CBOE Volatility's market value is the price at which a share of CBOE Volatility trades on a public exchange. It measures the collective expectations of CBOE Volatility Index investors about its performance. CBOE Volatility is listed at 15.24 as of the 27th of November 2024, which is a 8.58% down since the beginning of the trading day. The index's lowest day price was 15.24. With this module, you can estimate the performance of a buy and hold strategy of CBOE Volatility Index and determine expected loss or profit from investing in CBOE Volatility over a given investment horizon. Check out World Market Map to better understand how to build diversified portfolios. Also, note that the market value of any index could be closely tied with the direction of predictive economic indicators such as signals in gross domestic product.
Symbol
CBOE
CBOE Volatility '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 CBOE Volatility's index 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 CBOE Volatility.
0.00
05/31/2024
No Change 0.00
0.0
In 5 months and 30 days
11/27/2024
0.00
If you would invest 0.00 in CBOE Volatility on May 31, 2024 and sell it all today you would earn a total of 0.00 from holding CBOE Volatility Index or generate 0.0% return on investment in CBOE Volatility over 180 days.
CBOE Volatility 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 CBOE Volatility's index 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 CBOE Volatility Index upside and downside potential and time the market with a certain degree of confidence.
Today, many novice investors tend to focus exclusively on investment returns with little concern for CBOE Volatility's investment risk. Other traders do consider volatility but use just one or two very conventional indicators such as CBOE Volatility's standard deviation. In reality, there are many statistical measures that can use CBOE Volatility historical prices to predict the future CBOE Volatility's volatility.
CBOE Volatility Index secures Sharpe Ratio (or Efficiency) of 0.0316, which signifies that the index had a 0.0316% return per unit of risk over the last 3 months. We have found twenty-seven technical indicators for CBOE Volatility Index, which you can use to evaluate the volatility of the entity. The entity shows a Beta (market volatility) of 0.0, which signifies not very significant fluctuations relative to the market. the returns on MARKET and CBOE Volatility are completely uncorrelated.
Auto-correlation
0.20
Weak predictability
CBOE Volatility Index has weak predictability. Overlapping area represents the amount of predictability between CBOE Volatility time series from 31st of May 2024 to 29th of August 2024 and 29th of August 2024 to 27th 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 CBOE Volatility Index price movement. The serial correlation of 0.2 indicates that over 20.0% of current CBOE Volatility price fluctuation can be explain by its past prices.
Correlation Coefficient
0.2
Spearman Rank Test
-0.06
Residual Average
0.0
Price Variance
5.76
CBOE Volatility Index lagged returns against current returns
Autocorrelation, which is CBOE Volatility index'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 CBOE Volatility's index expected returns. We can calculate the autocorrelation of CBOE Volatility returns to help us make a trade decision. For example, suppose you find that CBOE Volatility has exhibited high autocorrelation historically, and you observe that the index 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
CBOE Volatility 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 CBOE Volatility index is displaying a positive serial correlation, investors will expect a positive pattern to continue. However, if CBOE Volatility index is observed to have a negative serial correlation, investors will generally project negative sentiment on having a locked-in long position in CBOE Volatility index over time.
Current vs Lagged Prices
Timeline
CBOE Volatility Lagged Returns
When evaluating CBOE Volatility's market value, investors can use the concept of autocorrelation to see how much of an impact past prices of CBOE Volatility index have on its future price. CBOE Volatility 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, CBOE Volatility autocorrelation shows the relationship between CBOE Volatility index current value and its past values and can show if there is a momentum factor associated with investing in CBOE Volatility Index.
Regressed Prices
Timeline
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