CBOE Volatility Index Forward View
| VIX Index | 16.88 0.53 3.24% |
Momentum 51
Impartial
Oversold | Overbought |
Using CBOE Volatility hype-based prediction, you can estimate the value of CBOE Volatility Index from the perspective of CBOE Volatility response to recently generated media hype and the effects of current headlines on its competitors.
The Naive Prediction forecasted value of CBOE Volatility Index on the next trading day is expected to be 16.20 with a mean absolute deviation of 1.21 and the sum of the absolute errors of 73.60. CBOE Volatility after-hype prediction price | USD 16.88 |
There is no one specific way to measure market sentiment using hype analysis or a similar predictive technique. This prediction method should be used in combination with more fundamental and traditional techniques such as index price forecasting, technical analysis, analysts consensus, earnings estimates, and various momentum models.
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. CBOE Volatility Additional Predictive Modules
Most predictive techniques to examine CBOE price help traders to determine how to time the market. We provide a combination of tools to recognize potential entry and exit points for CBOE using various technical indicators. When you analyze CBOE charts, please remember that the event formation may indicate an entry point for a short seller, and look at other indicators across different periods to confirm that a breakdown or reversion is likely to occur.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
CBOE Volatility Naive Prediction Price Forecast For the 30th of January
Given 90 days horizon, the Naive Prediction forecasted value of CBOE Volatility Index on the next trading day is expected to be 16.20 with a mean absolute deviation of 1.21, mean absolute percentage error of 2.66, and the sum of the absolute errors of 73.60.Please note that although there have been many attempts to predict CBOE Index prices using its time series forecasting, we generally do not recommend using it to place bets in the real market. The most commonly used models for forecasting predictions are the autoregressive models, which specify that CBOE Volatility's next future price depends linearly on its previous prices and some stochastic term (i.e., imperfectly predictable multiplier).
CBOE Volatility Index Forecast Pattern
CBOE Volatility Forecasted Value
In the context of forecasting CBOE Volatility's Index value on the next trading day, we examine the predictive performance of the model to find good statistically significant boundaries of downside and upside scenarios. CBOE Volatility's downside and upside margins for the forecasting period are 9.51 and 22.89, respectively. We have considered CBOE Volatility's daily market price to evaluate the above model's predictive performance. Remember, however, there is no scientific proof or empirical evidence that traditional linear or nonlinear forecasting models outperform artificial intelligence and frequency domain models to provide accurate forecasts consistently.
Model Predictive Factors
The below table displays some essential indicators generated by the model showing the Naive Prediction forecasting method's relative quality and the estimations of the prediction error of CBOE Volatility index data series using in forecasting. Note that when a statistical model is used to represent CBOE Volatility index, the representation will rarely be exact; so some information will be lost using the model to explain the process. AIC estimates the relative amount of information lost by a given model: the less information a model loses, the higher its quality.| AIC | Akaike Information Criteria | 119.0899 |
| Bias | Arithmetic mean of the errors | None |
| MAD | Mean absolute deviation | 1.2066 |
| MAPE | Mean absolute percentage error | 0.0668 |
| SAE | Sum of the absolute errors | 73.6021 |
Predictive Modules for CBOE Volatility
There are currently many different techniques concerning forecasting the market as a whole, as well as predicting future values of individual securities such as CBOE Volatility Index. Regardless of method or technology, however, to accurately forecast the index market is more a matter of luck rather than a particular technique. Nevertheless, trying to predict the index market accurately is still an essential part of the overall investment decision process. Using different forecasting techniques and comparing the results might improve your chances of accuracy even though unexpected events may often change the market sentiment and impact your forecasting results.CBOE Volatility Estimiated After-Hype Price Volatility
As far as predicting the price of CBOE Volatility at your current risk attitude, this probability distribution graph shows the chance that the prediction will fall between or within a specific range. We use this chart to confirm that your returns on investing in CBOE Volatility or, for that matter, your successful expectations of its future price, cannot be replicated consistently. Please note, a large amount of money has been lost over the years by many investors who confused the symmetrical distributions of Index prices, such as prices of CBOE Volatility, with the unreliable approximations that try to describe financial returns.
Next price density |
| Expected price to next headline |
CBOE Volatility Index Price Outlook Analysis
Have you ever been surprised when a price of a Index such as CBOE Volatility is soaring high without any particular reason? This is usually happening because many institutional investors are aggressively trading CBOE Volatility backward and forwards among themselves. Have you ever observed a lot of a particular company's price movement is driven by press releases or news about the company that has nothing to do with actual earnings? Usually, hype to individual companies acts as price momentum. If not enough favorable publicity is forthcoming, the Index price eventually runs out of speed. So, the rule of thumb here is that as long as this news hype has nothing to do with immediate earnings, you should pay more attention to it. If you see this tendency with CBOE Volatility, there might be something going there, and it might present an excellent short sale opportunity.
| Expected Return | Period Volatility | Hype Elasticity | Related Elasticity | News Density | Related Density | Expected Hype |
0.17 | 6.69 | 0.00 | 0.00 | 0 Events / Month | 0 Events / Month | Any time |
| Latest traded price | Expected after-news price | Potential return on next major news | Average after-hype volatility | |
16.88 | 16.88 | 0.00 |
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CBOE Volatility Hype Timeline
CBOE Volatility Index is at this time traded for 16.88. The entity stock is not elastic to its hype. The average elasticity to hype of competition is 0.0. CBOE is forecasted not to react to the next headline, with the price staying at about the same level, and average media hype impact volatility is insignificant. The immediate return on the next news is forecasted to be very small, whereas the daily expected return is at this time at 0.17%. %. The volatility of related hype on CBOE Volatility is about 234150.0%, with the expected price after the next announcement by competition of 16.88. Assuming the 90 days trading horizon the next forecasted press release will be any time. 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.CBOE Volatility Related Hype Analysis
Having access to credible news sources related to CBOE Volatility's direct competition is more important than ever and may enhance your ability to predict CBOE Volatility's future price movements. Getting to know how CBOE Volatility's peers react to changing market sentiment, related social signals, and mainstream news is a great way to find investing opportunities and time the market. The summary table below summarizes the essential lagging indicators that can help you analyze how CBOE Volatility may potentially react to the hype associated with one of its peers.
| HypeElasticity | NewsDensity | SemiDeviation | InformationRatio | PotentialUpside | ValueAt Risk | MaximumDrawdown | |||
| GENMF | Generation Mining Limited | 0.00 | 0 per month | 4.62 | 0.12 | 8.00 | (7.50) | 24.94 | |
| SMSI | Smith Micro Software | 0.00 | 0 per month | 0.00 | (0.11) | 5.45 | (6.35) | 17.12 | |
| CGNT | Cognyte Software | 0.02 | 9 per month | 2.28 | 0.02 | 3.70 | (4.23) | 13.68 | |
| SLOFF | Solstad Offshore ASA | 0.00 | 0 per month | 0.00 | 0.10 | 0.00 | 0.00 | 19.77 | |
| BMOOF | Blue Moon Metals | 0.00 | 0 per month | 1.64 | 0.31 | 6.63 | (3.52) | 14.90 | |
| CYBF | Cyberfort Software | 0.00 | 0 per month | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| PSWR | Prism Software | 0.00 | 0 per month | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Other Forecasting Options for CBOE Volatility
For every potential investor in CBOE, whether a beginner or expert, CBOE Volatility's price movement is the inherent factor that sparks whether it is viable to invest in it or hold it better. CBOE Index price charts are filled with many 'noises.' These noises can hugely alter the decision one can make regarding investing in CBOE. Basic forecasting techniques help filter out the noise by identifying CBOE Volatility's price trends.CBOE Volatility Related Equities
One of the popular trading techniques among algorithmic traders is to use market-neutral strategies where every trade hedges away some risk. Because there are two separate transactions required, even if one position performs unexpectedly, the other equity can make up some of the losses. Below are some of the equities that can be combined with CBOE Volatility index to make a market-neutral strategy. Peer analysis of CBOE Volatility could also be used in its relative valuation, which is a method of valuing CBOE Volatility by comparing valuation metrics with similar companies.
| Risk & Return | Correlation |
CBOE Volatility Market Strength Events
Market strength indicators help investors to evaluate how CBOE Volatility index reacts to ongoing and evolving market conditions. The investors can use it to make informed decisions about market timing, and determine when trading CBOE Volatility shares will generate the highest return on investment. By undertsting and applying CBOE Volatility index market strength indicators, traders can identify CBOE Volatility Index entry and exit signals to maximize returns.
CBOE Volatility Risk Indicators
The analysis of CBOE Volatility's basic risk indicators is one of the essential steps in accurately forecasting its future price. The process involves identifying the amount of risk involved in CBOE Volatility's investment and either accepting that risk or mitigating it. Along with some essential techniques for forecasting cboe index prices, we also provide a set of basic risk indicators that can assist in the individual investment decision or help in hedging the risk of your existing portfolios.
| Mean Deviation | 5.11 | |||
| Semi Deviation | 6.09 | |||
| Standard Deviation | 6.69 | |||
| Variance | 44.79 | |||
| Downside Variance | 43.68 | |||
| Semi Variance | 37.08 | |||
| Expected Short fall | (5.44) |
Please note, the risk measures we provide can be used independently or collectively to perform a risk assessment. When comparing two potential investments, we recommend comparing similar equities with homogenous growth potential and valuation from related markets to determine which investment holds the most risk.
Story Coverage note for CBOE Volatility
The number of cover stories for CBOE Volatility depends on current market conditions and CBOE Volatility's risk-adjusted performance over time. The coverage that generates the most noise at a given time depends on the prevailing investment theme that CBOE Volatility is classified under. However, while its typical story may have numerous social followers, the rapid visibility can also attract short-sellers, who usually are skeptical about CBOE Volatility's long-term prospects. So, having above-average coverage will typically attract above-average short interest, leading to significant price volatility.
Other Macroaxis Stories
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