Flight-to-Liquidity Flows in the Euro Area Sovereign Debt Crisis

García, Juan Ángel; Gimeno, Ricardo

doi:10.2139/ssrn.2538371

Cited by 15 publications

(11 citation statements)

References 45 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning approaches in the existing literature for controlling financial indexes measuring credit risk, liquidity risk and risk aversion include the works in [2,4,8,9,18], among others. Efforts to make machine learning models accepted within the economic modeling space have increased exponentially in recent years [19,24].…”

Section: Related Workmentioning

confidence: 99%

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Consoli

Pezzoli

Tosetti

2020

Machine Learning, Optimization, and Data Science

View full text Add to dashboard Cite

The Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.

show abstract

Section: Related Workmentioning

confidence: 99%

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Consoli

Pezzoli

Tosetti

2020

Machine Learning, Optimization, and Data Science

View full text Add to dashboard Cite

show abstract

“…These findings suggest that breaking down credit and liquidity risk premia is important in modeling the yield spreads, but an identification strategy is necessary. Longstaff (2004), De Santis (2014), Garcia and Gimeno (2014), and Schwarz (2018) find significant evidence of German Bunds benefiting from flight to liquidity. They use yield spreads between sovereign bonds and equivalent bonds issued by government sponsored agencies as a proxy of liquidity risk factor.…”

Section: Factor Structure and Dynamicsmentioning

confidence: 99%

An Arbitrage-Free Yield Net Model with Application to the Euro Debt Crisis

2019

View full text Add to dashboard Cite

We develop a parsimonious arbitrage-free yield net model for consistent bond pricing across maturities and issuers. Containing a core curve and multiple periphery curves, the yield net is spanned by three layers of factors: base factors spanning all curves, common spread factors spanning all periphery yield spreads, and speci…c factors each spanning yield spreads of a periphery issuer. Under the arbitrage-free assumption, we prove a parsimonious solution to the risk-neutral process that guarantees strong identi…cation on the latent risk factors and parameters. We apply the model to Treasury yields of Germany and GIIPS countries from 2009 to 2016. The model …ts data remarkably well and disentangles the common credit risk, market liquidity risk, and country-speci…c risks. The results demonstrate that relative risk pricing determines signs and magnitudes of the " ‡ight to liquidity" e¤ect and spillover e¤ects among bonds of di¤erent issuers.

show abstract

“…Specifically, bond premia in core bond markets remained relatively compressed and continued to exhibit a strong comovement among them, partly as a result of further flight-to-safety and flight-to-liquidity flows from SMP bond markets (see Garcia and Gimeno, 2014). In contrast, in SMP bond markets premia developments started to display wider divergences both within the SMP and with respect to the core group.…”

Section: Risk Premia In Euro Area Bond Marketsmentioning

confidence: 99%

Bond Risk Premia, Macroeconomic Factors and Financial Crisis in the Euro Area

García

Werner

2015

SSRN Journal

Self Cite

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThis paper investigates the power of macroeconomic factors to explain euro area bond risk premia using (i) a large dataset that captures the nowadays data-rich environment (ii) the Elastic Net variable selection. We find that macroeconomic factors, in particular economic activity and sentiment indicators, explain 40% of the variability of risk premia before the crisis, and up to 55% during the financial crisis, and both for core countries (from 40% to 60%) and periphery countries (from 35% to 44%). Moreover, macroeconomic factor models clearly outperform financial indicators like the CP-factor and credit default swap (CDS) premia, even in periods of significant market turbulence.JEL codes: E43, E44, G01, G12, C52, C55 We search for quantitative evidence on the extent to which macroeconomic factors are priced in in bond premia. To determine whether, when and by how much bond premia is related to price, economic activity, business sentiment or financial factors, or a combination of those, we employ the Elastic Net estimator (EN henceforth, Zou and Hastie, 2005), 1 a variable selection approach that helps overcome some specific challenges of euro area bond market data. First, we can evaluate a large number of potential determinants: 132 monthly macroeconomic indicators from nine macroeconomic groups, and, whenever possible, we also consider country-specific in addition to EA wide data (see the online appendix for details of our data and some additional results). Second, we can select observable factors based on their explanatory power for bond premia, which provides higher transparency and interpretability than principal components or other statistical techniques that instead summarise the information content of the explanatory variables (e.g. Stock and Watson 2002). Finally, the EN is particularly suitable for small sample analysis, which fits well with the short history of the EA and our goal of investigating the financial crisis impact.We first document the strong impact of the financial crisis on bond risk premia across 11 EA markets. We show that the fairly strong commonality in bond risk premia dynamics across euro area 1 The EN estimator belongs to a broad class of Least Angle Regression estimators (LARS) that are designed to rank the importance of every explanatory variable using a response vector (in our case excess returns) and help select a parsimonious model using a regularization parameter.ECB Wo...

show abstract

Flight-to-Liquidity Flows in the Euro Area Sovereign Debt Crisis

Cited by 15 publications

References 45 publications

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

An Arbitrage-Free Yield Net Model with Application to the Euro Debt Crisis

Bond Risk Premia, Macroeconomic Factors and Financial Crisis in the Euro Area

Contact Info

Product

Resources

About