Dynamic interactions between sectoral tail risk and market integration: Evidence from Borsa Istanbul using PELVE and DCC-GARCH
ÇİĞDEM KURT CİHANGİR & Burak Alparslan Eroğlu
Abstract
This study investigates the dynamics of systemic risk transmission and market integration across sectoral equity indices in Türkiye. A novel aspect of our approach is the joint use of the probability equivalent level of Value-at-Risk and Expected Shortfall (PELVE) to quantify forward-looking, distribution-sensitive tail risk, and the Dynamic Conditional Correlation (DCC-GARCH) model to capture time-varying sectoral integration. By integrating these two perspectives, we offer a comprehensive framework to track how tail risk and inter-sector connectivity co-evolve over time. Our results reveal that sectoral tail risks are unevenly distributed and exhibit strong intra-sectoral spillover effects. Sectors such as chemical, petrol and plastic (XKMYA), basic metal (XMANA), and textile and leather (XTEKS) emerge as key transmitters and recipients of risk shocks. Meanwhile, DCC dynamics highlights periods of heightened and asymmetric co-movement, including temporary decoupling from the benchmark index (XU100). Generalized impulse response functions (VAR-GIRF) indicate that market integration shocks significantly influence tail risk, while reverse effects remain limited. Notably, financial sectors, particularly banks (XBANK) and diversified financials (XUMAL), demonstrate "risk-absorbing" behaviors under specific conditions, reducing systemic vulnerability. XKMYA consistently appears as the most interconnected sector in both risk and integration domains, underscoring its critical systemic role. The findings hold across daily, weekly, and monthly data frequencies, offering timely insights for risk monitoring and macroprudential regulation in emerging markets.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.