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Financial markets have extremely complex behavior that cannot be fully modeled using classical approaches. In particular, numerous empirical studies show that market volatility exhibits some form of long-range dependence and has time-varying Hoelder regularity with prominent periods of "roughness" (i.e., of Hoelder order ? 0.1). These two properties are far beyond the capabilities of classical Brownian diffusions and it is challenging to reproduce them simultaneously in one model. In the existing literature, the phenomenons of long-range dependence and roughness mentioned above are often addressed by using fractional Brownian motion. However, in this case, these two features turn out to be mutually exclusive and cannot be grasped simultaneously. Furthermore, existing stochastic models based on fractional Brownian motion pose additional challenges of the technical kind: they tend to produce prices with moment explosions (and hence are not applicable to pricing some widespread derivatives); they may have volatilities that hit zero (or even become negative) which results in problems with transitioning between physical and pricing measures; they often lack efficient numerical algorithms for derivative pricing, hedging, etc. In this book, we introduce a novel class of stochastic processes driven by general Hoelder noises that allows for a very broad flexibility in the noises (to account for both roughness and long-range dependence simultaneously) and grasps the unconventional behavior of market volatility. We also present a variety of associated numerical methods and propose practically feasible algorithms for various applications, such as pricing of derivatives (including options with discontinuous payoffs) and quadratic hedging.