CBOE Volatility Index Forecast - Simple Exponential Smoothing
| VIX Index | 15.24 1.63 9.66% |
CBOE Volatility Simple Exponential Smoothing Price Forecast For the 25th of November
Given 90 days horizon, the Simple Exponential Smoothing forecasted value of CBOE Volatility Index on the next trading day is expected to be 15.24 with a mean absolute deviation of 1.02, mean absolute percentage error of 2.10, and the sum of the absolute errors of 61.05.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 7.32 and 23.16, 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 Simple Exponential Smoothing 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 | 117.0137 |
| Bias | Arithmetic mean of the errors | 0.0052 |
| MAD | Mean absolute deviation | 1.0175 |
| MAPE | Mean absolute percentage error | 0.0542 |
| SAE | Sum of the absolute errors | 61.05 |
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.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 Index Technical and Predictive Analytics
The index market is financially volatile. Despite the volatility, there exist limitless possibilities of gaining profits and building passive income portfolios. With the complexity of CBOE Volatility's price movements, a comprehensive understanding of forecasting methods that an investor can rely on to make the right move is invaluable. These methods predict trends that assist an investor in predicting the movement of CBOE Volatility's current price.| Cycle Indicators | ||
| Math Operators | ||
| Math Transform | ||
| Momentum Indicators | ||
| Overlap Studies | ||
| Pattern Recognition | ||
| Price Transform | ||
| Statistic Functions | ||
| Volatility Indicators | ||
| Volume Indicators |
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 | 5.58 | |||
| Semi Deviation | 5.86 | |||
| Standard Deviation | 7.92 | |||
| Variance | 62.74 | |||
| Downside Variance | 37.38 | |||
| Semi Variance | 34.34 | |||
| Expected Short fall | (7.05) |
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.