US Dollar Price Research Articles

Bitcoin Price Bubbles and The Factors Driving Bitcoin Price Formation

The Bitcoin, one of the most discussed event of recent times, is the first cryptocurrency to be released. Bitcoin has the biggest market capitalization in the cryptocurrencies, and it is a decentralized payment tool that cannot be controlled by any government institution. Cryptocurrencies operate on top of a decentralized network, recording and verifying transactions using a technology called Blockchain. Many bubble price formations occurred in financial markets throughout history. Cryptocurrency markets are prone to manipulation because they are not connected to any center. This study aims to to investigate the buble formations in Bitcoin (BTC) US Dollar price for the period of 11 December 2017 - 31 July 2023, and also to determine the main financial variables affecting Bitcoin prices. ARDL Bounds test proves cointegration between Bitcoin price and dependent variables. Error Correction Model determines positive connection between BTC and Nasdaq100 index. A daily change in Nasdaq100 index leads to a 0.58% increase in BTC price. Model captures also negative relationship between Volatility Index, USD Index and BTC price. No long-term relationship emerges in the results. In the long run, unique and different variables shape BTC price formation.

Modelling common bubbles in cryptocurrency prices

Bubbles and spikes in cryptocurrency prices increase considerably the risk on investments in these assets. In the traditional time series literature bubbles are viewed as nonstationary and non-estimable components of a process. In this paper, we adopt a different approach and consider the bubbles as inherent features of a strictly stationary causal-noncausal (mixed) Vector Autoregressive (VAR) process. This approach allows us to model and estimate the common bubbles and spikes in cryptocurrency prices. It also provides us linear combinations of cryptocurrencies that eliminate common bubbles analogously to the cointegrating vectors eliminating common trends in unit root processes. They are used to build cryptocurrency portfolios immune to the risk of common bubbles that ensure stable investment strategies. The mixed VAR model is estimated from the US Dollar prices of Bitcoin, Ethereum, Ripple, and Stellar over the period 2017–2019. We document the common bubbles and illustrate the behaviour of bubble-free portfolios.

COVID-19 and commodity effects monitoring using financial & machine learning models

This article focuses on examining the effects of the COVID-19 pandemic and gold prices on the stock market. It primarily analyzes the relationship between COVID-19 cases and stock market prices, along with the impact on various commodity elements such as gold, oil, Chinese RMB, and US Dollar prices. These commodity elements are considered essential indicators of a country's financial health, and the study investigates how the increase in COVID-19 cases affects these financial elements. The research incorporates financial models, machine learning algorithms, and a financial Gaussian mixture model for data analysis and comparison. The findings shed light on the correlation between the virus, trading outcomes, and the importance of Karachi Stock Exchange-100 index data in preventing market crashes. The study also explores the implications of emergencies on the finance sector and provides insights for future financial predictions and the impact of social disasters on the economy.

Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST

This study predicts the stock price of Petkim Petrokimya Holding Corp. (PETKM), which is listed in Borsa Istanbul (BIST), using PETKM stock price, US dollar (USD/TRY) price and BIST Chemical, Petroleum & Plastic (XKMYA) index price. A time series data set with three inputs and one output is created using these data. Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. According to the calculated error metrics, LSTM and RFR algorithms gave better results than CNN with an MSE value less than 0.02. However, the fact that the R2 values of the most successful models created with all three algorithms were greater than 95% revealed that all the algorithms mentioned could be used to estimate this data set.

Pairwise and high-order dependencies in the cryptocurrency trading network

In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.

Retrodicting with the truncated Lévy flight

There is a point in predicting the past (retrodicting) because we lack information about it. To address this issue, we consider a truncated Lévy flight to model data. We build on the finding that there is a power law between truncation length and standard deviation that connects the bounded past and unbounded future. Even if a truncated Lévy flight cannot predict future extreme events, we argue that it can still be used to model the past. Because we avoid the exact form of the probability density function while allowing its distributional moments, with the exception of the mean, to vary over time, our method is applicable to a wide range of symmetric distributions. We illustrate our point by using US dollar prices in 15 different currencies traded on foreign exchange markets.

Cost-effectiveness of prophylactic retropubic sling at the time of vaginal prolapse surgery

Prophylactic midurethral sling placement at the time of prolapse repair significantly reduces the risk for de novo stress urinary incontinence, but it is associated with some small but significant morbidities. Because there has not been a standardized approach to midurethral sling utilization, decision analysis provides a method to evaluate the cost and effectiveness associated with varying midurethral sling placement strategies in addressing the risk for de novo stress urinary incontinence. We aimed to compare the cost effectiveness of the 3 midurethral sling utilization strategies in treating de novo stress urinary incontinence 1 year after vaginal prolapse repair. The 3 approaches are (1) staged strategy in which prolapse repair is done without prophylactic midurethral sling placement, (2) universal sling placement in which prolapse repair is accompanied by prophylactic midurethral sling placement, and (3) selective sling placement in which prolapse repair is accompanied by prophylactic midurethral sling placement only in patients with a positive prolapse-reduced cough stress test. We created a decision analysis model to compare staged strategy, universal sling placement, and selective sling placement. We modeled probabilities of de novo stress urinary incontinence, patients choosing subsequent midurethral sling surgery for de novo stress urinary incontinence, and outcomes related to midurethral sling placement. De novo stress urinary incontinence rates were determined for each strategy from published data. The likelihood of patients with de novo stress urinary incontinence choosing midurethral sling surgery as their first-line treatment was also determined from the literature, and this scenario was only applied to patients without prophylactic midurethral sling placement at their index prolapse repair. Finally, outcomes related to midurethral sling placement, including recurrent or persistent stress urinary incontinence, voiding dysfunction requiring sling lysis, mesh exposure requiring excision, and de novo overactive bladder requiring medications, were all derived from publicly available data. All midurethral sling placement procedures were assumed to be retropubic. The costs for each procedure were obtained from the 2020 Centers for Medicare & Medicaid Services Physician Fee Schedule or from previous literature with convertion to 2020 equivalent US dollar prices using the Consumer Price Index. The primary outcome was modeled as the incremental cost-effectiveness ratio. We performed multiple 1-way sensitivity analyses to assess model robustness. The lowest-cost strategy was the staged strategy, which cost $1051.70 per patient, followed by $1093.75 for selective sling placement and $1125.54 for universal sling placement. The selective sling approach, however, had the highest health utility value; therefore, universal sling placement was dominated by selective sling placement because it is both less costly and more effective. When compared with the staged strategy, selective sling placement was cost effective with an incremental cost-effectiveness ratio of $2664 per quality adjusted life-years, meeting the predetermined threshold. In multiple 1-way sensitivity analyses, the variable with the largest effect was the percentage of patients electing to undergo subsequent midurethral sling surgery for de novo stress urinary incontinence after the index surgery. Only when this proportion exceeded 62% did universal sling placement become the cost-effective option because selective sling placement surpassed the predetermined incremental cost-effectiveness ratio threshold and became dominated. Selective sling placement was the preferred and cost-effective strategy in treating de novo stress urinary incontinence 1 year after vaginal prolapse repair. Surgeons should counsel their patients preoperatively regarding the possibility of de novo stress urinary incontinence after prolapse repair, as well as on the benefits and risks of prophylactic midurethral sling placement surgery.

Analysis of the Brazilian energy policies for natural gas using artificial neural networks

Finding appropriate forecasting methods for the effective management of energy resources is extremely important for improving energy consumption efficiency and decreasing its impact on the environment. Natural gas is a primary source of electricity in Brazil as well as the world because it is a promising alternative fuel for reducing polluting gas emissions. In Brazil, the price of natural gas is higher than that in other countries. A new policy for the gas market, also known as the new gas market, has recently been approved and is being implemented to solve this problem. Thus far, the impact of a changing gas energy policy on national demand is unclear. This work uses multi-layer perceptron type of artificial neural networks (ANNs) to analyze Brazilian energy changes. Future demand for new and old gas markets are forecast from 2021 to 2030. Brazil’s total demand for natural gas is described using seven demands (industrial, automotive, residential, commercial, generation, cogeneration, and others). These demands are predicted individually using four input variables (time (in months), US dollar price (converted to Brazilian real), natural gas price, and GDP) for ANNs. The results show that adopting a new gas market would lead to an increased demand. Conversely, if there were no changes in the energy policy, the demand for natural gas would decrease over time. Because the input variables can be quantified for any country, the proposed approach can also be used to forecast the demands of other countries as well, which would be useful in analyzing their energy policies.

MODEL DINAMIS AUTOREGRESSIVE DISTRIBUTED LAG (STUDI KASUS: PENGARUH KURS DOLAR AMERIKA DAN INFLASI TERHADAP HARGA SAHAM TAHUN 2014-2018)

The aim of this research is to determine the dynamic model equation of autoregressive distributed lag by using koyck method, to find out the effect of log US dollar exchange rate and log inflation on log stock price in 20142018, and to forecast value of log stock price on January 2019August 2019. The data used in 20142018. The data was transformed into logarithm format. Time series plot of log US dollar exchange rate, log inflation, and log stock price suggest that the fluctuation in the data, for instance, both upward and downward trends, during the period. We obtained that the Koyck transformation could changed the lag distribution model into autoregressive distributed lag (ARDL) dynamic model. Furthermore, the log of US dollar exchange rate and log inflation have negative effect on log stock price in particular period. We measured forecasting accuracy using mean absolute prediction error (MAPE) and concluded that ARDL forecasting using Koyck model shows a significant increase in stock price.

Relationship Between the Brent Oil Price and the US Dollar Exchange Rate

This article deals with an analysis of the relationship between the Brent oil price and the US dollar price. This paper analyses the development of the intensity and direction of dependence between the nominal effective exchange rate of the US dollar and the price of Brent oil and other commodities, such as industrial metals, agricultural commodities, gold (including jewellery and platinum) in the period from January 1994 to April 2018. The next section tests the hypothesis that there is a short-term relationship between the effective US dollar exchange rate and the oil price. The last part of the article deals with the question whether there is a long-term relationship between these variables: Brent oil price, effective exchange rate of the US dollar, industrial production index in OECD countries, three-month treasury bill, US ending stocks of crude oil, US percent utilization of refinery operable capacity and the price of ethanol.

Dissection of Bitcoin's multiscale bubble history from January 2012 to February 2018.

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious ‘present’ times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

CO- Integration Approach Study of Crude Oil Prices and USD/ INR

The crude oil price and US dollar/INR influence the value of Indian rupee as well as values of currencies of other countries . Over the past decades, oil price and US dollar dominate the overall global markets. The crude oil price and US dollars instability bond with the economic growth and welfare of a country. Hence the study examined the volatility of crude oil price and US dollar in the Indian commodity market, during the study period from 2009 to 2018. US dollar price were collected from the Reserve Bank of India (RBI) and crude oil price were collected from Multi Commodity Exchange (MCX). To check the volatility, the following statistical tools namely descriptive statistic, ADF and GARCH (1,1) model were used. Based on the result, crude price recorded low volatility compared to U.S dollar price during the study period.

Comparison of Ophthalmic Medication Prices Between the United States and Australia

Health care prices may drive differences in health care costs across high-income nations. Adalimumab, ranibizumab, and aflibercept are high-cost medications in the United States and Australia. A comparison of their prices over time may elucidate how ophthalmic medication prices contribute to health care costs. To compare changes in the prices of adalimumab, ranibizumab, and aflibercept in the United States and Australia, the highest and lowest spenders on health care, respectively, among high-income nations. This retrospective price comparison study examined prices paid by government entities in the United States (Medicare) and Australia (Pharmaceuticals and Benefits Scheme). The analysis and data collection were conducted from March 28 to May 4, 2018, in accordance with guidelines set by the International Society for Pharmacoeconomics and Outcomes Research Task Force on Good Research Practices and prior published studies. No human participants or related data were included in this study. The change in mean prices of adalimumab, ranibizumab, and aflibercept in the United States and Australia. Initial, final, and change in medication price annually from 2013 to 2017 in inflation-adjusted 2017 US dollars. The mean prices (US dollar prices unadjusted for inflation) in 2013 and 2017 in the United States were $1114 ($1053) and $1818 ($1818), respectively, for adalimumab; $2102 ($1988) and $1904 ($1904), respectively, for ranibizumab; and $2074 ($1961) and $1956 ($1956), respectively, for aflibercept. The mean (Australian dollar prices unadjusted for inflation) 2013 and 2017 prices in Australia were $1854 (A $1797) and $1206 (A $1574), respectively, for adalimumab; $2157 (A $2090) and $972 (A $1268), respectively, for ranibizumab; and $2030 (A $1967) and $996 (A $1300), respectively, for aflibercept. The estimated annual change in price for adalimumab was +12.8% (95% CI, 9.1%-16.5%) in the United States compared with -11.1% (95% CI, -15.0% to -7.1%) in Australia, a difference of 23.9% per year (95% CI, 19.7%-28.0%; P < .001). The annual change in price for ranibizumab was -2.6% (95% CI, -3.9% to -1.3%) in the United States compared with -18.5% (95% CI, -29.3% to -7.8%) in Australia, a difference of 15.9% per year (95% CI, 7.6%-24.2%; P = .003). The annual change in price for aflibercept was -1.5% (95% CI, -2.2% to -0.7%) in the United States compared with -16.9% (95% CI, -25.1% to -8.6%) in Australia, a difference of 15.4% (95% CI, 9.1%-21.8%; P = .001). Results of this study indicate that the prices of adalimumab, ranibizumab, and aflibercept significantly decreased during the past 5 years in Australia compared with the United States. These data do not indicate why these differences are noted or what actions might affect future pricing in either country.

Relationship Between the Volatility of Stock Returns and the Volatility of Macroeconomic Variables: A Case of Turkey

In this paper, we examined the relationship between BIST-100 Index (SPI) and a set of macroeconomic variables volatility using Vector Autoregressive (VAR) model. The relationship between the stock market and macroeconomic variables has been subjected to serious economic research. A stock market plays important role for the reallocation of funds in many sectors of an economy. The macroeconomic factors make investors to choose the stock because investors are interested to know about the factors affecting the working of stock to manage their portfolios. Some investors show the stock prices volatility is based on directional trend in the stock prices but actually volatility is amount of fluctuation in stock prices. For this purpose we used the volatility of the variables. This study period 2006-2018 stock market using monthly data for Turkey is to examine the relationship between stock return volatility and macroeconomic volatility. We used the macroeconomic variables volatility these are industrial production (IP), money supply (M1), inflation rate (CPI), US dollar equivalent exchange rate (EX) and oil prices (OIL). We used montly data for the period between january 2006 and december 2018. Asymmetric GARCH models are used for the series volatility. The best performing GARCH model in these models are considered as volatlity. Exchange rate and industrial production index have an important effect on stock market volatility.

The multilateral relationship between oil and G10 currencies

The relationship between oil and currencies is expected to be multilateral in nature. However, the use of the US dollar price of oil to measure this relationship can suffer from aggregation bias, which is caused by the well-known numéraire effect: the quotation of oil in US dollars. In this paper, we remove the numéraire effect from the US dollar price of oil by creating a global (multilateral) price of oil, which is quoted relative to multiple currencies. This allows us to estimate an unbiased multilateral relationship between oil and G10 currencies. We find that the global price of oil moves multilaterally with a group of “oil” currencies: the Norwegian krone, the Australian dollar, the Canadian dollar and the British pound. In contrast, the Japanese Yen and the Swiss Franc move multilaterally against the group of oil currencies and not against the global price of oil.

Blockchain and Bitcoin: Technological Breakthrough or the Latest Tulip Price Bubble?

Bitcoin, exchangeable for legal tender, and blockchain, the novel form of digital ledger technology it is based on, are often confused and conflated. We make the case that, in some respects, this conflation is justified. The price action in Bitcoin is consistent with a prototyping phase of a new technological breakthrough. In support of this, we test empirically software decisions with real world consequences. We find that for the period July 2010 to July 2017, Bitcoin is a hedge of implied inflation expectations, as proxied by Treasury Inflation Protected securities' breakevens. We construct a vector error correction model between Bitcoin and Ethereum. We examine whether the Bitcoin price obeys the law of one price by comparing actual trade data across two exchanges. We highlight the hard fork of Bitcoin into Bitcoin [Classic] and Bitcoin Cash, which is suggestive that splits enhance value. We dovetail this with existing theory to argue that the US Dollar price of Bitcoin may be reflecting estimates of the market penetration of distributed ledger technologies, and its time frame for adoption. This may suggest that the price declines of 2018 reflect a loss of confidence in this adoption.

Dissection of Bitcoin's Multiscale Bubble History

We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious present analysis times t2, positioned in advance to bubble crashes, we employ a clustering method to group LPPLS fits over different time scales and the predicted critical times tc (the most probable time for the start of the crash ending the bubble). Each cluster is argued to provide a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.

Gold and Currencies

It is well known that US dollar gold prices are negatively related to the value of the US dollar and that gold prices denominated in other currencies are negatively related to these currencies. But how strong is this relationship for each currency and what is its cause? This paper provides new evidence on the strength of the negative gold-currency association. For example, a one percent increase in the US dollar leads to a one percent decrease in the US dollar gold price and this relationship holds for all major currencies on a daily basis. The finding implies that gold is a globally traded asset. A multivariate analysis further suggests that gold functions as a global currency benchmark.

Emerging Market Demand and the Changing Dynamics of the Price of Gold

Empirical research on the dynamics of the US dollar price of gold has largely focused on the macroeconomic influence of developed economies and gold’s role as a safe haven during times of market turmoil. However, the economies of several emerging markets now account for a substantial fraction of global gold holdings, and these holdings are not necessarily correlated with financial market uncertainty in developed markets. This paper compares two extensions to a popular econometric model of the US dollar gold price. We do so by incorporating the consumer holdings of gold from the largest emerging markets and comparing the effectiveness of our model over time to one that incorporates a measure of volatility in US financial markets (the VIX index). Our results indicate that emerging market consumer demand has a significant and growing impact on the relationship between USD exchange rates and the price of gold, and that this relationship has a stronger influence than does the level of uncertainty in US financial markets.

Investing in Gold: What to Expect?

Gold is an attractive investment vehicle for many people. Some risks of in investing in gold are surveyed. The price of gold and its fluctuation are examined which raises the concern to all short and long-term investors in gold. This prompts us to study what drives the price movements of gold. In order to shed some light on this intriguing issue, we briefly review the recent history of gold prices within the last century and look at some relationships of gold with the US dollar and stock prices, among other variables. We conclude with some forecast of future prices.