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on Market Microstructure |
| By: | Istvan Barra (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam, the Netherlands) |
| Abstract: | We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the data via two different approaches: ordered probit models and discrete distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic patterns. We consider distributions with heavy tails to address occurrences of jumps in tick by tick discrete prices changes. In particular, we introduce a dynamic version of the negative binomial difference model with stochastic volatility. For each model we develop a Markov chain Monte Carlo estimation method that takes advantage of auxiliary mixture representations to facilitate the numerical implementation. This new modeling framework is illustrated by means of tick by tick data for several stocks from the NYSE and for different periods. Different models are compared with each other based on predictive likelihoods. We find evidence in favor of our preferred dynamic negative binomial difference model. |
| Keywords: | Bayesian inference; discrete distributions; high-frequency dynamics; Markov chain Monte Carlo; stochastic volatility |
| JEL: | C22 C58 |
| Date: | 2016–04–22 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20160028&r=mst |
| By: | Benos, Evangelos (Bank of England); Zikes, Filip (Federal Reserve Board) |
| Abstract: | We use proprietary transactional data to examine the determinants of liquidity in the UK government bond (gilt) market, over a rich sample period that covers both the financial crisis of 2008–09 as well as the onset of the subsequent euro-zone sovereign debt crisis. During this period, gilt market liquidity fluctuates significantly with execution costs almost doubling at the peak of the crisis. Consistent with theory, dealer balance sheet constraints and increased funding costs are significant determinants of illiquidity. However, we document that increased funding costs also negatively impact the inter-dealer segment of the market which leads to a further reduction in liquidity. This is consistent with the notion that the inter-dealer segment enables dealers to share risk and manage their inventories. Additionally, gilt market illiquidity is also influenced by instances of reduced competition among dealers. Both of these effects are more pronounced at the peak of the financial crisis and economically significant: a one standard deviation decrease in the fraction of inter-dealer trading leads to an increase in trading costs of about $700K–$1.5 million daily for non-dealers, and a one standard deviation increase in dealer activity concentration leads to an incremental cost of about $270K–$1 million daily. |
| Keywords: | Gilt market; market liquidity; funding liquidity; inter-dealer trading; competition. |
| JEL: | G10 G12 G14 |
| Date: | 2016–05–06 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0600&r=mst |
| By: | Mikkel Bennedsen; Ulrich Hounyo; Asger Lunde; Mikko S. Pakkanen |
| Abstract: | We introduce a bootstrap procedure for high-frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample paths of Brownian semistationary processes, which uses an estimator based on a ratio of realized power variations. Our new resampling method, the local fractional bootstrap, relies on simulating an auxiliary fractional Brownian motion that mimics the fine properties of high frequency differences of the Brownian semistationary process under the null hypothesis. We prove the first order validity of the bootstrap method and in simulations we observe that the bootstrap-based hypothesis test provides considerable finite-sample improvements over an existing test that is based on a central limit theorem. This is important when studying the roughness properties of time series data; we illustrate this by applying the bootstrap method to two empirical data sets: we assess the roughness of a time series of high-frequency asset prices and we test the validity of Kolmogorov's scaling law in atmospheric turbulence data. |
| Date: | 2016–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1605.00868&r=mst |
| By: | Prosper Dovonon; Sílvia Gonçalves; Ulrich Hounyo; Nour Meddahi |
| Keywords: | jumps,bootstrap,block multipower variation, |
| Date: | 2016–05–09 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2016s-24&r=mst |