# Relationship between bid ask spreads and fluctuations in market prices

We next examine the relationship between the bid-ask spread and the volume Q, The ability to buy at a low price and sell at a high price is. Definition: Bid-Ask Spread is typically the difference between ask (offer/sell) price Ask price is the value point at which the seller is ready to sell and bid price is the . measures the fluctuations of a stock to changes in the overall stock market. and most liquid deciles; (6) the average return difference between the least . In securities markets with quoted bid and ask prices, .. (Jegadeesh and Titman, ) as well as of spread fluctuations (Madhavan et al., ).

These prices are determined by two market forces -- demand and supply, and the gap between these two forces defines the spread between buy-sell prices. The larger the gap, the greater the spread! Bid-Ask Spread can be expressed in absolute as well as percentage terms. When the market is highly liquid, spread values can be very small, but when the market is illiquid or less liquid, they can be large. Calculation of Bid-Ask Spread: So, all price points cannot be used to calculate Bid-Ask Spread.

This can be calculated by using the lowest Ask Price best sell price and highest Bid Price best buy price. The Bid-Ask Spread is one of the important trading points in the derivatives market and traders use it as an arbitrage tool to make little money by keeping a check on the ins and outs of Bid-Ask Spread. Bid-Ask spread is used in following arbitrage trades: When a trader buys the futures of a security having a particular expiry on one exchange and sells the same security contract with a near-expiry on another exchange, 2 Intra-market spread: The bid-ask spread partially characterize the liquidity of the market, which is one of the most important attribute of financial markets.

The focus of recent research has been to estimate the bid-ask spread, and its components, using transaction returns [3, 4]. In his seminal paper, Roll derives an implicit spread estimator in the equity market [5]. He uses a relationship between transaction price changes to estimate indirectly the effective spread in an efficient market [6], and his method only requires the transaction prices themselves.

To demonstrate the applicability of the high-low estimator to non-U. A question of both theoretical and practical crucial importance is to know what impact have the spread on the stock price fluctuation [1, 9, 10, 11, 12]. Roll [5] and French and Roll [10] show that serial correlation and variance of observed price changes are both affected by spreads. Here, we will develop a model of stock price evolution, which would exhibit statistical properties very close to the empirical findings [13, 14, 15].

Based on the developed model and on the two aforementioned spread estimators, we will show how bid-ask spread implies the positive autocorrelation in the absolute stock price returns. Also, we will show that our research suggests that bid-ask spread is responsible for the two-phase behaviour [15, 16] of the financial markets. Model pt Let pt be the price of a given stock. Without loss of generality we will focus on a one-period one time step case. A multi-period case will be considered later.

Personal preferences and interactions Further, based on the assumptions, personal preferences of agents and interactions between them are postulated.

If we assume that all trading is done at the return r, the gain of agent i is N! In a Nash equilibrium, every agent is doing the very best she can, given the actions of all others. It is evident that when all agents have reached such a point, none has any incentive to change unilaterally what she is doing, so the situation is regarded as an equilibrium.

Due to possible false or incom- plete information, this assumption may not always be true[24]. Analogous to the personal preferences, the interactions between agents can then be modelled with N! Thus, by minimising Eq. Finally, from behavioural finance it is known[25, 17, 26] that the agents choose from set of strategies close to equilib- rium in a different way than from set of extreme strategies.

The difference between August 30, Mentioned fact is usually modelled with log utility function[26].

Equilibrium probability measure Personal preferences define what strategies agents prefer individually, and interac- tions define how the other agents react when one agent decides to play a certain strategy.

Next, based on personal preferences and interactions, the equilibrium prob- ability measure of the stock price return is derived. The Gibbs probability measure converges to a Gibbs state for large N. Due to the translation invariance of energy functional, the Gibbs state is an equilibrium state of the system[27]. Using the probability measure Eq. We are particularly interested in the case of weak convergence[29].

According to the theory of truncated Levy distributions[30] the same result can be obtained for large, but finite M and N. Time dynamics of y will be defined under three natural assumptions. Second, return to equilibrium in terms of percentile change of uncertainty should be greater if uncertainty is greater.

Third, the return to equilibrium should not depend on the sign of y. Pursuant to the above, the deviation of the realised return from the estimated expected return serves as the input to Eq. The indicator function ensures that the discrete dynamical system Eq.

## Bid-ask Spread

The obtained distribution has fat tails and it is unimodal for low values of uncertainty, whereas for higher values of uncertainty, the shape of the distribution is bimodal. This is shown in Fig. Validation A validation is performed to show how defined model statistically matches the actual data. Furthermore, the time dependence of the two time series has been explored with the autocorrelation functions, Fig. The concurrence between them is found to be very good and consistent with the stylized facts[35, 32, 13] relating to time dependence of returns, zero autocorrelation for returns, and positive autocorrelation for absolute returns.

August 30, Comparison of probability density functions. The kernel based method was used for estimating the pdf from data. Comparison of the autocorrelation functions. Under Rolls ideal market assumptions, transaction prices can only bounce either at the ask price or at the bid price.

Based on that assumption and based on efficient market hypothesis [6] he derived the following effective spread August 30, Obviously positive values of the co- variance price changes represents a problem for defined estimator.

Several strategies exist in literature which resolves the problem [7]. The most common approach is to multiply the covariance by negative one, estimate the spread, and multiply the spread by negative one.

That approach we follow here and we also deal with the re- turns, rather than with the price changes. Further, uncertainty y defined in Chapter 2 enters into the proposed model for stock return evolution with square.

**What is Bid, Ask Price and Spread in Forex Trading - Hindi**

As our goal here is to connect dynamics of the uncertainty y with the bid-ask spread dynamics, we are primarily interested in the square values of the bid-ask spreads.

According to that, in Fig. Black line represents a square of the uncertainty estimation multiplied by Analysis is performed on the data set from Chapter 3.

Besides mentioned, there exist several similar spread estimators in literature [3, 36]. One of the most recent bid-ask spread estimator is due to Corwin and Schultz [7]. They derive an estimator for the bid-ask spread based on daily high and low prices.