We study two sources of heteroscedasticity in high-frequency financial data and estimate their contribution to overall volatility by means of a Markov switching (MS) structural VAR model. We achieve identification for all coefficients by assuming that the structural errors follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we first detect three regimesof volatility. Then we show that both sources of volatility matter for the transmission of shocks, and that information is channeled to the market mostly through demand shocks. This suggests that, on the EUR/USD market, some liquidity takers (LTs) are better informed than both liquidity providers and those LTs who follow a feedback strategy.

中文翻译：

---

外汇市场的内生波动

我们研究高频金融数据中的两个异方差来源，并通过马尔可夫转换 (MS) 结构 VAR 模型估计它们对整体波动性的贡献。我们通过假设结构误差遵循 GARCH-DCC 过程来实现对所有系数的识别。使用 2016 年 EUR/USD 交易商间市场的交易数据，我们首先检测了三种波动率。然后我们表明波动的两个来源对于冲击的传递都很重要，并且该信息主要通过需求冲击传递到市场。这表明，在欧元/美元市场上，一些流动性接受者 (LT) 比流动性提供者和那些遵循反馈策略的 LT 更了解情况。