We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) to improve both in-sample analysis and out-of-sample forecast performance traditional conditional heteroskedastic models. In particular, incorporate auxiliary deterministic processes, governed by recurrent neural networks, into variance e.g. GARCH-type flexibly capture dynamics ...