Model-Free Implied Volatility under Jump-Diffusion Models

The model-free implied volatility (MFIVol) is intended to measure the variability of underlying asset price on which options are written. Analytically, however, it does not measure exactly the variability under jump diffusion. Our extensive empirical study suggests that the approximation error can be as much as about 3%--5% although most samples over the data period exhibit less than 1% errors. Even with the non-negligible errors, the MFIVol may be still considered a valid volatility measure from the perspective of risk-neutral return density, in the sense that it is bounded by the two variability measures as well as reflecting the shape of the risk-neutral density via its higher central moments

Generalized residual vector quantization for large scale data

Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named \textit{residual vector quantization} (RVQ). Next, we propose \textit{generalized residual vector quantization} (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as the special cases of our proposed framework. We evaluate GRVQ on several large scale benchmark datasets for large scale search, classification and object retrieval. We compared GRVQ with existing methods in detail. Extensive experiments demonstrate our GRVQ framework substantially outperforms existing methods in term of quantization accuracy and computation efficiency.Comment: published on International Conference on Multimedia and Expo 201