Estimation and pricing under long-memory stochastic volatility

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Long memory and regime switching: A simulation study on the Markov Regime-Switching ARFIMA model (2015, unknown): “For instance, if volatility is pre-estimated, it can be used to enhance the accuracy of dynamic hedging strategies and derivatives pricing models, since long memory of high-frequency asset volatility is a key input in these strategies and models (Chronopoulou and Viens, 2012; Hyung et al., 2008; Ste”
Statistical inference for Vasicek-type model driven by Hermite processes (2017, methodology): “1 Introduction Our aim in this paper is to introduce and analyze a non-Gaussian extension of the fractional model considered in the seminal paper [2] of Comte and Renault (see also Chronopoulou and Viens [3], as well as the motivations and references therein) and used by these authors to model a sit”
Volatility is rough (2014, unknown): “In figure 13, we plot m(2, ) with the parameters H = 0.53, corresponding to the FSV model parameter estimate of Chronopoulou and Viens (2012), and α = 0.5 to ensure some visible decay of the volatility skew.”