Information feedback in temporal networks as a predictor of market crashes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11640%2F18%3A00494081" target="_blank" >RIV/00216208:11640/18:00494081 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1155/2018/2834680" target="_blank" >http://dx.doi.org/10.1155/2018/2834680</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2018/2834680" target="_blank" >10.1155/2018/2834680</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Information feedback in temporal networks as a predictor of market crashes

Popis výsledku v původním jazyce

In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.

Název v anglickém jazyce

Information feedback in temporal networks as a predictor of market crashes

Popis výsledku anglicky

In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Complexity

ISSN

1076-2787

e-ISSN

—

Svazek periodika

2018

Číslo periodika v rámci svazku

2018

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

000446019700001

EID výsledku v databázi Scopus

2-s2.0-85056463278