nep-mst 2018-10-29 papers

on Market Microstructure

Issue of 2018‒10‒29
two papers chosen by
Thanos Verousis

Mid-price estimation for European corporate bonds: a particle filtering approach By Olivier Gu\'eant; Jiang Pu
Liquidity Pricing of Illiquid Assets By Gianluca Marcato

Mid-price estimation for European corporate bonds: a particle filtering approach

By:	Olivier Gu\'eant; Jiang Pu
Abstract:	In most illiquid markets, there is no obvious proxy for the market price of an asset. The European corporate bond market is an archetypal example of such an illiquid market where mid-prices can only be estimated with a statistical model. In this OTC market, dealers / market makers only have access, indeed, to partial information about the market. In real-time, they know the price associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client (D2C) markets, they know the result of the requests for quotes (RFQ) they answered, and they have access to composite prices (e.g., Bloomberg CBBT). This paper presents a Bayesian method for estimating the mid-price of corporate bonds by using the real-time information available to a dealer. This method relies on recent ideas coming from the particle filtering (PF) / sequential Monte-Carlo (SMC) literature.
Date:	2018–10
URL:	http://d.repec.org/n?u=RePEc:arx:papers:1810.05884&r=mst

Liquidity Pricing of Illiquid Assets

By:	Gianluca Marcato
Abstract:	So far the main body of the asset pricing literature has computed liquidity risk premia for either markets or single assets. The vast majority of these studies have been focused on fairly liquid assets, but recently a greater attempt to price such an important component of asset pricing factors in markets with high illiquidity (especially in real estate) has also started to take place.The present paper brings these recent studies together, and estimates the liquidity premium of an illiquid asset (real estate) looking at three main aspects – time on market, liquidation bias and market liquidity – and using three main empirical models and several liquidity measures suggested in the literature. We find strong evidence of a high premium of around 3.0-3.5% that varies across sectors and periods. This estimation is robust to different measures of liquidity and model specifications.
Keywords:	Asset Pricing; Liquidity; Real Estate; Risk Premium
JEL:	R3
Date:	2018–01–01
URL:	http://d.repec.org/n?u=RePEc:arz:wpaper:eres2018_215&r=mst

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