Bivariate Sampling Statistics This site is a part of the JavaScript E-labs learning objects for decision making. Other JavaScript in this series are categorized under different areas of applications in the MENU section on this page. Enter (by replacing) your up-to-42 two samples paired-data sets where measurements are made jointly on two random variables (X, Y) per subject, and then click the Calculate button. Blank boxes are not included in the calculations but zeros are. In entering your data to move from cell to cell in the data-matrix use the Tab key not arrow or enter keys. Risk Assessment Process: Clearly, different subjective probability models are plausible they can give quite different answers. Cov(X, Y) / Var (X) is called the beta of the random variable X with respect to Y. Given you wish to invest $12,000 over one period (with the same length of time), how do you invest for the optimal strategy? Using the JavaScript we obtain: Beta (Currencies) = -0.4578313, and Beta (Gold) = -1.9
Matter of Stats Estimating Option-Implied Probability Distributions for Asset Pricing After interpolation in (K, σ)-space, we obtain enough data points to estimate the implied strike price density functions at each expiry time. To do this we use a computational finance principle developed by Breeden and Litzenberger [4], which states that the probability density function f(K) of the value of an asset at time T is proportional to the second partial derivative of the asset call price C = C(K). We first transform the data to the original domain ((K, C)-space) for each expiry time using the blsprice function: T0 = unique(D.T); S = D.S(1); rf = D.rf(1); for k = 1:numel(T0) newC(:, k) = blsprice(S, fineK, rf, T0(k), sigmaCallSABR(:, k)); end Here, fineK is a vector defining the range of strike prices used for the interpolation and sigmaCallSABR is the matrix created using SABR interpolation in which the columns contain the interpolated volatility smiles for each expiry time We then compute the numerical partial derivatives with respect to the strike price.
OCC: The Options Clearing Corporation CBOE | Chicago Board Options Exchange The Calculating Investor BlackLitterman.org Business Finance Online: Home This web site is an interactive learning tool for the Corporate Finance Student. The emphasis of this site is on the quantitative areas of Corporate Finance. Several applications and tools have been developed to help the student obtain an understanding of these concepts. The buttons on the bar at the top of this page provide links to web pages which cover the core areas of Corporate Finance. Concepts These pages provide an introduction to the key concepts related to the particular subject. Tools & Problems These pages present financial calculators, algorithmically generated practice problems, and quizzes. This web site is best viewed using a modern web browser. Several conventions are used throughout the site. while the links to the other pages in the section are indicated with a © 2002 - 2018 by Mark A.
RSI - Relative Strength Index Relative Strength Index (RSI) is a popular momentum oscillator developed by J. Welles Wilder Jr. and detailed in his book New Concepts in Technical Trading Systems. The Relative Strength Index compares upward movements in closing price to downward movements over a selected period. Wilder originally used a 14 day period, but 7 and 9 days are commonly used to trade the short cycle and 21 or 25 days for the intermediate cycle. RSI is smoother than the Momentum or Rate of Change oscillators and is not as susceptible to distortion from unusually high or low prices at the start of the window (detailed in Momentum). RSI Trading Signals Different signals are used in trending and ranging markets. Use trailing buy- and sell-stops to time entry into trades. Ranging Markets Set the Overbought level at 70 and Oversold at 30. Go long when RSI falls below the 30 level and rises back above it or on a bullish divergence where the first trough is below 30. Failure swings strengthen other signals. Example
Relative Strength Index (RSI) Introduction Developed J. Welles Wilder, the Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. RSI is an extremely popular momentum indicator that has been featured in a number of articles, interviews and books over the years. Wilder features RSI in his 1978 book, New Concepts in Technical Trading Systems. Calculation 100 RSI = 100 - -------- 1 + RS RS = Average Gain / Average Loss To simplify the calculation explanation, RSI has been broken down into its basic components: RS, Average Gain and Average Loss. The very first calculations for average gain and average loss are simple 14 period averages. First Average Gain = Sum of Gains over the past 14 periods / 14. The second, and subsequent, calculations are based on the prior averages and the current gain loss: Average Gain = [(previous Average Gain) x 13 + current Gain] / 14. Wilder's formula normalizes RS and turns it into an oscillator that fluctuates between zero and 100.
The Put Option selling – Varsity by Zerodha 6.1 – Building the case Previously we understood that, an option seller and the buyer are like two sides of the same coin. They have a diametrically opposite view on markets. Going by this, if the Put option buyer is bearish about the market, then clearly the put option seller must have a bullish view on the markets. Recollect we looked at the Bank Nifty’s chart in the previous chapter; we will review the same chart again, but from the perspective of a put option seller. The typical thought process for the Put Option Seller would be something like this – You may have a question at this stage – If the outlook is bullish, why write (sell) a put option and why not just buy a call option? Well, the decision to either buy a call option or sell a put option really depends on how attractive the premiums are. So, with these thoughts assume the trader decides to write (sell) the 18400 Put option and collect Rs.315 as the premium. 6.2 – P&L behavior for the put option seller = 315 – Max (0, -1260)