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There are several. This list is from Giyenko et al (2008)---in their work they compare all these different measures--- and includes spread proxies and price impact proxies. As for spread proxies: "Effective Tick" (Holden 2007, Giyenko et al 2008) "Holden measure" (Holden 2007) "LOT Y-split" (Giyenko et al 2008) "Roll measure" (Roll 1984) "Gibbs measure" (...

6

From an academic viewpoint you do not have a lot of choices: The Rosenbaum-Robert approach, the price model with uncertainty zones is a model of trades and duration between trades (implicitly). It is worthwhile to try it. You can also use an Hawkes process, it will have the nice effect of capturing clustering effects on trades. if you want to use ...

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'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

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%...

4

That's an interesting question, and I believe your example does indeed show that the answer is "yes". However, just because you paid a lower average price doesn't mean that there isn't market impact, especially if the writer of the option was naked (didn't have the stock already and had to buy it himself on the open market). It's just that the holder of the ...

4

Using intra-day data, the concept of viscosity is easier to define. At the microstructure scale, you can see the price moves as a diffusion constrained by the quantities in the order books. Viscosity is a mix of pressure of volumes, rounding by the tick size, and bid-ask bounce. See for instance A New Approach for the Dynamics of Ultra-High-Frequency Data: ...

4

The common practices are: if you trade less than 8% of the Average Daily Volume, you can use a VWAP or Implementation Shortfall algo. you need to "add" a slippage of 1/3 of the bid ask spread of the stock. Your only issue is that you want to use the close price instead of the VWAP one. Best option is to use the daily VWAP as a proxy. Otherwise measure the ...

4

Here are some practical application for trading illiquid names: For pricing/forecasting: You still still calculate fair volatility using stock print data. In an illiquid stock and there's a large open interest in the market where professional traders are long, vol might diminish since when stock goes up(down), all the vol traders would be selling(buying) ...

4

I would look at the following metrics when quantifying "liquidity" in listed options: bid/offer spread number contracts traded and from that follows notional traded (in the option not underlying) frequency of bid/offer adjustments relative to changes in the underlying delta. frequency of liquidity added/removed on the bid and offer side even when no trades ...

4

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 ...

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

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

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

Whether or not to exercise an option when the underlying is near the money can be a very complicated problem that depends on much more than simply whether or not the underlying is just over or under the strike price. Options traders refer to this as pinning, which tends to happen much more often than you might expect if stock price movements were truly ...

3

First: once you will have your liquidity indicator, you will need to know if the signal is worth the risk to go faster (or slower if it is a negative signal). Impulse control will tell you that: http://www.ceremade.dauphine.fr/~bouchard/pdf/BML09.pdf Optimal control of trading algorithms: a general impulse control approach, by Bruno Bouchard, Ngoc Minh Dang, ...

3

I just reviewed the paper Corporate Bond Liquidity Before and After the Onset of the Subprime Crisis by Dick-Nielsen, Feldhütter and Lando. They define a liquidity measure $\lambda$ as a conglomerate of price impact (Amihud) and its variability spread covariance (Roll) and its variability turnover imputed roundtrip cost (Feldhütter) zero trading days I ...

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 : ...

2

The "Navigating Liquidity" serie by CA Cheuvreux's former quant research team addresses different means to measure liquidity in a post MiFID (in europe) and post Reg NMS (for US) world. Unfortunately, CA Cheuvreux having been sold by CAcib, most of the links in the upper google search seems to be broken. You will find informations about measuring liquidity ...

2

No, exercising an out-of-the money option is never worth it. In your scenario, you should start buying at \$10. Keep buying until you push the ask up to \$10.09, then exercise however many options it takes you to get to 10,000 shares. This will get you your 10,000 shares at a lower cost than simply buying them all for \$10.08 through exercising your ...

2

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 ...

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

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.

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