Sentences with phrase «square of standard deviation»

Since variance is the square of standard deviation, and since 0.01 squared is 0.0001, this is the same thing as saying that the R2 is 99.99 %.

The square of standard deviation is the variance, as defined by Nobel Laureate Harry Markowitz, who is arguably best known as the pioneer of Modern Portfolio Theory.

VRP here differs from that in the referenced research in three ways: (1) it is a volatility premium rather than a variance premium based on standard deviation rather than the square of standard deviation; (2) it is implied volatility minus expected realized volatility, rather than the reverse, and so should be mostly positive; and, (3) estimation of expected realized volatility is much simpler.

Not exact matches

PESPX has an R - squared of 77 percent, a beta of 0.97, and a standard deviation of 11.0 percent.

Calculate daily realized volatility of IEF as the standard deviation of daily total returns over the past 21 trading days, multiplied by the square root of 252 to annualize.

«Identifying VXX / XIV Tendencies» finds that the Volatility Risk Premium (VRP), estimated as the difference between the current level of the S&P 500 implied volatility index (VIX) and the annualized standard deviation of S&P 500 Index daily returns over the previous 21 trading days (multiplying by the square root of 250 to annualize), may be a useful predictor of iPath S&P 500 VIX Short - term Futures ETN (VXX) and VelocityShares Daily Inverse VIX Short - term ETN (XIV) returns.

Standard deviation is calculated as the square root of variance.

Standard deviation is simply the square root of variance.

Given that the standard deviation of Newco's stock is simply the square root of the variance, the standard deviation is 0.0179, or 1.79 %.

Example: Standard Deviation Standard deviation (σ) is found by taking the square root of Deviation Standard deviation (σ) is found by taking the square root of deviation (σ) is found by taking the square root of variance:

R — Squared = (covariance between benchmark and mutual fund scheme / (Standard deviation of mutual fund scheme * standard deviation of benchmarkStandard deviation of mutual fund scheme * standard deviation of benchmarkstandard deviation of benchmark)-RRB- 2

The formula for HV is standard deviation of daily returns over the last 100 days times the square root of 252 times 100.

The annualized return (approximately) equals the average return minus one - half of the standard deviation squared (or one - half of the variance).

For implied volatility it is okey to use Black and scholes but what to do with the historical volatility which carry the effect of past prices as a predictor of future prices.And then precisely the conditional historical volatility.i suggest that you must go with the process like, for stock returns 1) first download stock prices into excel sheet 2) take the natural log of (P1 / po) 3) calculate average of the sample 4) calculate square of (X-Xbar) 5) take square root of this and you will get the standard deviation of your required data.

The weights on squared deviations from the mean (for the standard deviation computation) follow an exponential decay process with a half - life of 5 years, so that the most recent data point has twice the weight in the volatility estimate as 5 years ago, which has twice the weight as 10 years ago, and so on.

Multiply the square of the first asset's weight by the square of the first asset's standard deviation and add it to the square of the second asset's weight multiplied by the square of the second asset's standard deviation.

• Will display portfolio statistics like correlation coefficients, average / median / minimum / maximum rates of return over the selected time frame, along with standard deviation of monthly returns, Beta, Alpha (Jensen), R - squared, Treynor Ratio, and Sharpe Ratio, and all of that.

If each year's stock return were totally independent of the past, the standard deviation would fall in accordance with the square root of N. Even if we were to allow for a short - term memory of two or three years (i.e., momentum), independence would still imply this square root of N behavior.

The percentage spread (standard deviation) of the annualized return of stocks falls faster than 1 / (the square root of N), where N is the number of years.

The square root of variance, or standard deviation, has the same unit form as the data series being analyzed and is such more commonly used to measure risk.

The Levy - Gunthorpe standard deviation is superior to calculating the annualized standard deviation of returns as the product of the standard deviation of the monthly returns multiplied by the square root of 12.

For a Poisson distribution with small mean, the standard deviation is always large (square root of the mean).

For background behind the flattening - MRES process, glance at estimation theory and take a slightly longer look at least squares (minimizing the sum of the squared errors — related article at Minimum mean squared error — minimizing sum - of - squares and mean squared error are the same thing, and essentially the same thing as minimizing variance and standard deviation).

And if you judge MRES by other criteria than variance or standard deviation, e.g. getting an interesting shape, then you are still within the realm of estimation theory (you're estimating the parameters that give you your interesting shape) but no longer in that of minimum mean squared error.

More broadly, comparable critiques can also be leveled at sum - of - squares - based measures of variability, including the standard deviation (s) and standard error [Willmott et al., 2009].

(A-3)-RSB-, is often called the law of propagation of uncertainty and in common parlance the «root - sum - of - squares» (square root of the sum - of - the squares) or «RSS» method of combining uncertainty components estimated as standard deviations.

This approach yields a forecast of 5.6 million square kilometers with a one standard deviation range between 4.6 and 6.4 millions square kilometers.

For a Gaussian time series, the margin of error on a trend of length N t estimated by linear least - squares regression is a function of the magnitude of the interannual variability (given by the standard deviation σ), the lag - one autocorrelation and the trend length (Thompson et al. 2015).

They assess the predictive skill as the ratio of the root - mean - square error of the differences for each model between its predicted ΔT and its actual (simulated) ΔT, to the standard deviation of the simulated changes across all the models.

Nic wrote; «They assess the predictive skill as the ratio of the root - mean - square error of the differences for each model between its predicted ΔT and its actual (simulated) ΔT, to the standard deviation of the simulated changes across all the models.

Wu et al., 4.7 million square kilometers, standard deviation of 0.4, Model.

Our projected Arctic sea ice extent from the NCEP CFSv2 model with June 2013 revised - initial condition using 30 - member ensemble forecast is (surprisingly increased to) 4.7 million square kilometers with a standard deviation of 0.4 million square kilometers.

Averaging more years reduces the standard deviation by the square root of the number of years, so by the time you average ten years the standard deviation for a decade is down to 0.03 degrees.

One could argue that real SST measurements aren't quite so well - behaved, but it is possible to show (see Figure 11 of the HadSST2 paper, Rayner et al. 2006 for details) that the standard deviation of grid box averages falls roughly as one over the square root of the number of contributing observations and that the standard deviation for gridbox averages based on a single observation is a lot less than 10 degrees.

In the absence of serial correlation the standard deviation of this trend estimate is the standard deviation of the period - to - period changes in temperature divided by the square root of the number of periods in the interval.

Estimates of the standard deviation of Outlook values for September 2009 run at 0.3, 0.4, 0.4, 0.5, and 0.5 million square kilometers.

As the submitted uncertainty standard deviations are about 0.4 million square kilometers, most of the Outlook estimates overlap.

Applying these standard deviations to the consensus Outlook value of 4.6 million square kilometers, there is about a 16 % chance that the estimate could be near the 2007 record value and about a 40 % chance of there being sea ice greater than the 2008 value.

Root mean square error (RMSE, circle) and standard deviation (SD, half of error bar) of climate variables of the six model ensembles, CMIP3 - AO (red), CMIP3 - AS (magenta), HadCM3 - AO (blue), HadSM3 - AS (light blue), MIROC3 - AS (green), and NCAR - A (light green), respectively.

We also check the validity of the rank histogram approach by comparing the model - data difference with the ensemble spread through calculating the root mean square model - data difference (RMSE), and the standard deviation of the ensemble (SD).

I suspect I've got the calculation of standard deviation of the least - squares fit wrong.

The standard deviation of the ensemble September prediction is 0.4 million square kilometers.

The time - domain analysis (the standard deviation of all RR intervals [SDNN] and the square root of the mean squared differences of successive normal sinus intervals [RMSSD]-RRB-, the frequency - domain analysis (very low frequency, low frequency [LF], high frequency [HF], and total power [TP]-RRB-, and a non-linear complexity measure the approximate entropy were computed.