Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information

Fleming, Michael J.; Remolona, Eli M.

doi:10.1111/0022-1082.00172

Cited by 607 publications

(112 citation statements)

References 29 publications

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“…The relationship between liquidity and volatility has been widely analyzed by several authors. Fleming and Remolona (1999) have investigated the relationship among liquidity, volatility and public information in the US Treasury market and have shown that that volatility and liquidity respond simultaneously to the release of new information. More recently, Collin-Dufresne and Fos (2016) have explored the relationship between liquidity and noise trading volatility and found that liquidity is an important driver of trading volatility.…”

Section: Financial Hedging and Business Performance: A Reviewmentioning

confidence: 99%

Modeling Price Volatility Based on a Genetic Programming Approach

Ding

Zhang

Duygun

2019

British J of Management

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Business profitability is highly dependent on risk management strategies to hedge future cash flow uncertainty. Commodity price shocks and fluctuations are key risks for companies with global supply chains. The purpose of this paper is to show how artificial intelligence (AI) techniques can be used to model the volatility of commodity prices. More specifically, the authors introduce a new model – LIQ‐GARCH – that uses genetic programming to forecast volatility. The newly generated model is then used to forecast the volatility of the following three indexes: the Commodity Research Bureau (CRB) index, the West Texas Intermediate (WTI) oil futures prices and the Baltic Dry Index (BDI). The empirical model performance tests show that the newly generated model in this paper is considerably more accurate than the traditional GARCH model. As a result, this model can help businesses to design optimal risk management strategies and to hedge themselves against price uncertainty.

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Section: Financial Hedging and Business Performance: A Reviewmentioning

confidence: 99%

Modeling Price Volatility Based on a Genetic Programming Approach

Ding

Zhang

Duygun

2019

British J of Management

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“…The L shaped pattern was mainly supported by the studies of Fleming and Remolona (1999) for the USA market, Gary Tian andMingyuan Guo (2007) andFei Ren et al (2008) for the Chinese market, Jeffrey Williams and James Eaves (2007) for the Japanese market and Roberto Pascual and David Veredas (2009) for Spanish market. Similar findings were provided by Harju and Hussain (2006) who asserted that the European Indices FTSE 100, XDAX30, SMI and CAC40 followed a reverse J pattern which differed slightly to an L pattern.…”

Section: Past Literaturementioning

confidence: 87%

Analysis of Intra-Day Volatility under Economic Crisis Conditions

Glezakos¹,

Vafiadis²,

Mylonakis³

2011

IJEF

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The purpose of this paper is to examine intra-day volatility of the Athens (GI), Frankfurt (DAX) and New York (DJ) Stock Markets under conditions of economic crisis. After utilizing 5 minutes intervals of the periods SeptemberDecember of 2008 and 2009, a U-shaped intra-day volatility pattern was observed for DJ and an L-shaped one for DAX and GI. The results indicate a sharp spike in the first 30 minutes and some weaker spikes for the rest of the trading. Moreover, the influence of the New York Stock market to the European markets was dominant. At the same time, GI and DAX exhibited a significantly positive correlation, particularly in last quarter of 2008. Finally, volatility of returns was unusually high in 2008, obviously due to the prevailing global financial crisis.

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“…For example, in order to bracket the FOMC release time at 2:15 p.m., the 15/45-minute interval is constructed 5. GovPX data are used extensively in the term structure literature (e.g., Fleming and Remolona 1999;Balduzzi, Elton, and Green 2001;Gürkaynak, Sack, and Swanson 2005;Fleming and Piazzesi 2005). The market coverage by GovPX has declined over the years especially for longmaturity bonds.…”

Section: Methodsmentioning

confidence: 99%

The Asymmetric Impact of “Informative” and “Uninformative” Federal Open Market Committee Statements on Asset Prices

Farka

2010

Contemporary Economic Policy

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This paper examines the effect of the information content of Federal Open Market Committee (FOMC) statements on the level and volatility of Treasuries and stock returns. Statements are separated into "informative" and "uninformative" groups with informative statements delivering important information which was not previously anticipated by the consensus. We find evidence that the information content of FOMC statements tends to generate an asymmetric response, with informative statements having a larger effect than uninformative statements. Asymmetries tend to be more pronounced for the conditional mean than conditional volatility. The behavior of volatility is tent-shaped, spiking during policy announcements and declining before and after the release. We also find that the evolution of the language of the FOMC statements does matter to market participants and that joint releases of target rate changes and informative statements have a distinctly more pronounced impact than other types of announcements. (JEL E52, E58, E65, G12, G14)

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Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information

Cited by 607 publications

References 29 publications

Modeling Price Volatility Based on a Genetic Programming Approach

Modeling Price Volatility Based on a Genetic Programming Approach

Analysis of Intra-Day Volatility under Economic Crisis Conditions

The Asymmetric Impact of “Informative” and “Uninformative” Federal Open Market Committee Statements on Asset Prices

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