VAR在资产投资组合中的发展前景 VAR for a Portfolio of Options Commodities as an asset class going forward.pptVIP

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VAR在资产投资组合中的发展前景 VAR for a Portfolio of Options Commodities as an asset class going forward.ppt

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VAR在资产投资组合中的发展前景VARforaPortfolioofOptions

Model for price forecasting Auto regression Integrated Moving Average Model (ARIMA ) The mathematical models of the persistence, or autocorrelation, in a time series which calls …. Box and Jenkins ARIMA model ARIMA is a method for determining two things: 1. How much of the past should be used to predict the next observation (length of weights)2. The values of the weights.For example; y(t) = 1/3 * y(t-3) + 1/3 * y(t-2) + 1/3 * y(t-1) y(t) = 1/6 * y(t-3) + 4/6 * y(t-2) + 1/6 * y(t-1) Source: ARIMA(p,d,q): A nonseasonal ARIMA model is classified as an ARIMA(p,d,q) model, where: p is the number of autoregressive terms, d is the number of nonseasonal differences, and q is the number of lagged forecast errors in the prediction equation ( In this case, we choose d = 0) Rules for identifying ARIMA model Identifying the order of differencing and the constant: Rule 1: If the series has positive autocorrelations out to a high number of lags, then it probably needs a higher order of differencing. Rule 2: If the lag-1 autocorrelation is zero or negative, or the autocorrelations are all small and pattern less, then the series does not need a higher order of differencing. If the lag-1 autocorrelation is -0.5 or more negative, the series may be over differenced.? BEWARE OF OVERDIFFERENCING!! Rule 3: The optimal order of differencing is often the order of differencing at which the standard deviation is lowest. Rules for identifying ARIMA model Rule 4: A model with no orders of differencing assumes that the original series is stationary (among other things, mean-reverting). A model with one order of differencing assumes that the original series has a constant average trend (e.g. a random walk or SES-type model, with or without growth). A model with two orders of total differencing assumes that the original series has a time-varying trend (e.g. a random trend or LES-type model). Rule 5: A model with no orders of differencing normally includes a constan