# Tag Info

## Hot answers tagged estimation

4

The use of kernels to estimate volatility using intraday data is "nothing more" than combining: intraday volatility estimation kernel smoothing Thus you have to take care about the "usual pits" of these two approaches. Intraday volatility estimation. I hope you know the "signature plot" effect. Of course if you use the proper estimation method, it ...

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Using a realized kernel for calculating volatility will give you results in the same resolution as the data you feed them. So if you feed them minute-by-minute data, then the volatility will be calculated minute-by-minute. What that really means is that only once per minute will you have a good estimate of the volatility of whatever asset you're looking at. ...

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I don't know what you mean by "any scaling" rule. For the square-root of time I can say that it only needs uncorrelated returns. Assume that the return from time point $1$ to $T$ is called $r_{1,T}$ and that it is given as $r_{1,T} = r_1 + r_2 + \cdots + r_T = \sum_{t=1}^T r_t$ where $r_t, t=1,\ldots,T$ are the one-period (e.g. one day) returns. The ...

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It could be useful for optimal trading to have accurate estimates of the intraday seasonalities. Seasonalities come from a mix of: rythms (for instance European curves are impacted by US open and news announces) events (news) market design (proximity of fixing auctions) From an estimation viewpoint, you see that the more you can take these effects into ...

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What is your objective? There are many approaches that can accomplish this in broad terms but whether it is sensible depends on your application. For example if you are interested in intraday breaks in the levels process you can look at OLS with a priori indicator function breaks, or perhaps a univariate Kalman filter with a stochastic slope coefficient ...

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Both approaches can be useful. For stocks, sorting into quantiles is popular because it's easy to understand and explain it's a simple matter to build factor portfolios and track or backtest their performance, while the translation from expected returns to a portfolio is a bit more involved more robust than a single-stock regression, because it is less ...

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The chart you linked to offers data for the "instantaneous forward rate" which are the rates you are looking for (f(tj,tj+τk)). Regarding the construction of the zero-coupon yield curves (cited from the ECB website): "The ECB estimates zero-coupon yield curves for the euro area and derives forward and par yield curves. A zero coupon bond is a bond that ...

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Unfortunately, financial markets are not like physical measures, where you know the "true" value of a physical variable but you just access to it thanks to noised sensors. We do not know the "true" volatility, just because there is not such one value... In statistics you have two kinds of modelling procedures: the ones dedicated to estimate the unknown ...

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I would suggest writing the joint density as the product of the conditional densities then estimate parameters using an optimization package. The joint density is given by $$f(r_0, \ldots, r_T) = f(r_0) \prod_{t=1}^T f(r_t|r_0, \ldots, r_{t-1})$$ then the log likelihood function is L = \log(f(r_0)) + \sum_{t=1}^T \log(f(r_t | r_0, \ldots, r_{t-1}) ...

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One approach which I've encountered in practice is Optimal risk budgeting (ORB). This method is similar to Black Litterman in the sense that it uses active investor views as a starting point. The mean variance optimization is then restricted to those assets for which an active investor view is available, and the allocation is calculated with the constraint ...

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