A particle- lter based estimation method is developed for the stochastic volatility model with/without jumps and applied on the S&P 500 index value and the VIX term structure jointly. The model encompasses all mean-reverting stochastic volatility option pricing models with a constant elasticity...

This paper provides a new bridge sampler that can efficiently generate sample paths, subject to some endpoint condition, for non-Gaussian dynamic models. This bridge sampler uses a companion pseudo-Gaussian bridge as the proposal and sequentially re-simulates sample paths via a sequence of...

A data-cloning SMC² method is proposed as a general purpose optimization routine for estimating latent variable models by maximum likelihood. The latent variables are first marginalized out by SMC at any fixed parameter value, and the model parameters are then estimated by density tempered SMC....

Corporate credit default swap (CDS) premium is the market price of credit risk posed by a corporate obligor. Although corporate CDS are commonly used for risk benchmarking in accounting and credit risk management, liquid CDS are limited to less than 500 corporate names globally. CDS users must...

Corporate credit default swap (CDS) premium is the market price of credit risk posed by a corporate obligor. Although corporate CDS are commonly used for risk benchmarking in accounting and credit risk management, liquid CDS are limited to less than 500 corporate names globally. CDS users must...

Dynamicmacroeconomicmodels shouldby designbe amenabletomacro scenario analyses under some stated policy objective or presumed stress environment, for example, anticipating how an economy would look like with an inflation target or under severe economic downturn. However, such analyses are...

A data-cloning SMC² maximum likelihood estimation algorithm is proposed as a general-purpose optimization routine for models with latent variables. Our algorithm first marginalizes out latent variables by applying one layer of SMC at a fixed parameter value, and then estimates the model...

We estimate term structures of default probabilities for private firms using data consisting of 1,759 default events from 29,894 firms between 1999 and 2014. Each firm's default likelihood is characterized by a forward intensity model employing macro risk factors and firm-specific attributes. As...

Selecting a subset from many potential explanatory variables in linear regressions has long been the subject of research interest, and the matter is made more important in the era of big data when many more variables become available/accessible. Of late, the l_1-norm penalty based techniques...

A high-quality and granular probability of default (PD) model is on many practical dimensions far superior to any categorical credit rating system. Business adoption of a PD model, however, needs to factor in the long-established business/regulatory conventions built around letter-based credit...