Us Dollar Commodity Market Value
| DXUSD Commodity | 106.96 0.55 0.51% |
| Symbol | DXUSD |
US Dollar 'What if' Analysis
In the world of financial modeling, what-if analysis is part of sensitivity analysis performed to test how changes in assumptions impact individual outputs in a model. When applied to US Dollar's commodity what-if analysis refers to the analyzing how the change in your past investing horizon will affect the profitability against the current market value of US Dollar.
| 01/31/2024 |
| 11/26/2024 |
If you would invest 0.00 in US Dollar on January 31, 2024 and sell it all today you would earn a total of 0.00 from holding US Dollar or generate 0.0% return on investment in US Dollar over 300 days.
US Dollar Upside/Downside Indicators
Understanding different market momentum indicators often help investors to time their next move. Potential upside and downside technical ratios enable traders to measure US Dollar's commodity current market value against overall market sentiment and can be a good tool during both bulling and bearish trends. Here we outline some of the essential indicators to assess US Dollar upside and downside potential and time the market with a certain degree of confidence.
| Downside Deviation | 0.3802 | |||
| Information Ratio | (0.10) | |||
| Maximum Drawdown | 2.21 | |||
| Value At Risk | (0.46) | |||
| Potential Upside | 0.5033 |
US Dollar Market Risk Indicators
Today, many novice investors tend to focus exclusively on investment returns with little concern for US Dollar's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as US Dollar's standard deviation. In reality, there are many statistical measures that can use US Dollar historical prices to predict the future US Dollar's volatility.| Risk Adjusted Performance | 0.1732 | |||
| Jensen Alpha | 0.0639 | |||
| Total Risk Alpha | 0.022 | |||
| Sortino Ratio | (0.10) | |||
| Treynor Ratio | 0.5765 |
Sophisticated investors, who have witnessed many market ups and downs, anticipate that the market will even out over time. This tendency of US Dollar's price to converge to an average value over time is called mean reversion. However, historically, high market prices usually discourage investors that believe in mean reversion to invest, while low prices are viewed as an opportunity to buy.
US Dollar Backtested Returns
At this point, US Dollar is very steady. US Dollar retains Efficiency (Sharpe Ratio) of 0.24, which indicates the commodity had a 0.24% return per unit of price deviation over the last 3 months. We have found twenty-eight technical indicators for US Dollar, which you can use to evaluate the volatility of the commodity. Please validate US Dollar's Coefficient Of Variation of 416.65, mean deviation of 0.2863, and Risk Adjusted Performance of 0.1732 to confirm if the risk estimate we provide is consistent with the expected return of 0.0905%. The entity owns a Beta (Systematic Risk) of 0.14, which indicates not very significant fluctuations relative to the market. As returns on the market increase, US Dollar's returns are expected to increase less than the market. However, during the bear market, the loss of holding US Dollar is expected to be smaller as well.
Auto-correlation | -0.11 |
Insignificant reverse predictability
US Dollar has insignificant reverse predictability. Overlapping area represents the amount of predictability between US Dollar time series from 31st of January 2024 to 29th of June 2024 and 29th of June 2024 to 26th of November 2024. The more autocorrelation exist between current time interval and its lagged values, the more accurately you can make projection about the future pattern of US Dollar price movement. The serial correlation of -0.11 indicates that less than 11.0% of current US Dollar price fluctuation can be explain by its past prices.
| Correlation Coefficient | -0.11 | |
| Spearman Rank Test | -0.03 | |
| Residual Average | 0.0 | |
| Price Variance | 3.5 |
US Dollar lagged returns against current returns
Autocorrelation, which is US Dollar commodity's lagged correlation, explains the relationship between observations of its time series of returns over different periods of time. The observations are said to be independent if autocorrelation is zero. Autocorrelation is calculated as a function of mean and variance and can have practical application in predicting US Dollar's commodity expected returns. We can calculate the autocorrelation of US Dollar returns to help us make a trade decision. For example, suppose you find that US Dollar has exhibited high autocorrelation historically, and you observe that the commodity is moving up for the past few days. In that case, you can expect the price movement to match the lagging time series.
Current and Lagged Values |
| Timeline |
US Dollar regressed lagged prices vs. current prices
Serial correlation can be approximated by using the Durbin-Watson (DW) test. The correlation can be either positive or negative. If US Dollar commodity is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if US Dollar commodity is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in US Dollar commodity over time.
Current vs Lagged Prices |
| Timeline |
US Dollar Lagged Returns
When evaluating US Dollar's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of US Dollar commodity have on its future price. US Dollar autocorrelation represents the degree of similarity between a given time horizon and a lagged version of the same horizon over the previous time interval. In other words, US Dollar autocorrelation shows the relationship between US Dollar commodity current value and its past values and can show if there is a momentum factor associated with investing in US Dollar.
Regressed Prices |
| Timeline |