Volatilidad histórica

Generalized Black-Scholes-Merton Option Pricing Formula [Loxx]Generalized Black-Scholes-Merton Option Pricing Formula is an adaptation of the Black-Scholes-Merton Option Pricing Model including Numerical Greeks aka "Option Sensitivities" and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". Black-Scholes-Merton Option Pricing The BSM formula and its binomial counterpart may easily be the most used "probability model/tool" in everyday use — even if we con- sider all other

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Sprenkle 1964 Option Pricing Model w/ Num. Greeks [Loxx]Sprenkle 1964 Option Pricing Model w/ Num. Greeks is an adaptation of the Sprenkle 1964 Option Pricing Model in Pine Script. The following information is an except from Espen Gaarder Haug's book "Option Pricing Formulas". The Sprenkle Model Sprenkle (1964) assumed the stock price was log-normally distributed and thus that the asset price followed a geometric Brownian motion, just as in the Black and Scholes (1973) analysis. In this way he ruled out the possibility of negative stock prices,

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Modified Bachelier Option Pricing Model w/ Num. Greeks [Loxx]Modified Bachelier Option Pricing Model w/ Num. Greeks is an adaptation of the Modified Bachelier Option Pricing Model in Pine Script. The following information is an except from Espen Gaarder Haug's book "Option Pricing Formulas". Before Black Scholes Merton The curious reader may be asking how people priced options before the BSM breakthrough was published in 1973. This section offers a quick overview of some of the most important precursors to the BSM model. As early as 1900, Louis Bach

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Bachelier 1900 Option Pricing Model w/ Numerical Greeks [Loxx]Bachelier 1900 Option Pricing Model w/ Numerical Greeks is an adaptation of the Bachelier 1900 Option Pricing Model in Pine Script. The following information is an except from Espen Gaarder Haug's book "Option Pricing Formulas" Before Black Scholes Merton The curious reader may be asking how people priced options before the BSM breakthrough was published in 1973. This section offers a quick overview of some of the most important precursors to the BSM model. As early as 1900, Louis Bachelie

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Black Scholes Option Pricing Model w/ Greeks [Loxx]The Black Scholes Merton model If you are new to options I strongly advise you to profit from Robert Shiller's lecture on same . It combines practical market insights with a strong authoritative grasp of key models in option theory. He explains many of the areas covered below and in the following pages with a lot intuition and relatable anecdotage. We start here with Black Scholes Merton which is probably the most popular option pricing framework, due largely to its simplicity and ease in term

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Boyle Trinomial Options Pricing Model [Loxx]Boyle Trinomial Options Pricing Model is an options pricing indicator that builds an N-order trinomial tree to price American and European options. This is different form the Binomial model in that the Binomial assumes prices can only go up and down wheres the Trinomial model assumes prices can go up, down, or sideways (shoutout to the "crab" market enjoyers). This method also allows for dividend adjustment. The Trinomial Tree via VinegarHill Finance Labs A two-jump process for the asset p

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Implied Volatility Estimator using Black Scholes [Loxx]Implied Volatility Estimator using Black Scholes derives a estimation of implied volatility using the Black Scholes options pricing model. The Bisection algorithm is used for our purposes here. This includes the ability to adjust for dividends. Implied Volatility The implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (such as Black–Scholes), will return a theoretical value equal to the curr

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Cox-Ross-Rubinstein Binomial Tree Options Pricing Model [Loxx]Cox-Ross-Rubinstein Binomial Tree Options Pricing Model is an options pricing panel calculated using an N-iteration (limited to 300 in Pine Script due to matrices size limits) "discrete-time" (lattice based) method to approximate the closed-form Black–Scholes formula. Joshi (2008) outlined varying binomial options pricing model furnishes a numerical approach for the valuation of options. Significantly, the American analogue can be estimated using the binomial tree. This indicator is the comp

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RSI-Adaptive, GKYZ-Filtered DEMA [Loxx]RSI-Adaptive, GKYZ-Filtered DEMA is a Garman-Klass-Yang-Zhang Historical Volatility Filtered, RSI-Adaptive Double Exponential Moving Average. This is an experimental indicator. The way this is calculated is by turning RSI into an alpha value that is then injected into a DEMA function to output price. Price is then filtered using GKYZ Historical volatility. This process of creating an alpha out of RSI is only relevant to EMA-based moving averages that use an alpha value for it's calculation.

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Roger & Satchell Estimator Historical Volatility Bands [Loxx]Roger & Satchell Estimator Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using theRoger & Satchell Estimator Historical Volatility Bands for bands calculation. What is Roger & Satchell Estimator Historical Volatility? The Rogers–Satchell estimator does not handle opening jumps; therefore, it underestimates the volatility. It accurately explains the volatility portion that can be attributed entirely to a trend in the price evoluti

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Garman-Klass-Yang-Zhang Historical Volatility Bands [Loxx]Garman-Klass-Yang-Zhang Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using the Garman-Klass-Yang-Zhang Historical Volatility Bands for bands calculation. What is Garman-Klass-Yang-Zhang Historical Volatility? Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-c

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Garman & Klass Estimator Historical Volatility Bands [Loxx]Garman & Klass Estimator Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using the Garman & Klass Estimator Historical Volatility (instead of "regular" Historical Volatility ) for bands calculation. What is Garman & Klaus Historical Volatility? Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. The Garman and Klass estimator for estimating historical volatility assumes Brownian

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High/Low Historical Volatility Bands [Loxx]High/Low Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using the Historical Volatility high/low (instead of "regular" Historical Volatility) for bands calculation. What is Historical Volatility? Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average p

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Parkinson's Historical Volatility Bands [Loxx]Parkinson's Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using the Parkinson's historical volatility (instead of "regular" Historical Volatility) for bands calculation. What is Parkinson's Historical Volatility? The Parkinson's number, or High Low Range Volatility developed by the physicist, Michael Parkinson in 1980, aims to estimate the Volatility of returns for a random walk using the High and Low in any particular period. IVol

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Historical Volatility Bands [Loxx]Historical Volatility Bands are constructed using: Average as the middle line. Upper and lower bands using the Historical Volatility for bands calculation. What is Historical Volatility? Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period. Using

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Full Volatility Statistics and Forecast This is a tool designed to translate the data from the expected volatility of different assets, such as for example VIX, which measures the volatility of SP500 index. Once get the data from the volatility asset we want to measure(for this test I have used VIX), we are going to translate it the required timeframe expected move by dividing the initial value into : 252 = if we want to use the daily timeframe, since there are ~252 aproximative daily trading days 52 = if we want to use the weekly t

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vol_cone Draws a volatility cone on the chart, using the contract's realized volatility (rv). The inputs are: - window: the number of past periods to use for computing the realized volatility. VIX uses 30 calendar days, which is 21 trading days, so 21 is the default. - stdevs: the number of standard deviations that the cone will cover. - periods to project: the length of the volatility cone. - periods per year: the number of periods in a year. for a daily chart, this is 252. for a thirty minute chart on

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Damiani Volatmeter [loxx]I wasn't going to publish this since it's one my go to private indicators, but I decided to push this out anyway. This is a variation on Damiani Volatmeter to make it easier to understand what's going on. Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the o

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Average Daily Pip Ranges by month Shows historical average daily pip ranges for specific months for FOREX pairs useful for guaging typical seasonal volatility; or rough expected daily pip ranges for different months works on both DXY and foreign currencies option to plot 10yrs worth of data; with 10yr average of the average daily range for specific months cast back to any previous 10yrs of your choosing @twingall

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Jurik Composite Fractal Behavior (CFB) on EMA [Loxx]Jurik Composite Fractal Behavior (CFB) on EMA is an exponential moving average with adaptive price trend duration inputs. This purpose of this indicator is to introduce the formulas for the calculation Composite Fractal Behavior. As you can see from the chart above, price reacts wildly to shifts in volatility--smoothing out substantially while riding a volatility wave and cutting sharp corners when volatility drops. Notice the chop zone on BTC around August 2021, this was a time of extremely l

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Sentient level The indicator presented here is made based on the study published on NSE:INDIAVIX . Basically it shows 2 sigma (by default) trading ranges of the next day (by default) of indices e.g. NSE:NIFTY & NSE:BANKNIFTY . Everyday three new lines get plotted automatically on the chart of the instrument (preferably NSE:NIFTY & NSE:BANKNIFTY ) you want to trade. Generally it's expected that the index to be traded within the ranges however in case of major gap-up or gap-down if the index opens above the high

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Historical Volatility Ratio Historical volatility is an indicator of the extent to which a price may diverge from its average in a given period. Hence, increased price fluctuation results in a higher historical volatility value. It is important to keep in mind that the historical volatility figure does not indicate the price direction but rather how unstable a price is. Volatility is generally a measure of the riskiness of an investment. Increased volatility serves as an indication of increased uncertainty and risk. The o

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por DreWill1738

trailing_drawdown Description: Drawdown was a tool to measure historical risk, derived from measuring current wealth from its previous peak, casually from portfolio construction (weights allocation), will consider to having a minimum drawdown. In this indicator, the drawdown for individual assets is utilized to measure its value or percentage from its trailing peak (default to 1-yr period). Drawdown: drawdown = (price/peaks)-1 Feature: Static: display drawdown as percentage Dynamic: display drawdown

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por palitoj_endthen

Index Reversal Range with Volatility Index or VIX What is the Indicator? • The indicator is a visualization of maximum price in which the respective index can go up to in comparison with it's Volatility Index or VIX. Who to use? • Intraday • Swing • Position • Long term Investors • Futures • Options • Portfolio Managers • Mutual Fund Managers • Index Traders • Volatility based Traders • Long term Investors and Options Traders gets the maximum benefit What timeframe to use? • 1 Year: Position & Investors • 6 Months: Position & Investors •

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por Arun_K_Bhaskar