On Dealer Markets Under Competition

Ho, Thomas; Stoll, Hans R.

doi:10.2307/2327382

Cited by 68 publications

(56 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inventory costs force the dealer after every trade to adjust his prices. When conducting a trade at the ask, he will increase his quote and decrease the quote when having conducted a trade at the bid, see Stoll (1978), Ho & Stoll (1980) and Ho & Stoll (1981) for inventory based models of market making.…”

Section: The Modelmentioning

confidence: 99%

Inventory Effects on Daily Returns in Financial Markets

Krause

2003

Int. J. Theor. Appl. Finan.

View full text Add to dashboard Cite

We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We also consider GARCH effects in daily return series and show that estimates using daily returns are biased from the influence of the level of prices. Using daily price changes instead, we find evidence of a significant GARCH component. These results suggest that microstructure elements have a considerable influence on the return generating process.Keywords: inventory control, bid-ask spread, volatility dynamics, GARCH JEL Classification: C22, G10It is a well known fact from a large number of empirical investigations that financial return series exhibit specific patterns, e.g. positive autocorrelations in volatility. Many of these patterns have been modeled using GARCH processes as introduced by Engle (1982) and Bollerslev (1986) and developed further by many other authors. Bollerslev et al. (1992) and Diebold & Lopez (1995) give an overview of these models as well as their empirical evidence.Another empirical finding is the positive relation between return volatility and trading volume. A large number of contributions address this issue with theoretical as well as empirical investigations, see e.g. Foster & Viswanathan (1990), Foster & Viswanathan (1993a), Foster & Viswanathan (1993b, He & Wang (1995), Aoki (1999), Chen et al. (1999), Focardi et al. (1999 or Iori (1999) as few examples. A common approach in many of these models is to assign observed effects to the processing of private information.

show abstract

Section: The Modelmentioning

confidence: 99%

Inventory Effects on Daily Returns in Financial Markets

Krause

2003

Int. J. Theor. Appl. Finan.

View full text Add to dashboard Cite

show abstract

“…Researchers have shown that volume, number of shares traded and number of transactions are significant determinants of the bid-ask spread in stock markets. 14 Inventory control models, like in Ho and Stoll (1980, 1981, show that uncertainty in the arrival of buy and sell orders drives dealers away from their optimal inventory position. As in Amihud and Mendelson (1980), when the trader approaches his desired inventory book, the bid-ask spread would be reduced.…”

Section: -1 Motivationmentioning

confidence: 99%

“…1 Schwartz (1988) identifies four classes of variables the literature focuses on as determinants of bid-ask spreads in financial markets: activity, risk, information and competition. Ho and Stoll (1980, 1981 show that uncertainty in the order flow limits dealers' ability to maintain their optimal inventory position.…”

Section: Introductionmentioning

confidence: 99%

The Microstructure of Currency Markets: An Empirical Model of Intra-day Return and Bid-Ask Spread Behavior

Pasquariello

2001

SSRN Journal

View full text Add to dashboard Cite

This paper analyzes the intra-day relationship between bid-ask spreads and "market" return volatility for quotes posted in a truly global "around-the-clock" market setting, the one for the U.S. Dollar/Deutschemark exchange rate. We are able to identify a statistically and economically significant "Reverse U-shaped" pattern in the bid-offer spread in 1996. Tests of the stability and ordering of "market" volatility, performed across several different fractions of the day, reveal that variances of intra-day returns are heterogeneous and ordered, declining around the Asian lunch break, increasing steadily during the London morning trading hours, peaking at the opening of New York to subsequently fall with the closing of the European markets. Results also indicate that "market" volatility is significantly higher during intra-day versus overnight periods. Then, we introduce a structural model that attempts to explain those empirical regularities by capturing some currencyspecific features of the data: possibly asymmetric and stochastic trading cost structure, discrete directional updates and parameters' temporal heterogeneity, and by relating the bid-ask spread to two different sources of variation: trading costs and market risk. We evaluate these components via GMM using a set of convenient unconditional intra-day moments implied by the basic configuration of the model. Analysis of the resulting estimated patterns reveals that trading costs play a significant role in explaining the intra-day variability of bid and offer currency returns. Inventory considerations appear to be more relevant in the trading morning and by London's closing, while the perceived risk of arrival of informed trades seems more likely to affect the dealers' cost structure during the late morning/early afternoon in London, when the bid-ask spread is highest. The contribution of the "true" currency risk to the total variability of the posted bid and ask quotes' returns is not surprisingly highest at the opening of the European markets. Jumps are more likely to occur during the London morning hours and less likely by the end of the day. Additionally, the estimated upward jump size is higher than the estimated discrete downward adjustment. This evidence suggests that during 1996 central banks unsuccessfully attempted to resist the devaluation trend of the Deutschemark versus the dollar. JEL Classification: C51, F31

show abstract

“…The optimal inventory and price setting policy are derived from the tradeoff between risk and return for each agent. Ho and Stoll (1980) and Ho and Stoll (1983) focus on the competition among dealers by deriving equilibrium inventories and market spreads. Grossman and Miller (1988) model liquidity events for risk-averse investors resulting in an immediate need to trade the security.…”

Section: Introductionmentioning

confidence: 99%

Price Dispersion in OTC Markets: A New Measure of Liquidity

2008

View full text Add to dashboard Cite

In this paper, we model price dispersion effects in over-the-counter (OTC) markets to show that, in the presence of inventory risk for dealers and search costs for investors, traded prices may deviate from the expected market valuation of an asset. We interpret this deviation as a liquidity effect and develop a new liquidity measure quantifying the price dispersion in the context of the US corporate bond market. This market offers a unique opportunity to study liquidity effects since, from October 2004 onwards, all OTC transactions in this market have to be reported to a common database known as the Trade Reporting and Compliance Engine (TRACE). Furthermore, market-wide average price quotes are available from Markit Group Limited, a financial information provider. Thus, it is possible, for the first time, to directly observe deviations between transaction prices and the expected market valuation of securities. We quantify and analyze our new liquidity measure for this market and find significant price dispersion effects that cannot be simply captured by bid-ask spreads. We show that our new measure is indeed related to liquidity by regressing it on commonly-used liquidity proxies and find a strong relation between our proposed liquidity measure and bond characteristics, as well as trading activity variables. Furthermore, we evaluate the reliability of end-of-day marks that traders use to value their positions. Our evidence suggests that the price deviations from expected market valuations are significantly larger and more volatile than previously assumed. Overall, the results presented here improve our understanding of the drivers of liquidity and are important for many applications in OTC markets, in general.

show abstract

On Dealer Markets Under Competition

Cited by 68 publications

References 0 publications

Inventory Effects on Daily Returns in Financial Markets

Inventory Effects on Daily Returns in Financial Markets

The Microstructure of Currency Markets: An Empirical Model of Intra-day Return and Bid-Ask Spread Behavior

Price Dispersion in OTC Markets: A New Measure of Liquidity

Contact Info

Product

Resources

About