What drives U.S. financial sector volatility? A Bayesian model averaging perspective

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F20%3A00116660" target="_blank" >RIV/00216224:14560/20:00116660 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0275531919302697#" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0275531919302697#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ribaf.2019.101095" target="_blank" >10.1016/j.ribaf.2019.101095</a>

Jazyk výsledku

angličtina

Název v původním jazyce

What drives U.S. financial sector volatility? A Bayesian model averaging perspective

Popis výsledku v původním jazyce

We investigate the driving forces behind the quarterly stock price volatility of firms in the U.S. financial sector over the period from 1990 to 2017. The driving forces represent a set of 28 economic indicators that are routinely used to detect financial instability and crises and correspond to the development of the financial, monetary, real, trade and fiscal sector as well as to the development of the bond and equity markets. The dimensionality and model choice uncertainty are addressed using Bayesian model averaging, which led to the identification of only seven variables that tend to systematically drive the stock price volatility of financial firms in the U.S.: housing prices, short-term interest rates, net national savings, default yield spread, and three credit market variables. We also confirm that our results are not an artefact of volatility associated with market downturns (for negative semi-volatility), as the results are similar even when market volatility is associated with market upsurge (positive semi-volatility). Given the identified drivers, our results provide supporting empirical evidence that dampening credit cycles might lead to decreased volatility in the financial sector.

Název v anglickém jazyce

What drives U.S. financial sector volatility? A Bayesian model averaging perspective

Popis výsledku anglicky

We investigate the driving forces behind the quarterly stock price volatility of firms in the U.S. financial sector over the period from 1990 to 2017. The driving forces represent a set of 28 economic indicators that are routinely used to detect financial instability and crises and correspond to the development of the financial, monetary, real, trade and fiscal sector as well as to the development of the bond and equity markets. The dimensionality and model choice uncertainty are addressed using Bayesian model averaging, which led to the identification of only seven variables that tend to systematically drive the stock price volatility of financial firms in the U.S.: housing prices, short-term interest rates, net national savings, default yield spread, and three credit market variables. We also confirm that our results are not an artefact of volatility associated with market downturns (for negative semi-volatility), as the results are similar even when market volatility is associated with market upsurge (positive semi-volatility). Given the identified drivers, our results provide supporting empirical evidence that dampening credit cycles might lead to decreased volatility in the financial sector.

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

2020

Kód důvěrnosti údajů

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

Název periodika

RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE

ISSN

0275-5319

e-ISSN

—

Svazek periodika

51

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

101095

Kód UT WoS článku

000502534700053

EID výsledku v databázi Scopus

2-s2.0-85071867198