Dynamic margin setting with EWMA volatilities

The Perceptron with Dynamic Margin

The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum ...

Implementation of Dynamic Semiparametric Factor Model for Implied Volatilities

Dynamic Semiparametric Factor Model (DSFM) is a convenient tool for analysis of implied volatility surfice (IVS). It offers dimension reduction of the IVS and can be therefore applied in hedging, prediction or risk mangement. However the estimation of the DSFM parameters is a complex procedure since it requires huge number of observation. Therefore the efficient implementation is a key issue fo...

Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails∗

We propose a new model for dynamic volatilities and correlations of skewed and heavytailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the skewed and fat-tailed shape of the distribution directly affects the dynamic behavior of the time-varyin...

Localizing Volatilities *

We propose two main applications of Gyöngy (1986)’s construction of inhomogeneous Markovian stochastic differential equations that mimick the one-dimensional marginals of continuous Itô processes. Firstly, we prove Dupire (1994) and Derman and Kani (1994)’s result. We then present Bessel-based stochastic volatility models in which this relation is used to compute analytical formulas for the loc...

Direction Setting the Margin between IPStream and ATM interconnection Prices