## A. Papapantoleon

38 records found

1

## Authored

## Stability of backward stochastic differential equations

### The general Lipschitz case

In this paper, we obtain stability results for backward stochastic differential equations with jumps (BSDEs) in a very general framework. More specifically, we consider a convergent sequence of standard data, each associated to their own filtration, and we prove that the assoc ...

## Marginal and Dependence Uncertainty

### Bounds, Optimal Transport, And Sharpness

Motivated by applications in model-free finance and quantitative risk management, we consider Frechet classes of multivariate distribution functions where additional information on the joint distribution is assumed, while uncertainty in the marginals is also possible. We deriv ...

## Contributed

## A Novel Approach to FX Swap Portfolio Management

### With an Application in Portfolio Optimization

## Market impact modeling and optimal execution strategies for equity trading

### An intraday and multiday study

## Electrical energy storage scheduling

### Short-term scheduling for the intraday market using stochastic programming

## (Dynamic) hedging of a mortgage portfolio

### Investigating margin and value stability

## Multi Target XGBoost Cash Flow Prediction

### An Efficient Machine Learning Algorithm For Future Liability Projections

## Valuing Cross-Border Capacity as a Real Option

### An IOHMM Approach

## Spectral Calibration of Time-inhomogeneous Exponential Lévy Models

### With Asymptotic Normality, Confidence Intervals, Simulations, and Empirical Results

## Efficient Estimation of the Expected Shortfall

### In a Nested Simulation Framework

## Option Pricing Techniques

### Using Neural Networks

## Energy Study of Drying

### Using Machine Learning to Predict the Energy Consumption of an Industrial Powder Drying Process

## Restoration of Missing Data using a Human Adaptive Framework

### The Cleansing Algorithm

Improving data quality is of the utmost importance for any data-driven company, as data quality is unmistakably tied to business analytics and processes. One method to improve upon data quality is to restore missing and wrong data entries.

Improving data quality is of the utmost importance for any data-driven company, as data quality is unmistakably tied to business analytics and processes. One method to improve upon data quality is to restore missing and wrong data entries.

The goal of this research is construct an algorithm such that it is possible to restore missing and wrong data entries, while making use of a human adaptive framework. This algorithm has been constructed in a modular fashion and consists of three main modules: Data Transformation, Data Structure Analysis and Model Selection. Data Transformation has concerned itself with conversion of raw data to data types and forms the other modules can use.

Data Structure Analysis has been designed to deal with correctly missing data and dichotomy in the target feature by making use of three clustering algorithms: DBSCAN, K-Means and Diffusion Maps. DBSCAN is used to determine the necessity of clustering as well as the initialisation of the K-Means algorithm. K-Means and Diffusion Maps have been used as clustering methods in the one-dimensional target feature and the two-dimensional input-target feature pairs, respectively. Data Structure Analysis has further been designed to perform feature selection through three filter methods: CorrCoef, FCBF and Treelet.

Model Selection has proposed a novel approach to selection of the best model of a candidate set through the optimisation of a conditional model ranking strategy based on the prior construction of theoretical testing. Our candidate set consisted of Expectation Maximisation, K-Means, Multi-Layer Perceptron, Nearest Neighbor, Random Forest, Linear Regression, Polynomial Regression, ElasticNet Regression.

In terms of restorability, it was shown that the optimal configuration of the Cleansing Algorithm for the restoration of missing data, was provided by opting not to use clustering, using a custom alteration to the Treelet algorithm for feature selection and making use of the model selection strategy. This not only lead to the greatest restorability of 56.90% on Aegon data sets, which was an improvement of 44.83% when compared to not using the Cleansing Algorithm, but also to the reduction of computation time by over 400%. A more realistic restorability due to the presence of correctly missing data, was given by the same configuration making use of one-dimensional output clustering. This resulted in a restorability on Aegon data sets of 43.10%. As such it was deemed possible to restore missing data on Aegon data sets.

With respect to the human adaptive framework, it was determined that the construction of the algorithm be modular in the sense that any alternate feature selection or clustering approach can be implemented with ease. Furthermore, the model selection module allows us to customize the theoretical testing and choice of regression or classification models for the restoration of missing data. In doing so, the algorithm has laid the foundations for human adaptivity of the Cleansing Algorithm.