유가 충격과 금융시장 간의 상관성 연구 : 한국과 미국 시장 비교 분석

다국어 초록 (Multilingual Abstract)

This dissertation examines how oil price shocks are transmitted to financial markets across structurally heterogeneous economies, motivated by the divergent reactions of Korea and the United States to the 2020 pandemic-induced oil price collapse and the 2022 Russia-Ukraine war-driven surge. Two analytically distinct channels are decomposed into—oil price level shocks and oil price volatility shocks （OVX）—and their independent effects on equity, foreign exchange, and volatility markets are investigated using daily data from January 2010 to December 2024 （T=3,534 observations）.
The analysis applies a five-stage hybrid econometric framework: （i） pretesting with ADF/KPSS unit root tests and Granger causality for exogeneity validation; （ii） symmetric VARX modeling with dynamic multiplier analysis, wherein all exogenous variables are pre-lagged by one period （t−1 only rule） to mitigate simultaneity bias; （iii） asymmetric VARX with Wald tests decomposing OVX into positive and negative components following Hamilton's （1983, 2003） asymmetry insight; （iv） dual DCC-GARCH design contrasting Raw DCC and Residual DCC to separate oil-mediated and idiosyncratic co-movement channels, with statistical significance assessed via Moving Block Bootstrap （B=1,000 replications）; and （v） Bai-Perron structural break tests endogenously identifying two break points （July 2012, March 2020） that partition the sample into three regimes: Pre-Shale, Shale Boom, and Pandemic/War.
Four principal findings emerge. First, level and volatility shocks operate as independent and opposing transmission channels: a one-standard-deviation Brent shock raises S＆P500 by +19.63bp and KOSPI by +3.97bp, whereas an OVX shock lowers S＆P500 by −28.21bp and KOSPI by −7.77bp. Conflating these two channels, as in much of the prior literature, produces offsetting effects that obscure true transmission dynamics. Second, financial markets exhibit pronounced downside asymmetry: OVX increase responses are 3.56 times larger than decreases in Korea and 2.21 times in the United States （p〈0.01）, suggesting that loss aversion operates as an emergent property of aggregate market behavior. Third, a novel correlation dualization pattern is documented: level-dimension synchronization （Brent-Stock） weakens across regimes （0.530 to 0.199）, while volatility-dimension co-movement （OVX-VIX/VKOSPI） intensifies during crisis regimes. Fourth, the S-R-E framework reveals structural heterogeneity: the United States exhibits 4.9 times greater sensitivity and 5.8 times stronger resilience （i.e., faster mean reversion） while Korea faces compounded exposure through simultaneous equity and currency transmission owing to near-complete crude oil import dependence.
This study contributes along four dimensions: formalizing dual-channel transmission theory that separates real-economy information from uncertainty signals; proposing a replicable five-stage hybrid methodology generalizable to other macro-financial transmission studies; documenting downside asymmetry of volatility shock responses and correlation dualization as new stylized facts; and extending the S-R-E framework from household-level vulnerability analysis to cross-country macro-financial comparison. Policy implications are differentiated across central banks, regulatory authorities, asset managers, and corporate treasurers—with the Bank of Korea notably requiring a triple defense line of foreign reserves, currency swap arrangements, and composite-trigger intervention protocols, given Korea's dual-channel vulnerability that simultaneously propagates OVX shocks through both equity and currency markets.

목차 (Table of Contents)

• 제1장 서론 1
• 제1절 연구 배경 1
• 제2절 연구 범위와 연구 주제 4
• 제3절 논문의 구성 11
• 제2장 이론적 배경 및 선행연구 13
• 제1절 유가 충격의 이론적 기초 13
• 제2절 유가와 주식시장 19
• 제3절 유가와 환율시장 25
• 제4절 유가와 변동성 지수 31
• 제5절 동적 상관관계 분석(DCC-GARCH) 모형 36
• 제6절 행동재무학적 관점 38
• 제7절 지정학적 리스크와 에너지 안보 40
• 제8절 금융시장 취약성의 S-R-E 프레임워크 41
• 제3장 연구 가설 및 연구 방법 45
• 제1절 연구 가설 45
• 제2절 데이터 및 변수 설정 47
• 제3절 분석 프레임워크 52
• 제4절 유가 변수의 외생성 검증 56
• 제5절 구조적 변화 검정(Bai-Perron Test) 56
• 제6절 VARX 모형 57
• 제7절 동적 상관관계 분석 (DCC-GARCH) 모형 63
• 제4장 실증분석 69
• 제1절 자료 및 기초 통계 분석 69
• 제2절 데이터 적합성 85
• 제3절 구조적 변화 검정 및 기간 구분 표본 87
• 제4절 VARX 모형 분석 91
• 제5절 동적 상관관계 분석(DCC-GARCH) 114
• 제6절 부분 기간별 분석 123
• 제7절 특정 원유 공급 충격 효과 비교 131
• 제8절 한국과 미국 종합적 결과 비교 133
• 제5장 결론 142
• 제1절 실증분석 결과 요약 142
• 제2절 학술적 시사점 144
• 제3절 실무적 시사점 146
• 참고 문헌 167
• ABSTRACT 180