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Jun
11
comment Required Rate of Return vs Expected Return
@XiaowenLi, well you can chose your own wording on this site, can't blame my neighbor for failing the exam because I copied the wrong answer off his answer sheet ;-)
Jun
11
comment Required Rate of Return vs Expected Return
With CFA exams just having taken place, is this a CFA question?
Jun
9
comment How do you explain the volatility smile in the Black-Scholes framework?
@Gracchus, Gracchus/JoeTheCoder, when will you settle on which answer you wish to accept of your question you asked 7 months ago? You seem to change your mind every single week on numerous questions you asked. Any rational behind that?
Jun
7
comment Change option B&S pricing
So what work have you done so far (re answering this question)? Btw this site is intended for practitioners in the quant industry your question looks awfully like homework.
Jun
7
comment Continuous returns for negative roll-adjusted futures data
You should not derive cumulative roll adjustments. Simply calculate the roll adjustment each time you actually roll the contract and then derive the backward adjustment factor. This adjustment factor will never be negative and because you multiply/divide it with your past pricing data you should never arrive at a negative price at any point.
Jun
5
comment Usage of Bollinger bands
Bollinger bands (aka standard deviation envelopes) are most often used in non-proprietary indicators by data vendors, including Bloomberg. There is no edge in using them as all they do is measure historical realized price variation off a defined mean.
Jun
5
comment Quantitative risk management strategy for a large participant in an illiquid market
What you are asking sounds like something most people like to keep to themselves. It is similar to someone asking for a VWAP strategy that can handle highly illiquid stocks that trade in a 3-5 tick range in a given trading day. It makes no difference whether it is an actual trading strategy or a risk management strategy. A successful approach presents and edge and I think most people like to keep an edge to themselves. Just my hunch.
Jun
4
comment Risk prediction based on financial statements
I do not see how this relates to quantitative finance, given financial statement analysis is pretty much the antidote to financial mathematics. With all the accounting gimmicks (some corporations hold more off-balance sheet assets and liabilities than on-balance) its a moot point to derive meaningful conclusions regarding risk and expected return by looking at financial statements only. Sadly, in today's time the CFO's most valued skill set is in making accountants sign off on massaged balance sheets and income statements. What more to say?
Jun
2
comment Aprox intraday implied volatility using intraday option prices and EOD greeks
Just to give you an example, if you only use IV(t-1) and OptionPrice(t-1) as well as OptionPrice(t) you may witness an unchanged option price from t-1 -> t and incorrectly deduce that IV must be about the same, while the price of the underlying moved down in the same observation period and IV exploded. Also, this is a very bad approach for short-dated options because you ignore delta decay as well as the effect of changes in vega with respect to the passage of time.
Jun
2
comment Aprox intraday implied volatility using intraday option prices and EOD greeks
@chrisaycock, fair point made, yes of course one can approximate anything given there are past reference data that can be regressed. But as I said as long as you look to trade off such IV data approximations are not enough and introduce too much error due to the non linearity. If you can accept a reasonable estimation error then you can use the previous IV and underlying price relationship and derive the estimate of IV on the changed price of the underlying. But there is a reason why most all vol desks apply greeks in case they work off static IVs.
Jun
1
comment Aprox intraday implied volatility using intraday option prices and EOD greeks
no there is not (because the IV<->Price relationship is not linear, hence the name non-linear product), but there is an exact way to calculate your IV from the option tick prices and ticks of the underlying. Not sure, though, what benefit you would derive from doing that.
Jun
1
comment Blackbox Optimization + Bootstrapping = Parameter Selection?
@amirsani, I do not understand what else you like to know. Bootstrapping in statistics is nothing else than simply a re-mapping of the population of sample data so that you your sample becomes the population and a sub-sample the sample. Therefore, you can know the inference error when making an inference from sample->population statistic. There is no law or rule how you divide up the buckets, in fact one bootstrap technique is using overlapping sampling periods.
May
30
comment Portfolio software that shows 'total return' for each investment
Yahoo finance data should provide what you are looking for. I second what Jeff said: do not make your life harder by introducing a software application. Just add to your spread sheet.
May
30
comment Dynamic hedging strategy example
Could you at least disclose that this is some sort of homework. Its incredibly hard to come to any other conclusion.
May
30
comment Blackbox Optimization + Bootstrapping = Parameter Selection?
If you could express your question in simple terms then I may want to help out if can. The current format sounds incredibly complex and honestly speaking I do not follow at all.
May
27
comment Which prices to use to compute realized volatility?
@Ilya, I do not understand the second part of your comment. So which intraday volatility model do you apply? If it requires O/H/L/C data points then just use those (mid points of bid/offer or trades), if your model dictates the usage of compressed data points throughout the trading session then compress as much as necessary to get sane readings, meaning a 1-minute bucket where prices jump all over makes little sense, better to use larger compression and end up with less buckets but better quality of data that produce meaningful results.
May
25
comment So many volatility models. Any comparisons of them?
@Jase, you can compare anything, whether you derive meaningful conclusions from your comparison is a different issue. I do not believe one derives value from comparing volatility models that are almost entirely based on historical prices with a model that captures market expectations of future price levels and/or variation. One is used to assess past price return variation, the other to price contracts with future contingent payoffs.
May
25
comment So many volatility models. Any comparisons of them?
They are not comparable either. Forecasts of realized data fall into the same category as Garch models (though Garch models additionally incorporate Brownian Motion). Implied volatility models do not deal with realized pricing data but current prices and expected values.
May
24
comment So many volatility models. Any comparisons of them?
@Jase, because those are different tools aiming to target entirely different questions. (Sample) standard deviation measures variation in past price/return data whereas implied volatility expresses expectations of future asset/asset return variation.
May
23
comment Replicating strategy in the Black-Scholes model
@Mark, I think his pre-publishing notes are still available for free as pdf download somewhere. Google and you shall find. Btw, unless I am mistaken those are his lecture notes which he compiled to his two books, one on Discrete the other on Continuous time series. And the notes are not copyright protected and can thus be legally obtained for free. But if you intend to make a career in this field I highly recommend to get his books. In my opinion the best treatise of introductory stochastic calculus