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1

FVIX is not hard to compute. Just regress changes in VIX on excess returs of your base assets (it can be the 25 FF portfolios if those are what you are trying to explain) i.e run the following: \Delta VIX_t = X_t\beta+\epsilon


-2

Remember that VXX is built from options on the SPX. The far-out future is generally more uncertain than the near future, so insurance for the far-out future is more expensive. So far this has been true ~4/5 of the time. The remaining ~1/5 of the time something crazy is going on right now and things are expected to be calmer in the far-out future. ...


1

There does exist some volatility control indexes (e.g., see page 37 of the S & P index methodology, which can be downloaded from http://ca.spindices.com/documents/methodologies/methodology-index-math.pdf?force_download=true), and also options on them, which are usually embedded in certain structured notes (e.g., google "Risk Aligned Deposit Notes"). ...


18

Many of them are on my website at emanuelderman.com. Others I probably have anyway. Feel free to email me


7

I had read some of them; actually, it does not exist an on-line library that collected them (or, better, it existed here, but it seems the website does not work anymore). I reported here below some of them that you did not find: More Than You Ever Wanted To Know* About Volatility Swaps Model Risk The Volatility Smile And Its implied Tree Enhanced ...


0

Do you mean the "realize measure" of volatility using the intraday Transaction-and-Quote data? If that's the case, just trade data, or mid-quote would be sufficient. Looking at Level II and level III data really introduces a lot of noises (hudge cancellation rates, orders placed by HFT blindly to gain time-priority advantage). Using those data to calculate ...


1

The volatility of your asset $y_t$ is simply its time varying standard deviation, given by $\beta \exp(x_t/2)$. Once you've got the estimates for latent factor $x_t$ from converged MCMC chain, calculate the expected value for volatility at time $t$ using $$ \hat{v_t} = \mathbb{E}[\beta \exp(x_t/2)] = \frac{1}{R}\sum_{r=1}^R \beta \exp(x_t^{(r)}) $$ where $R$ ...


1

Possibly you might be able to first estimate the bid-ask spread from execution prices, using the method of Roll (1984), and then adjust the volatility for this. Essentially the bid-ask bounce adds to the underlying volatility, so knowing an estimate of the b/a and the apparent volatility, the underlying volatility could be recovered by subtraction. ...


1

A variance swap can be replicated with vanilla European options. If you take derivative with respect to variance, you need to do the same thing on both sides. That is, you need also take derivative with respect to variance on those vanilla options. However, the resulting derivative is not the vega in the usual sense, which is the derivative with respect to ...


1

My 2 Zimbabwe cents: A few years ago developing new ARCH like models became almost a fad and large numbers of them were published without a clear justification in my humble opinion. However there is an important distinction I do think. Some markets are symmetric, while others (such as Stock Indexes) show a Leverage Effect where the volatility rises when ...


1

First, for each of the 3 currencies taken separately, find out the leverage lambda_i (i=1,2,3) that would be required to produce an annual standard deviation of 12%. [In my experience the std dev of currencies is about 8 or 10%, so the three lambdas will be small, like 1.25 or 1.2]. Then find out what is the volatility that results when the three 12% vol ...


0

You won't find a systematic approach, that would require the models to be arranged in some system, whereas generally each is an adjustment of GARCH. The best you can do is to know the usage cases of as many as possible and then use your own judgement as to which model is appropriate.


1

The concept of entropy in the financial field is related to the market efficiency and predictability one; the measure approximate entropy by Pincus (1991) is considered as a market efficiency measure and it has been empirically proven it is correlated to the main market efficiency measures as shown by Eon & Kim (2008) and Risso (2008). I suggest you to ...


0

For time-frames of >21 days you can just compute the standard deviation of daily returns over that time period and then annualize it to make it comparable. For the 5 days realized volatility the best option is really to use intraday data and sum the squared intraday returns.


0

Garch models are not good to predict "many" periods ahead, but for "very short" times. If you want to predict 2 months from here, maybe you should be working with monthly data. I did a similar exercise with some indexes (symb=c("^BVSP","^MERV","^DJA","^N225")) using daily returns from="1991/01/01", look the incredible predictions.



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