Abstract

1. LITERATURE


Transmission channels between oil price shock and financial indicators have been analyzed by scholars with application of various empirical models such as Granger causality (Jones and Kaul, 1996; Arouri and Nguyen, 2010), OLS (Chen et al., 1986; Jones and Kaul, 1996; Faff and Brailsford, 1999; Basher and Sadorsky, 2006; Aloui et al., 2012), GARCH (Filis et al., 2011; Jammazi, 2012), VAR (Huang et al., 1996; Sadorsky, 1999; Ciner, 2001; Hammoudeh and Aleisa, 2005; Park and Ratti, 2008; Apergis and Miller, 2009; Cunado and de Gracia, 2014; Nazlioglu et al., 2015), SVAR (Wang et al., 2013; Chen et al., 2014; Kang et al., 2015a).
Some recent studies directly focus on the spillover effects of oil price shocks on financial stress indexes (Chen et al., 2014; Nazlioglu et al., 2015), yet their number is scant. This paper aims to fill this gap by investigating the time-varying effects of oil price shocks on financial stress by employing the TVP-VAR model.
1.1. RESEARCH SUBJECT
In this study, we analyze time-varying changes of the structural shocks in the global oil market and their effects on the systemic risk stemmed from the US financial system.
1.2. RESEARCH PURPOSE AND IMPORTANCE
The TVP-VAR model consistently and robustly captures the time-varying nature of the structural oil market shocks to the financial activity of the US.


1.3. CONTRIBUTION of the ARTICLE to the LITERATURE


We contribute to the related literature by evaluating time-varying propagations between oil specific shocks and financial activity of the US by employing a seminal approach.
2. DESIGN AND METHOD


2.1. RESEARCH TYPE
This is an empirical research study.


2.2. RESEARCH PROBLEMS
To detect time-varying effects of structural shocks transmitted by the global oil market on the financial stress of the US.
2.3. DATA COLLECTION METHOD
Our data set consists of monthly West Texas Intermediate (WTI) spot crude oil prices, world crude oil production in millions of barrels per day averaged monthly and Kansas City Financial Stress Index (KCFSI) covering the 1990 February – 2018 September period. The oil price and KCFSI data have been collected from the FRED database of St. Louis Federal Reserve and the oil production data have been obtained from the U.S. Energy Information Administration (EIA) database.


2.4. QUANTITATIVE / QUALITATIVE ANALYSIS
In this study, we implement the Bayesian estimation of time-varying parameter VAR (TVP-VAR) model of Del Negro and Primiceri (2015) in which the coefficients and variance-covariance matrix of the innovations can change over time.
2.5. RESEARCH MODEL
In this study, we employ the TVP-VAR model to capture time-varying nature of the oil price shocks.
2.6. RESEARCH HYPOTHESES
Structural oil price shocks can significantly affect the financial activity of the US.
3. FINDINGS AND DISCUSSION


3.1. FINDINGS as a RESULT of ANALYSIS
Time-varying unconditional standard deviation for the financial stress properly captures well-known financial stress incidents over the analyzed period. The index significantly surges in the late-1990s covering the Russian Debt Moratorium and the Long Term Capital Management (LTCM) crisis. The time-varying volatility of the KCFSI peaked during the Global Financial Crisis (GFC) shortly after the Lehman Brother’x collapse. The index dramatically plunges to its average levels during the post-GFC era, yet it notably increases around well-known financial stress events of the European Sovereing Debt Crisis (ESDC).
Posterior coefficient of variables with lag 1 in the TVP-VAR are computed by carrying out the Monte Carlo Markov Chain (MCMC) algorithm for 50.000 times in the structural VAR model. Using the first five years (1990:2-1995:6) data as the training sample, it is detected that the posterior coefficients of the lag of the KCFSI and real oil prices on the financial stress are negative and have a decrasing trend. Likewise, it is found that the posterior coefficients of the lag of the global oil production on the real oil prices are positive and tend to decline.
Impulse-Response Functions (IRFs) of TVP-VAR are estimated to exhibit one percent standard deviation structural oil price shock to financial stress and one percent standard deviation oil supply shock to the oil price. According to the IRFs, financial stress permanently increases in response to one percent positive oil price shock in the first 5 months, and remains unchanged for 20 months. Concurrently, oil prices plummet in response to one percent positive structural oil supply shock in the first 3 months and stabilize thereafter.
3.2. DISCUSSING the FINDINGS with the LITERATURE
Our findings are in line with the related studies in the literature that detected negative effects of positive oil price shocks on the financial stress of the US (Hamilton, 1996; Ferderer, 1997; Brown and Yucel, 1999).


4. CONCLUSION, RECOMMENDATION AND LIMITATIONS


4.1. RESULTS of the ARTICLE
Time-varying unconditional standard deviation for the financial stress properly captures well-known financial stress incidents over the analyzed period. The time-varying volatility of the KCFSI peaked during the Global Financial Crisis (GFC) shortly after the Lehman Brother’s collapse. The index significantly increases around well-known financial stress events of the European Sovereing Debt Crisis (ESDC).
The posterior coefficients of the lag of the KCFSI and real oil prices on the financial stress are negative and have a decrasing trend. Likewise, it is found that the posterior coefficients of the lag of the global oil production on the real oil prices are positive and tend to decline.
Financial stress permanently increases in response to one percent positive oil price shock in the first 5 months, and remains unchanged for 20 months. Concurrently, oil prices plummet in response to one percent positive structural oil supply shock in the first 3 months and stabilize thereafter.

4.2. SUGGESTIONS BASED on RESULTS
In order to prevent the adverse effects of oil price shocks on the financial system, the dependence on non-renewable energy sources should be mitigated. In this context, clean and renewable energy policies should be encouraged by the policymakers. Finally, authorities should monitor oil price developments regularly to avoid negative effects of the oil price shocks on the economy.


4.3. LIMITATIONS of the ARTICLE
In our view, there are no limitations of the study.