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16

ML/AI systems are susceptible to a number of risks not traditionally discussed in risk management: What I call 'backtest arbitrage'. In the process of automated model generation and testing, your machine learner may discover, exploit, and concentrate on irregularities in your backtesting system which do not exist in the real world. If, for example, your ...


12

One of the reasons the ARCH family of models is used is that you only need price data to generate the model. These data exist back to the 1800s, so ARCH is great for looking at volatility over very long periods. I don't know that I'd say that the ARCH model has a lot of problems -- it solved the problem of not allowing volatility in time or in the level of ...


10

The risks involved in trading is everywhere and always a multifaceted thing: it includes the volatility of the selected asset, the leverage and concentration of the porfolio, whether there is a stop loss, a hedge, etc. Also, risk management is frequently not tied to the "alpha model" directly (e.g. VaR, shortfall, and scenario testing). For instance, one ...


8

GARCH(1,1) is a "standard approach for modeling volatility" mainly in academic literature. Most of us in the real world don't use it. Volatility forecasting tends to come more from looking at more-liquid comparables for future market volatility than from fitting fancy retrospective models. As for ignoring the dependence of residuals, well, folks are ...


5

stochastic vol and Levy process models are popular. Jump diffusion less so. FT techniques are definitely used. These days most of the focus is on valuation adjustments for vanilla products rather than how to price structured products. It tends to use both MC and lattice methods. If you want to be topical, I'd advise something related to valuation ...


5

Maybe not really an answer, but a justification of your approach. It's likely that your results can be expresses as $$ \mathsf EX_1 = 1.2\text{ and }\mathsf EX_2 = 2 $$ where $X_i$ for $i=1,2$ is a random pf of a situation in a class $i$ (we denote it $S_i$). Your method solves the following problem: given a fixed number of trials we would like to ...


4

It seems that the daily range would be based on the open. The close is just part of the range of that day (it must fall within the range, it just happens to be the last transaction of that day). From a practical perspective, if you were looking for non-normal price deviations, you could not calculate whether the price at time N is within its normal ...


4

...and assuming the stock opens at 48 and closes at 50 with a high of 51 and a low of 47 the percentage ranges will be 8.3% and 8.5%, the point being that your percentage measure of the range is determined entirely by the price level(s) of the denominator(s). This is an important caveat as these levels will change as the price bar data becomes historical ...


3

Personally, I prefer the book Foreign Exchange Option Pricing by Iain Clark and the book FX Options and Smile Risk by Antonio Castagna. The book FX Options and Structured Products by Uwe Wystup is also good.


3

LSM is very fiddly. The most important things in my view are 1) don't believe anyone who says that the choice of basis functions doesn't matter. 2) implement an upper bounder, eg Andersen--Broadie (2003) or Joshi-Tang (2014) so you can tell if your prices are good 3) do two passes, one to build the strategy, one to price, if they give very different ...


2

It depends on what the strategy does. For a long/short signal on an equity symbol, one way is to look at the options prices / implied volatility for that symbol. Your system should give an expected timeframe and profitability, so the risk involved could be quantified by the price of buying options to insure yourself against losses compared to your expected ...


2

Neither. This question and its references suggest that the Philipis Ouliaris (1990) test is a significant improvement over EG ADF and JCT. Given the automation, you should probably run all three tests and see if there's any consensus. It actually surprises me there isn't (at least in Eviews) a function that shows a table of the EG, JCT, and PO results ...


2

Well, well ... First of all, i doubt people would think of hedge fund strategies in the way you are thinking. If I were to classify, the first order of a super high-level classification would be, for example, equity stock selection (e.g. say Apple vs Google, etc.), macro selection (e.g. currencies, commodities, country bets via stock indices, etc.), or bond/...


2

GARCH models were developed by Robert Engle precisely to deal with the problem of auto-correlated residuals (which occurs when you have volatility clustering for example) in time-series regression. To ask "Why are GARCH models used to forecast volatility if residuals are often correlated?" misses this point.


1

Would it be OK to mix put/call prices such that I only ever calculate implied volatility for in-the-money options? No. Use OTM options because they usually have narrower bid-ask spread. Ideally you calculate all IVs, and then use highest bid IV, smallest ask IV. If so, I assume this surface can then immediately be used to calculate ... Yes, then you ...


1

If you calculate a var swap using SPX term structure implied vol versus a GARCH(1,1) estimated on 2yrs of past prices, you may see the first 4-5 (non-weekly) expiries offer roughly constant premiums over realised (negative varswap value) which would suggest at least someone is pricing using GARCH in the market.



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