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Simulation-Based Valuation of Project Finance - Does Model Complexity Really Matter?

Weber, Florian - 2011

. Third, the choice of the volatility forecasting mode affects the simulation results significantly. Fourth, we find a … significant impact of correlation versus no correlation but do not find a strong impact of sophisticated correlation forecasting …

Persistent link: https://www.econbiz.de/10013133631

Simulation-based valuation of project finance : does model complexity really matter?

Weber, Florian; Schmid, Thomas; Pietz, Matthäus; … - 2010

forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model … varied. We find that the applied volatility forecasting models have a strong influence on the expected net present value … distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and …

Persistent link: https://www.econbiz.de/10008659217

An alternative Bayesian approach to structural breaks in time series models

Hauwe, Sjoerd van den; Paap, Richard; Dijk, Dick van - 2011

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

Persistent link: https://www.econbiz.de/10011383033

A Multi-Level Dimension Reduction Monte-Carlo Method for Jump-Diffusion Models

Dang, Duy-Minh - 2017

This paper develops and analyses convergence properties of a novel multi-level Monte-Carlo (mlMC) method for computing prices and hedging parameters of plain-vanilla European options under a very general $b$-dimensional jump-diffusion model, where $b$ is arbitrary. The model includes stochastic...

Persistent link: https://www.econbiz.de/10012972095

Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems

Malhotra, Yogesh - 2015

Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

Persistent link: https://www.econbiz.de/10013029835

Multilevel Dimension Reduction Monte-Carlo Simulation for High-Dimensional Stochastic Models in Finance

Dang, Duy-Minh - 2015

One-way coupling often occurs in multi-dimensional stochastic models in finance. In this paper, we develop a highly efficient Monte Carlo (MC) method for pricing European options under a N-dimensional one-way coupled model, where N is arbitrary. The method is based on a combination of (i) the...

Persistent link: https://www.econbiz.de/10013029894

American Monte Carlo by Stochastic Separation of the Expected Value

Hammarlid, Ola - 2021

American Monte Carlo is a solution to the puzzle of calculating the value of derivatives with the right to an early exercise, when using Monte Carlo simulation. One of the technique uses regression of some suitable basis functions, which is a bit arbitrary, and could if made wrong render in...

Persistent link: https://www.econbiz.de/10013228567

Options on Realized Variance by Transform Methods : A Non-Affine Stochastic Volatility Model

Drimus, Gabriel G. - 2012

In this paper we study the pricing and hedging of options on realized variance in the 3/2 non-affine stochastic volatility model, by developing efficient transform based pricing methods. This non-affine model gives prices of options on realized variance which allow upward sloping implied...

Persistent link: https://www.econbiz.de/10013116726

Option Portfolio Value at Risk Using Monte Carlo Simulation Under a Risk Neutral Stochastic Implied Volatility Model

He, Peng - 2013

This paper calculates option portfolio Value at Risk (VaR) using Monte Carlo simulation under a risk neutral stochastic implied volatility model. Compared to benchmark delta-normal method, the model produces more accurate results by taking into account nonlinearity, passage of time,...

Persistent link: https://www.econbiz.de/10013090202

Markov-Chain Approximation and Estimation of Nonlinear, Non-Gaussian State Space Models

Farmer, Leland - 2014

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Persistent link: https://www.econbiz.de/10013048908