Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral- t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative...

In this paper, the Partial Distribution (PD) and multivariate Partial Distribution (MPD) are presented in their concepts, properties and applications, and PD is compared with the lognormal and the levy distribution. Though the levy distribution is better to describe the exchange returns in...

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

The complexity of the world oil market has increased dramatically in recent years and new approaches are needed to understand, model, and forecast oil prices today. Many models have been explored and most of the papers and modeling projects referenced in this paper identify their own...

In this paper there's a simulation study on evolutionary finance models from Evstigneev/Hens/Schenk-Hoppe and applies the scenario generation algorithm from Hoyland/Kaut/Wallace with moment matching. Further it shows with different dividend models which investment strategies perform best under...

This study proposes a novel nonparametric estimation approach to solving asset-pricing models. Our method is robust to misspecification errors and it inherits a closed-form solution that facilitates ease of implementation. By transforming the Euler equation, our estimate is fully identified, and...

Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applications in one of the core functions of finance, the investment process. This includes return forecasting, risk modelling and portfolio...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a sharp...

We present a simple rational approximation to the solution of the rough Heston Riccati equation valid in a region of its domain relevant to option valuation. Pricing under rough Heston using this approximation is both fast and very accurate