We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non-Gaussian asset models built using Lévy processes. The proposed framework allows for dependence between assets and different tail-behaviors and jump structures for each asset. Our...

This paper studies volatility spillovers in credit default swaps (CDS) between the corporate sectors and Latin American countries. Daily data from October 14, 2006, to August 23, 2021, are employed. Spillovers are computed both for the raw data and for filtered series which factor out the effect...

When alternatives are compared using an estimated criterion function, this may introduce a discrepancy between the true and the estimated criterion. In this paper, we consider a situation where a preordering (ranking) of stochastic sequences is defined from expected loss/gain, using a parametric...

A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...

In this paper, a mathematical model for American call option pricing incorporating the seasonal effect inspite of leverage effect on volatility is developed. The effect of strike price, interest rate, dividends and maturities on option pricing and portfolio dynamics is discussed by solving the...

Motivated by recent innovations in volatility forecasting, the presented study analyses the explanatory power of HAR framework in the context of MICEX index seeking to expand the empirical evidence on the subject of the optimal forecasting methodology. The rest of the paper is organized as...

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic...

Here, we introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability...

Forecasts of stock market volatility is an important input for market participants in measuring and managing investment risks. Thus, understanding the most appropriate methods to generate accurate is key. This paper examines the ability of Machine Learning methods, and specifically Artificial...