# Tag Info

7

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 should ...

5

I found and answer to my own question. So, I post it here for people who maybe have the same problem. The answer, however, is quite intuitive. The last observation used for the estimation of the physical density is also the time point where the investors know the most about the physical density because at this point the most possible historical observations ...

3

First, I assume that your price data are all from the same asset but spread over a certain time range. If you are looking for the distribution of the price of this asset on the real axis, you have plenty of methods (several fields in mathematics and statistics deal with this topic). As a first step you could make a histogram of your data. There you can see ...

3

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. ...

2

One simple approach is Construct the cumulative probability function (CDF), which will be a step-function. Smooth the CDF; for example, by using splines or a kernel smoothing function. Calculate the slope of the smoothed CDF, giving a curvy linear PDF. In R, this could be done using the ecdf function and one of the kernel smoothers. Again, as vanguard2k ...

1

Not exactly the answer you're looking for: It's not obvious that a region of stability is a desirable property. One can trivially construct an example where this is true: suppose the actual generation function of your target is $f: x_t \mapsto 2x_t+1$ over $\mathbb{R}$, and you have a signal $s$ of one parameter $s\left(p,x_t\right)=\left(p^{e} \mod 3\... 1 To shorten the notation, let's write$T_t = T(D_t,y_t)$and$\delta_t = \delta(D_t,y_t)\$. There are two ways to show that, in fact, the dynamics of $$\xi_t = \xi(D_t, y_t,t) = e^{-\int_0^t \delta_s ds}\, T_t$$ is given by $$\frac{d\xi_t}{\xi_t} = \left( -\delta_t + \frac{\mathscr{L} T_t}{T_t} \right)dt \quad+\quad \text{diffusion terms}.$$ First way (...

1

The problem is to find the best functional form of the utility function plus estimate its parameters. A good starting point is the following draft chapter from an upcoming book which gives a good intuition and many examples: Preferences by Andrew Ang

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The cross-validation procedure does not turn on the choice of algorithm. Yes - calculate the prediction error of the fitted models when predicting the V'th part of the data. Combine the V estimates of prediction average using a simple average. Subsets should be randomly sampled (roughly equally sized). 2a. Subsets should not overlap. No. As long as the ...

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