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Systematically finding most liquid futures instruments Can we put together a better list than the academic articles? Yes! The lists in existing publications [1, 2] are great, but fall slightly short of your goal: I'm asking for a systematic, repeatable procedure for determining a list of what I expect to be around 100-300 markets instruments. [3] What'...


9

They are a lot of open problems in market microstructure. To have an idea of the whole landscape, have a look at Market Microstructure in Practice, 2nd Edition, by L and Laruelle. I would split them in From the viewpoint of exchanges Optimal fee schedules to "attract" liquidity (and hence efficient market makers), have a look at Optimal make-take ...


6

Defining asset classes from a quantitative perspective is an interesting question that is not really addressed "officially" as far as I know. Let's try to write some requirements you want strategic decisions to make sense: each asset class should have at least one or two different "economic drivers" than the others you want tactical ...


5

You've got your calculation of the spread wrong, for what you're trying to do. Looking at the spot prices: SGD = USD 0.8, MXN = USD 0.077, NOK = USD 0.16. So in descending order they are SGD, NOK, MXN. The order of levels on your chart is SGD, NOK, MXN. INR vs CHF is the same: CHF = USD 1.1, INR = USD 0.017, so you get a larger spread for CHF in dollar ...


5

With respect to what you need, you have to consider different aspects of optimal trading: the Almgren-Chriss framework (cited by Anna, since Jim and Alex -amongst others- extended it) focus on obtaining an optimal trading rate, it is nice but not really what you need. You can nevertheless use it to plan / schedule your trading during the day. but what you ...


5

In the paper Optimal split of orders across liquidity pools: a stochastic algorithm approach (2011) we present the theoretical aspect of liquidity seeking, thus you will learn how they work. There is a seminal (once again) white paper by Robert Almgren on iceberg chasing that is very informative too.


5

You can find a varying number of practitioners and academics on both sides of this debate. To be honest, the question of whether "High Frequency Traders" increase liquidity is ill-posed. The label is often misused and is broadly encompasing of too many different types of traders. So, in general: Any trader that posts resting limit orders is adding ...


5

You can try using different approaches. Starting from something not that "heavy" like the NN. 0) Pre study - you need to prepare your data (how you will treat a negative spread (i.e. ASK - BID <0), what will you do if you will have 0 spread and then you will divide some value by it?), - plan your research ahead - how will you divide your limited data ...


5

The better price will come from two live traders (one on each side of the trade) willing to take a smaller percentage commission for a large block trade. For example, if a trader's average commission per day is USD 2,000 and someone sends them a 100M block trade, they aren't going to insist on their standard commission rate. Let's say they usually get a .5%...


5

The three ways to manufacture pseudo-implied vols I know of are: Find a related underlying and, even if only few options trade on it, 'borrow' its implied vols. Compute statistical vol from historical underlying prices (not strike dependent, still useful to know). Compute breakeven vol, still based on historical underlying prices, strike dependent, by ...


4

You can use refined methodologies but if you just need a rough estimation of liquidity, you can simply use an average of daily volume over N days. In practice, for equities, people tend to use N = 20 or 30. Once you have the average daily volume (say 100,000 shares), you compare it to your holding (say 50,000 shares) to determine the the size of your ...


4

The two types of orders are called "Attributed" and "Non-Attributed". Venues will sometimes provide incentives to encourage order attribution. For example, Direct Edge has their "Edge Attribution Incentive Program" which you can read about on their price list. I believe NASDAQ has offered incentives for attribution in the past, but I don't think they do ...


4

In practice, this equation won't even hold for the vast majority of bonds in the US Treasury market, which is the most liquid government bond market. The chart below shows the spreads of US Treasuries relative to a fitted curve (more specifically, a model price is calculated for each bond by discounting its cash flows using a theoretical zero coupon curve. ...


3

Breakpoint approaches Test based To be well received in a financial econometrics journal, you want test-based approaches. Depending on your question it is common to see a linear regression (least squares) where the parameter suspected of breaking is interacted with an indicator function $I(E)$ where $E$ is the event in question; this function assumes a ...


3

Definitely time series analysis. What you essentially want to do is some form of impact analysis. this can be done naturally using multivariate time series models like Vector Auto Regression models. Also when working with data to model liquidity you might want to use some specialized procedures like GARCH and ACD. Further there are methods to model non ...


3

Research where the liquidity is, Who are the holders and who have historically been the buyers. Getting insight who the buyers and at what price level they would sell (or buy more) is a good technique. Often time ownership information is available to the public information. Once you figure out such levels then you know the price levels you can provide ...


3

The cost of forex trading is reflected through the bid-ask spread you pay as a retail client to a broker. period. There spread IS indicative of the cost of trading the pair, AT that specific point in time (and OANDA does not reject your trades or recall trades on any rates they offer at a specific time, up to a specific trade size). So what you are doing ...


3

Liquidity traders have no discretion with regard to the timing of their trades. Their trades are triggered by exogenous (to the financial market) reasons and are not related to information. Then we can not guess/forecast their trades and that's why we can consider the quantity (not the price) they ask/offer as random variables. An academic definition : Two ...


3

There is a cascade of methods to choose a settlement price in futures markets - starting with trades in the relevant market, and going through trades in other expiries (plus spreads), quotes, quotes in spread markets, trades in related markets, previous day’s settlement prices etc. This answer to a related question may be helpful - https://quant....


2

My blog discusses this at length: https://mechanicalmarkets.wordpress.com/2015/02/16/protecting-client-interests-anonymity-in-us-equities/ . To quickly summarize, as Louis Marascio said, DirectEdge pays brokers to disclose their MPID. Another possible incentive is the broker trying to maximize their order's chance of execution; if by disclosing their MPID ...


2

If you are after treasuries, you can check http://www.newyorkfed.org/research/staff_reports/sr381.pdf which discusses trade impact on BrokerTec. If you are after equities, the literature is enormous, you can pretty much google for "trade impact limit order book" or smth similar. In practice, it's an empirical approach: you put all the factors, that seem ...


2

You should turn to market microstructure research. Large and frequent trades can temporarily increase the spread and observed transaction price. Additionaly, trades done near the release of new information ( macro news, firms news,...) most likely need to overcome larger spreads.


2

You're going to have to do a lot of guesswork, obviously, so it's best to keep things mathematically simple. First off, choose a "certainty level" as some quantile $q$, perhaps around 0.9, and the corresponding normal variate $z=N^{-1}(1-q)$. Start by figuring out how much time $T_i$ you think each position $N_i$ will take to liquidate if necessary. Then ...


2

Generally if they are missing a completely at random data in few places, you do not have to be worried. I advice you to use one of the technics of imputation: - Previous value - cannot be used in this case - Educated Guessing - you have "knowledge" about the data, you can try to use some interpolation in your mind. - Common-Point Imputation - try to ...


2

I agree with all Robert says above, but if you already have the data, and you want to quickly create a neural network model and run the analysis, I would suggest the following: The Heaton Site has a Wiki, links to papers, links to books, a forum, etc. that will help you get started, but you might try the PluralSight course Introduction to Machine Learning ...


2

I would consider Amihud (2002) as a good first approximation with that level of data.


2

Seems like the least interesting pattern of all - data errors at the Yahoo's side. Just checked with Bloomberg - nothing similar is present. If not a data error, this could be bid-ask bounce (dismissed with here since you mention it's a liquid stock).


2

I thought a little bit more about your problem and can suggest an analogy. I work in fixed income which deals with IBOR reference rates. One of the outstanding questions is often about the IBOR basis - how much higher a 6M rate should be compared with a 6M period generated by two rolling 3M rates, or 6 rolling 1M rates. From a lending banks' perspective each ...


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