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What are the well known methods for forecasting (daily - weekly - monthly) volatility of a stock price? How about a bond price?

Let's say I have in my disposition the price time series at a very high frequency. How should I avoid dealing with the micro structure of the market?

Thanks

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For your first question there a 2 types of models: stochastic volatility and GARCH which need as an imput the return of asset. For HF data this models can be modified, I am not familiar with SV models but GARCH type would be a fractional integraded GARCH (FIGARCH) or a multiplicación componentes GARCH (mcsGARCH). There are some stailized fact od HF data that you have to take into acount like diurnal seasonality and a lot of autocorrelation, there are more that you could search un google or search note un this page bacause there are tons of cuestiona about thia topic

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My favorite model of volatility is the HAR RV model by Fulvio Corsi. Good combination of simplicity and accuracy.

http://jfec.oxfordjournals.org/content/7/2/174.short?rss=1&ssource=mfc

About the microstructure issue: you cannot avoid dealing with it, it is important if you are computing volatility from very high frequency.

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