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8

Volume merely indicates how much buy-side interest exists in a stock. For liquidity, the sell-side interest is more relevant, which implies the quote characteristics (the limit-order book). In addition to the bid-ask spread, I look at the top-of-book quote size. Here's an example from BATS: sym | bid ask bidsize asksize ----| ...

7

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" ...

7

From Implied Liquidity : Towards stochastic liquidity modeling and liquidity trading We will call the parameter, ﬁtting the bid-ask spread (under a symmetric distortion) around the mid price, the implied liquidity parameter. Hence for the European Call option (strike K and maturity T ) with given market bid (b) and ask (a) prices, the implied ...

6

For my master thesis, I used the bid-ask spread as a liquidity measure. Intuitively, it is the price to the have the liquidity (or even the price of liquidity); the bigger the bid-ask spread, the lower the liquidity. I know that Carlo Acerbi of MSCI is also looking into liquidity risk management and has a very interesting model for liquidity which is ...

5

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

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 ...

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

For a non-listed company you usually have a poor idea of the share value. If you do not know the underlying price it is impossible to accurately estimate the option price. So, for cases like this, people typically analyze the options on a risk/reward basis, using similar scenario analyses to those used to analyze the cashflows, assets and other components ...

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 ...

4

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 ...

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 ...

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

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

You can have a look at what the guys at Nanex. Here is an example of what they look at. The chart is colour coded for market depth (the colder the colour the less depth)

3

To search for "stop-hunting" I would search for stop orders and doing so on ssrn turned up the following: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=920687 But if you're looking for something that addresses what's in the body of the question then I'd search for something entirely different. When one knows there is a large cluster of orders in the ...

3

In general liquidity is most often modeled, for most types of instruments, via proxy by the 'bid-ask spread' (wider = less liquid, narrower = more liquid) You can choose to model the bid-ask spread in dollars, or what is often most helpful, for options, is to model the bid-ask spread in terms of implied volatility (of the difference between the bid and ask ...

3

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.

3

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 ...

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

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 ...

2

The academic term for this phenomenon is "predatory trading." Brunnermeier and Pederson (2005) wrote an entire paper on the topic, and you can also examine the references contained therein. This paper studies predatory trading, trading that induces and/or exploits the need of other investors to reduce their positions.We show that if one trader needs ...

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

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, ...

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

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 ...

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