2,522 reputation
414
bio website quant.stackexchange.com
location United States
age 37
visits member for 3 years, 6 months
seen Aug 12 at 2:12
  • primarily trade options(listed equities).
  • math background + quant masters
  • algorithmic developer

Dec
21
comment What is the best method to compute project volatility in Real Option Valuation?
I just clicked the link and the paper came up fine... "ESTIMATING PROJECT VOLATILITY AND DEVELOPING DECISION SUPPORT SYSTEM IN REAL OPTIONS ANALYSIS"
Nov
11
comment Probability of touching
@WilliamS.Wong - the OP is about probability of touching, before expiration. Read it again.
Aug
8
comment What is the best live options data API?
Nanex is the best reasonably priced for listed options/equities, and the performance is really good with the advanced feed compression that they do. It can be accessed from C, C++, C#, Java, etc... they have several helpful starter examples on their site.
Jan
12
comment Is equity market making a game of speed?
The quote moves in response to trades, or other quotes, not directly in response to moves in the market. The point I'm making is that you don't need to be 'fast' per se, you just need to be about as fast as the other market makers in your stock. That is sufficient.
Jan
9
comment Is equity market making a game of speed?
I suppose it comes down to how one defines 'fast' (seconds, milliseconds, microseconds, etc...). Low liquidity generally means that there are fewer trades going on. The speed necessity tends to be a result of increasing levels of competition for order flow. Low liquidity stocks typically dont have that much competition for order flow, so relative to highly liquid issues(more competition), a market maker does not have to be as fast to get the flow in an illiquid market.
Jan
9
comment How sensitive are vertical spreads to changes in implied volatility?
Yes, because an increase in vega means an increase in option premium, so if you're long an option that's an increase in your PnL line, and the opposite is true if you're short an option. In a verticle spread, you're long and short options for the same expiry. When the strikes of these options are somewhat close to each other, this effect (higher vega increases option premium) causes the PnL from both options to offest giving the PnL graph the flattening tendancy that I mentioned.
Dec
30
comment how expected moves are priced into options
calculating the move and its likelyhood, from the option price is not difficult, but that should probably be a separate question.
Dec
20
comment What is the market standard for pricing VIX futures?
Theoretically, any future can be replicated with a bank account and the underlying. In practice, in this case however, the difference in expiry and settlement make that kind of direct replication infeasible, without basis risk. As I said, a good theoretical model to use is variance gamma. A good approach to use in practice is to use the options on SPX. Also not a precise hedge/pricing instrument, but very liquid which cuts out a lot of risk. You can find some articles about both of these issues on the CBOE's website.
Oct
28
comment In a covered call strategy, should I hold the call or sell/roll if the delta becomes too small?
@CQM, thanks for the feedback. Do you mind marking this the accepted answer?
May
6
comment What is the best method to compute project volatility in Real Option Valuation?
heres's another simulation approach(not only for correlated inputs, but with more math): etd.auburn.edu/etd/bitstream/handle/10415/147/…
Apr
22
comment What type of investor is willing to be short gamma?
The only thing I'd add to this is that gamma is typically the tail wagging the dog in these trades. Trades that invole short gamma, particularly when described in terms of "rent", are attractive because of being long theta and willing to risk being short vol. Gamma is seldom a consideration because it's effects are not very pronounced until very close to expiration. The exception here is clearly if you are using close to expiry options as a hedge for the delta of a portfolio.
Apr
22
comment What type of investor is willing to be short gamma?
"being short gamma is being long volatility" - false. short gamma = short vol. "For ITM options, being short gamma is being long the underlying. " - false (eg short call (itm or otherwise) is short the underlying).
Apr
21
comment How does return-based analysis calculate expected return of a trading system?
instead of "clearly indicating that you should use this trading system" I mean clearly you should NOT use this system. Just compounding the returns returns (1.25*1.25*.6 - 1) gives u -6.25 (you can't add %'s together, you have to multiply them then it works.) (geometric return does this)
Apr
20
comment How does return-based analysis calculate expected return of a trading system?
@MilkTrader: don't use the arithmetic mean to calculate the expectation, use the geometric mean. this shows you the negative expectation in the case of your example, and the rest of the analysis still works. Geometric mean requires that all of your data samples are on evenly spaced data(like daily in your example). Also geometric mean is guaranteed to be always less than or equal to the arithmetic mean. (in your example the geometric mean is 93.75%, clearly indicating that you should use this trading system)
Apr
20
comment How does return-based analysis calculate expected return of a trading system?
@Milktrader Ahh, I see what you're saying, (trade vs return). This is why I mentioned the part about, "any level of granularity that you have data" So if daily returns is what you have, you still compute the average daily return, then look at min daily return, max daily return, and build a histogram, normalize, and go from there... This is a long way to say, YES... return based analysis uses the same approach as transaction based.
Apr
19
comment How does return-based analysis calculate expected return of a trading system?
@Milktrader: Technically it's the average of the daily returns. That is the expectation on any given day. However, normally this is not sufficient information. To make it useful one needs to look at winning trades vs losing trades and generate a histogram and then normalize(z-score) so that you end up with a confidence interval that allows you to say X% of the time the daily return will be within Z standard deviations of the expected daily return. Then you can choose how to size/risk decision upcoming trades.
Apr
18
comment How does return-based analysis calculate expected return of a trading system?
and don't forget the most important metric... max drawdown.
Mar
25
comment Heuristics for calculating theoretical probabilities of being ITM at time T for listed options
@Dragan: A heuristic for that is to calculate the breakeven, at price of the underlying for a given position, then take the delta for an option at that strike. that is your probability of success for the trade. (the probability that the underlying reaches that level by expiration it works for any options position)
Mar
22
comment Keeping a track record honest
@barrycarter: tweets, brokerage statements, and attestation by a CPA will get it done. Assuming that this question is not purely academic... what you want is more subscribers, right? With social media, if your service is valuable you can get other subscribers to provide the social proof for you simply by providing valuable selections. They'll retweet, or repost, or whatever... and then the 100% non-repudiation isn't an issue. If someone follows you closely for any length of time, they'll notice if you are playing delete games with your tweet stream. Don't worry about it.
Mar
20
comment Discrete-time model: stock dynamics
@Gortaur: All of those models are suitable for both currencies and stocks. There are several non autoregressive models that are applicable for stocks, but don't work well for currencies.