„volatility“, dt. Schwankung, Unbeständigkeit) ist ein aus der Physik stammender Begriff, der dazu dient, die Unbeständigkeit der Parteipräferenzen einer. volatility - Wörterbuch Englisch-Deutsch. Ben needs to learn how to control his volatility before it gets him into serious trouble. volatility nnoun: Refers. Übersetzung im Kontext von „volatility“ in Englisch-Deutsch von Reverso Context: price volatility, market volatility, low volatility, volatility adjustment, volatility.
Deutsch-Englisch-Wörterbuchvolatility - Wörterbuch Englisch-Deutsch. Ben needs to learn how to control his volatility before it gets him into serious trouble. volatility nnoun: Refers. Übersetzung für 'volatility' im kostenlosen Englisch-Deutsch Wörterbuch von LANGENSCHEIDT – mit Beispielen, Synonymen und Aussprache. Englisch-Deutsch-Übersetzungen für volatility im Online-Wörterbuch wavybrains.com (Deutschwörterbuch).
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Volatility is often measured as either the standard deviation or variance between returns from that same security or market index.
In the securities markets, volatility is often associated with big swings in either direction. An asset's volatility is a key factor when pricing options contracts.
A higher volatility means that a security's value can potentially be spread out over a larger range of values. This means that the price of the security can change dramatically over a short time period in either direction.
A lower volatility means that a security's value does not fluctuate dramatically, and tends to be more steady. One way to measure an asset's variation is to quantify the daily returns percent move on a daily basis of the asset.
Historical volatility is based on historical prices and represents the degree of variability in the returns of an asset. This number is without a unit and is expressed as a percentage.
While variance captures the dispersion of returns around the mean of an asset in general, volatility is a measure of that variance bounded by a specific period of time.
Thus, we can report daily volatility, weekly, monthly, or annualized volatility. Volatility is often calculated using variance and standard deviation.
The standard deviation is the square root of the variance. To calculate variance, follow the five steps below.
This is a measure of risk, and shows how values are spread out around the average price. It gives traders an idea of how far the price may deviate from the average.
Ninety-five percent of data values will fall within two standard deviations 2 x 2. Despite this limitation, standard deviation is still frequently used by traders, as price returns data sets often resemble more of a normal bell curve distribution than in the given example.
For example, a stock with a beta value of 1. Conversely, a stock with a beta of. It is effectively a gauge of future bets investors and traders are making on the direction of the markets or individual securities.
A high reading on the VIX implies a risky market. A variable in option pricing formulas showing the extent to which the return of the underlying asset will fluctuate between now and the option's expiration.
Volatility, as expressed as a percentage coefficient within option-pricing formulas, arises from daily trading activities. How volatility is measured will affect the value of the coefficient used.
Volatility is also used to price options contracts using models like Black-Scholes or binomial tree models. More volatile underlying assets will translate to higher options premiums, because with volatility there is a greater probability that the options will end up in-the-money at expiration.
Options traders try to predict an asset's future volatility and so the price of an option in the market reflects its implied volatility.
Suppose that an investor is building a retirement portfolio. Since she is retiring within the next few years, she's seeking stocks with low volatility and steady returns.
She considers two companies:. The investor would likely choose Microsoft Corporation for their portfolio since it has less volatility and more predictable short-term value.
Implied volatility IV , also known as projected volatility, is one of the most important metrics for options traders. As the name suggests, it allows them to make a determination of just how volatile the market will be going forward.
This concept also gives traders a way to calculate probability. One important point to note is that it shouldn't be considered science, so it doesn't provide a forecast of how the market will move in the future.
Unlike historical volatility, implied volatility comes from the price of an option itself and represents volatility expectations for the future.
Because it is implied, traders cannot use past performance as an indicator of future performance. Instead, they have to estimate the potential of the option in the market.
Also referred to as statistical volatility, historical volatility HV gauges the fluctuations of underlying securities by measuring price changes over predetermined periods of time.
It is the less prevalent metric compared to implied volatility because it isn't forward-looking. When there is a rise in historical volatility, a security's price will also move more than normal.
At this time, there is an expectation that something will or has changed. If the historical volatility is dropping, on the other hand, it means any uncertainty has been eliminated, so things return to the way they were.
Although the Black-Scholes equation assumes predictable constant volatility, this is not observed in real markets, and amongst the models are Emanuel Derman and Iraj Kani 's  and Bruno Dupire 's local volatility , Poisson process where volatility jumps to new levels with a predictable frequency, and the increasingly popular Heston model of stochastic volatility.
It is common knowledge that types of assets experience periods of high and low volatility. That is, during some periods, prices go up and down quickly, while during other times they barely move at all.
Periods when prices fall quickly a crash are often followed by prices going down even more, or going up by an unusual amount.
Also, a time when prices rise quickly a possible bubble may often be followed by prices going up even more, or going down by an unusual amount.
Most typically, extreme movements do not appear 'out of nowhere'; they are presaged by larger movements than usual. This is termed autoregressive conditional heteroskedasticity.
Whether such large movements have the same direction, or the opposite, is more difficult to say. And an increase in volatility does not always presage a further increase—the volatility may simply go back down again.
Not only the volatility depends on the period when it is measured but also on the selected time resolution. The effect is observed due to the fact that the information flow between short-term and long-term traders is asymmetric.
As a result, volatility measured with high resolution contains information that is not covered by low resolution volatility and vice versa.
Some authors point out that realized volatility and implied volatility are backward and forward looking measures, and do not reflect current volatility.
To address that issue an alternative, ensemble measures of volatility were suggested. One of the measures is defined as the standard deviation of ensemble returns instead of time series of returns.
Using a simplification of the above formula it is possible to estimate annualized volatility based solely on approximate observations.
Suppose you notice that a market price index, which has a current value near 10,, has moved about points a day, on average, for many days.
The rationale for this is that 16 is the square root of , which is approximately the number of trading days in a year The average magnitude of the observations is merely an approximation of the standard deviation of the market index.
Volatility thus mathematically represents a drag on the CAGR formalized as the " volatility tax ". Realistically, most financial assets have negative skewness and leptokurtosis, so this formula tends to be over-optimistic.
Some people use the formula:. Despite the sophisticated composition of most volatility forecasting models, critics claim that their predictive power is similar to that of plain-vanilla measures, such as simple past volatility   especially out-of-sample, where different data are used to estimate the models and to test them.
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