The process of fitting mathematical finance (MF) models for option pricing — known as calibration — is expensive because evaluating the pricing function usually requires Monte-Carlo sampling. Inspired by the success of deep learning for simulation, we present a hypernetwork based approach to...

We present a mixture of normalizing flows (MoNF) approach to European option pricing with guarantees that its estimations are free from static arbitrage. In contrast to many existing methods that meet economic rationality constraints (e.g., non-arbitrage) by introducing auxiliary losses, our...

In this paper the zero vanna implied volatility approximation for the price of freshly minted volatility swaps is generalized to seasoned volatility swaps. We also derive how volatility swaps can be hedged using a strip of vanilla options with Gaussian weights. For the family of stochastic...

The study examines the pricing of credit derivatives using the structural modeling framework. These type of models are known to have problems with accurately valuing derivative securities. To address this challenge, the study proposes incorporating additional sources of risk associated with the...

Using monthly data for Belgium, France, Germany, Italy and Spain for the period 2002-2019, we build a Hierarchical Euro Area Dynamic Nelson-Siegel model that allows for time varying exposures of national factors on the common components, and for stochastic volatility both at the regional and...

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

A recent strand of the literature has proposed stochastic time-varying coefficient models for modelling structural change in the macroeconomy under both exogeneity and endogeneity. Subsequently, a new class of kernel based non-parametric estimators has been introduced for these models. These...

An (s, S) inventory system with service facilities has been investigated in this study. Demands arrive as a Poisson process with parameter λ (0) to an infinite capacity pool at any given time t. When the inventory is greater than the buffer size a customer will transfer to the buffer with rate...

We show how solutions to a large class of partial differential equations with nonlocal Riccati-type nonlinearities can be generated from the corresponding linearized equations, from arbitrary initial data. It is well known that evolutionary matrix Riccati equations can be generated by projecting...