One of the most basic methods of technical analysis that is used in the practice of investment is the analysis of moving averages, usually calculated for exchange rates in a candlestick representation. The following paper proposes a new, state model, describing the process of trajectory changes...

In recent years, Bitcoin and other cryptocurrencies like Ethereum and Dogecoin have emerged as important asset classes in general, and diversification and hedging instruments in particular. The recent COVID-19 pandemic has provided the chance to examine and assess cryptocurrencies' behavior...

We study the jump behaviour in the sovereign risks of major oil-exporting countries and examine whether it is affected by jumps in the price and volatility of crude oil. Data used are daily from 14 February 2011, to 31 July 2019. We detect the presence of jumps in many oil exporters and find...

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...

In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve...

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models-a simple one and a more complex one-and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for...

Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks. However, many of these approaches do not enforce any no-arbitrage conditions, and the subsequent local volatility surface is never considered. In...

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...