging effectiveness can be reached by taking short positions in energy futures (such as natural gas), on the other hand for both the US bond and US Dollar volatility investors it can be reached by taking short positions in metal futures (such as gold).

Finansal Piyasalar ve Bitcoin Bağımlılığı: Copula-Garch Yaklaşımı

This paper aims to examine the relationship between Bitcoin and preeminent financial indicators using Copula-GARCH method. In the study, we use closing prices of Bitcoin and US 10-Year Bond Yield, Gold Spot US Dollar, US Dollar Index, S&P 500, FTSE 100 and NIKKEI 225. To our knowledge, our paper is the first to examine this issue empirically. Analysis results show that there is no strong interdependence between Bitcoin and preeminent financial indicators. These findings provide new information that will benefit policy makers, banks, financial investors, and risk managers in trading activities for both long-term and short-term strategies.

Hedging Strategies of Green Assets against Dirty Energy Assets

Previous studies argue that the relationships between clean/green and dirty energy assets are time-varying, but there is a lack of evidence on the hedging ability of clean energy stocks and green bonds for dirty assets, such as crude oil and an energy stock index exchange traded fund (ETF), and the portfolio implications. Furthermore, potential drivers of the dynamics of the hedge portfolio returns are still unknown. To address these research gaps, the authors provide an extensive analysis of the hedging ability of clean/green assets against two dirty energy assets (crude oil prices and energy ETF) using daily data from 3 January 2012 to 29 November 2019. Using corrected dynamic conditional correlation models, the authors model correlation and then compute hedge ratios and hedging effectiveness, which all seem to vary with time. The results from hedging effectiveness indicate that investors should follow a dynamic hedging strategy and that clean energy stocks are more effective hedge than green bonds, especially for crude oil. The application of regression analyses shows that the implied volatilities of US equities and crude oil as well as US dollar index have a negative impact on the hedge portfolio returns, whereas gold prices and inflation have a positive impact.