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If you trade in the US market or follow market analysis you probably heard about Zacks Rank system provided by Zacks Investment Group. This ranking system is based on fundamental conditions of the company which revise frequently. the Zacks analysts revise their analysis based on most recent information about each company, and at the end Zacks rank each company in five different level. 1- Strong Buy 2- Buy 3- Hold 4- Sell 5- Strong Sell You should be noted that based on Zacks, these are not recommendation for buying or selling the equities, rather these are the analysis of companies fundamental usually based on a benchmark like SP500 index. In other words, for instance if a company has Zacks Rank 3 or Hold, that means its returns is foretasted to be aligned with the SP500 return. For the Zacks Rank 4 or Sell the projection is it under-perform the benchmark and so on. Zacks claim these analysis is the 1 to 3 months projection and can be revised anytime based on the new informati

If you read my previous post regards forecasting volatility using GARCH models you won’t have any trouble to forecast volatility by APARCH models. APARCH model is the extended form of GARCH model that allowing to combine two effects in practical use (i.e. asymmetry in the impact of positive and negative lagged returns (i.e. leverage effects) and have flexible power in the volatility calculation). The formula of APARCH is: The APARCH model is one of the most complicated models in use. It allows for leverage effects when gamma is not zero and power effects when delta is not 2. If we impose both values to zero and 2 respectively we just get the GARCH model back. You should note that in many case estimation will fail if the data sample is too short or exhibits structural breaks in volatility like what happened to SP500 index during the 2007-2009 crisis, or it will fail if the data sample size is too short. If we restrict t

You probably heard about the GRACH models and its application to forecast volatility for financial stock prices. In this post I don’t want to go deeply about the concept of the GARCH models, instead I want to show you an application of using these models to forecast volatility. This is the first post of a GARCH application series, for the future we explain more about the more advanced application and how to interpret the result. The general model of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) is GARCH(p, q) , which p and q are the number of lag variances and the number of lag residual errors respectively. If we define p=0 i.e. GARCH(0, q) is equivalent to an ARCH(q) model. In the ARCH(q) process the conditional variance is specified as a linear function of past sample variances only, whereas the GARCH(p, q) process allows lagged conditional variances to enter as well. This corresponds to some sort of adaptive learning mechanism. GARCH(1,1):