Statistical charts and diagrams
In statistical quality control, the EWMA chart (or exponentially weighted moving average chart) is a type of control chart used to monitor either variables or attributes-type data using the monitored business or industrial process's entire history of output. While other control charts treat rational subgroups of samples individually, the EWMA chart tracks the exponentially-weighted moving average of all prior sample means. EWMA weights samples in geometrically decreasing order so that the most recent samples are weighted most highly while the most distant samples contribute very little. Although the normal distribution is the basis of the EWMA chart, the chart is also relatively robust in the face of non-normally distributed quality characteristics. There is, however, an adaptation of the chart that accounts for quality characteristics that are better modeled by the Poisson distribution. The chart monitors only the process mean; monitoring the process variability requires the use of some other technique. The EWMA control chart requires a knowledgeable person to select two parameters before setup: 1. * The first parameter is λ, the weight given to the most recent rational subgroup mean. λ must satisfy 0 < λ ≤ 1, but selecting the "right" value is a matter of personal preference and experience. One 2005 textbook recommends 0.05 ≤ λ ≤ 0.25, while a 1986 journal article recommends 0.1 ≤ λ ≤ 0.3. 2. * The second parameter is L, the multiple of the rational subgroup standard deviation that establishes the control limits. L is typically set at 3 to match other control charts, but it may be necessary to reduce L slightly for small values of λ. Instead of plotting rational subgroup averages directly, the EWMA chart computes successive observations zi by computing the rational subgroup average, , and then combining that new subgroup average with the running average of all preceding observations, zi - 1, using the specially–chosen weight, λ, as follows: . The control limits for this chart type are where T and S are the estimates of the long-term process mean and standard deviation established during control-chart setup and n is the number of samples in the rational subgroup. Note that the limits widen for each successive rational subgroup, approaching . The EWMA chart is sensitive to small shifts in the process mean, but does not match the ability of Shewhart-style charts (namely the and R and and s charts) to detect larger shifts. One author recommends superimposing the EWMA chart on top of a suitable Shewhart-style chart with widened control limits in order to detect both small and large shifts in the process mean. Exponentially weighted moving variance (EWMVar) can be used to obtain a significance score or limits that automatically adjust to the observed data. (Wikipedia).
Volatility: Exponentially weighted moving average, EWMA (FRM T2-22)
[here is my XLS https://trtl.bz/2t1pb9S] The exponentially weighted moving average (EWMA) cures the key weakness of the common historical standard deviation by assigning greater weight to more recent returns and lessor weights to more distant (in the past) returns. Its key parameter is lam
From playlist Quantitative Analysis (FRM Topic 2)
Estimating Volatilities and Correlations | Financial Risk Manager Exam Questions | Simplilearn
🔥Explore Our Free Courses With Completion Certificate by SkillUp: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=EstimatingVolatilitiesDec31&utm_medium=DescriptionFirstFold&utm_source=youtube This video explains the: 1.Volatility 2.Equal Weighted 3.Generalized Autore
From playlist FRM Tutorial | Financial Risk Management Tutorial | Simplilearn
FRM: Exponentially weighted moving average (EWMA)
The EWMA approach to volatility is an improvement over simple volatility because it assigns greater weight to more recent observations (in fact, the weights are proportional). For more financial risk videos, visit our website! http://www.bionicturtle.com
From playlist Volatility
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FRM: GARCH(1,1) to estimate volatility
GARCH(1,1) estimates volatility in a similar way to EWMA (i.e., by conditioning on new information) EXCEPT it adds a term for mean reversion: it says the series is "sticky" or somewhat persistent to a long-run average. For more financial risk videos, visit our website! http://www.bionictur
From playlist Volatility
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Maximum likelihood estimation of GARCH parameters (FRM T2-26)
[My xls is here https://trtl.bz/2NlLn7d] GARCH(1,1) is the popular approach to estimating volatility, but its disadvantage (compared to STDDEV or EWMA) is that you need to fit three parameters. Maximum likelihood estimation, MLE, is an immensely useful statistical approach that can be used
From playlist Quantitative Analysis (FRM Topic 2)
Comparing volatility approaches: MA versus EWMA versus GARCH (FRM T2-25)
The general form for all three is: σ^2(n) = γ*V(L) + α*u^2(n-1) + σ^2(n-1). Discuss this video in our FRM forum here: https://trtl.bz/2WmccjS Subscribe here https://www.youtube.com/c/bionicturtle?sub-confirmation=1 to be notified of future tutorials on expert finance and data science, inc
From playlist Quantitative Analysis (FRM Topic 2)
Volatility: GARCH 1,1 (FRM T2-23)
[my xls is here https://trtl.bz/2t794bU] The GARCH(1,1) volatility estimate shares a similarity to EWMA volatility: both assign greater (lesser) weight to recent (distant) returns. But the GARCH(1,1) has an additional feature: it models a long-run (aka, unconditional) variance toward which
From playlist Quantitative Analysis (FRM Topic 2)
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From playlist PHYSICS 50 ELECTROMAGNETIC RADIATION
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How to Create Chart Templates for Default Chart Formatting in Excel
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