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seen Oct 16 at 17:09

Jul
9
comment American Swaption Heding with Malliavin Calculus
Sorry, I meant the other delta in front of the summation sigma in C. It might be just a constant? Hm, maybe it is not a delta at all... So the hat does not denote some kind of averaging but R^hat is simply a constant, correct?
Jul
9
comment American Swaption Heding with Malliavin Calculus
Could you define delta, Z and R^hat, please?
Feb
15
comment Change of order size
You do not lose queue priority then. But check for the exchange and market you are interested in. This might differ from exchange to exchange and from market to market.
Oct
20
comment Alternative liquidity measures
If you explain what Amhiud is, I might give you another one.
Oct
19
comment .NET statistical packages recommendation
It's not .NET or C(++/#), but you could use python or call it from these languages. There you could use pandas. If you can write C, you'll learn Python in an hour. Knowing Matlab would be a plus.
Oct
17
comment Order and position management in (semi-)automated trading system
Simple database: save sent orders, save broker/exchange response, i.e. logging. Positions are deduced from responses. History and currently active orders/positions should be separated (flag as active). Maybe I overcomplicated by thinking about design patterns.
Oct
14
comment Resources for performance statistics of trading systems
It's just monthly data, but maybe state of trend following is of interest to you. There are monthly posts at least for a couple of years. see automated-trading-system.com/…
Oct
13
comment Resources for performance statistics of trading systems
With 'successful trading systems' I see 2 difficulties: 1) 'successful' is debatetable and depends on your definition (is a year of non-negative returns enough, although ultimately you lost 75% in 3 years?) 2) I believe truely successful strategies are not disclosed and those that are, might suffer from survivorship bias. Maybe looking at (hedge) fund performances would be a viable alternative, although survivorship bias might apply as well.
Oct
10
comment Analyzing tick data
1, 2, 3 & 4 are options, not steps. Either 1, 2, 3 or 4. ad 2) treat time as some variable tightly related to the original time series, maybe forecasting both in order to know where price goes and when it goes there. ad 3) find some small time increment such that all oservations of the original time series roughly fit on some time of your new equidistant time series. ad 4) summarize your data maybe per 500 microseconds and create for example open/high/low/close information for each 500 microsecond batch
Oct
9
comment Switching from Matlab to Python for Quant Trading and Research
While I strongly dislike R's syntax and Python gives this pragmatic feeling of "I can get things done [quickly]", Python really lacks R's statistics tools. Python has a mid-size basis of statistics tools, but you need to call R for pretty basic stuff like seasonality/trend-decomposition, not to speak of more advanced statistics, but it partly compares with Matlab, I think. I believe something like the Rmetrics package isn't given anywhere outside of commercial tools yet, so that is definately something Python is lacking currently.
Oct
9
comment Backtesting VaR model violation independence
Although you seem to refer to Value at Risk (VaR), I wasn't sure in the first half of your question if you maybe refer to Vector AutoRegressive (VAR) models.
Sep
24
comment Trade Count Time Series
I am undecided which answer to accept. Since I believe that IB offers a lot of asset classes and exchanges and the count is supposedly then available for all history, I opt for this one.
Sep
23
comment Trade Count Time Series
Ok, so IB provides this data. Nice. This is not an option if you do not trade via IB, but for me and, I believe, many others this is ok.
Sep
23
comment Trade Count Time Series
Yes, I stumbled upon this link yesterday and figured that simply loading tick data might give a hint - how to interpret ticks? It is not the BBO I get, but the prices of trades that happened, right?
Aug
18
comment How to hedge against lack of volatility
What is "the 30 delta" here? Is it delta of 0.3?
May
19
comment How does one go from measure P to Q(risk-neutral) when modeling an asset paying dividends?
S under Q is supposed to return r (risk-neutrality) in total. Since it continuously yields delta, a drift of r under Q would yield r+delta. Thus risk-neutral drift is to be corrected by -delta.
May
18
comment How does one go from measure P to Q(risk-neutral) when modeling an asset paying dividends?
Yes, you are right. It should've been mu=mu*-r, i.e. theta=(mu*-r)/sigma with mu*=0 with your gBm assumption. This would've yielded (r-delta) under X.
May
18
comment How does one go from measure P to Q(risk-neutral) when modeling an asset paying dividends?
ad) "I assume you are referring to dS/S = mu dt + sig dz, but why does this stop being gBm if mu = 0?": In "The index is described as "following a geometric Brownian motion", which to me says that the there is no other drift going on" you seemed to deduce that mu=0 from the fact that the index is supposed to follow a gBm. I wanted to make clear to you, that although the index is supposed to follow a gBm, this does not imply that mu=0.
May
2
comment Good reference on sample autocorrelation?
Your claim that lim_{n->inf} sqrt(n) r_hat is supposed to be an element of the normal distribution does not make sense mathematically. A distribution is not a set. If you mean its support, that is the real line, an element of which your limit surely is. But there is little value in that claim. Besides, regarding autocorrelation basically autoregressive time series and fractional Brownian motion (and numerical approximation thereof) come to my mind. But I'm not sure wether either topic fits your background and time frame.
Apr
30
comment Stock Price Behavior and GARCH
miggety: "at each time step": Is that for each path and each time step, so maybe 100,000 GARCH recalculations per time step? Does the GARCH-result depend that much on 1 additional time step? I mean you add 1 data point each time step. @Owe Jessen miggety's GBM formula is the Euler-Maruyama discretisation of the SDE which yields an analytic solution like the one you are using, Owe. See GBM as well.