The EWMA Heston model
提出了EWMA Heston模型，一种马尔可夫随机波动率模型，能捕捉波动率动态的多种经验特征，且比基于分数过程的粗糙波动率模型更易于模拟。基于S&P500数据验证了该模型与市场数据的一致性，可作为现有随机波动率模型的替代方案。
This paper introduces the exponentially weighted moving average (EWMA) Heston model, a Markovian stochastic volatility model able to capture a wide range of empirical features related to volatility dynamics while being more tractable for simulations than rough volatility models based on fractional processes. After presenting the model and its principal characteristics, our analysis focuses on the use of its associated Euler-discretization scheme as a time-series generator for Monte-Carlo simulations. Using this discretization scheme, and on the basis of S&P500 empirical time series, we show that the EWMA Heston model is overall consistent with market data, making it a credible alternative to other existing stochastic volatility models.