Many studies in economics deal with the non-reliability cost to assess insurance fees or investment analyses, but none takes into consideration the mechanical aspect of reliability analysis. Other studies in mechanics give some tools and methods to carry out reliability analyses and fragility study. This study developed a framework where economical and mechanical considerations for infrastructure investment decision-making. The theoretical reasoning is here developed to couple mechanical reliability analyses, which are composed of fragility curves, and economical reliability analyses, which is based on resilience cost functions. This coupling is carried out with some probabilistic considerations, giving the concept of "probable cost of failure". The strength of this framework is that it can be used to analyze all possible critical components in a network with all possible natural hazards or malicious event or other undesired events which it is possible to assess its probability of occurrence. The results of the analysis are indicators of probable cost of failure of an infrastructure, which represents the insurance fee. These indicators can be computed for railway lines, for critical components, for events. This tool enables decision-makers to prioritize safety investments and to guide strategic choices. The next step of this study will be to develop smart data analysis tools, because of this framework needs and produces a lot of data, which must be smartly analyzed and presented.

We propose a new approach using the MJ$_1$ semi-metric, from the more general MJ$_p$ class of semi-metrics \cite{James2019}, to detect similarity and anomalies in collections of cryptocurrencies. Since change points are signals of potential risk, we apply this metric to measure distance between change point sets, with respect to returns and variance. Such change point sets can be identified using algorithms such as the Mann-Whitney test, while the distance matrix is analysed using three approaches to detect similarity and identify clusters of similar cryptocurrencies. This aims to avoid constructing portfolios with highly similar behaviours, reducing total portfolio risk.

The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing optimally in continuous time and finite horizon under exponential and mean-variance utilities. We also extend our model to incorporate constraints on the investor's portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.

We consider a fundamental dynamic allocation problem motivated by the problem of $\textit{securities lending}$ in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to $n$ clients, each of whom has a private demand that is unknown to the lender. The lender would like to maximize the usage of the resource $\mbox{---}$ avoiding allocating more to a client than her true demand $\mbox{---}$ but is constrained to sell the resource at a pre-specified price per unit, and thus cannot use prices to incentivize truthful reporting. We first show that the Bayesian optimal algorithm for the one-shot problem $\mbox{---}$ which maximizes the resource's expected usage according to the posterior expectation of demand, given reports $\mbox{---}$ actually incentivizes truthful reporting as a dominant strategy. Because true demands in the securities lending problem are often sensitive information that the client would like to hide from competitors, we then consider the problem under the additional desideratum of (joint) differential privacy. We give an algorithm, based on simple dynamics for computing market equilibria, that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful. Finally, we leverage this private algorithm to construct an approximately truthful, optimal mechanism for the extensive form multi-round auction where the lender does not have access to the true joint distributions between clients' requests and demands.

We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013).

In quantitative finance, useful features are constructed by human experts. However, this method is of low efficient. Thus, automatic feature construction algorithms have received more and more attention. The SOTA technic in this field is to use reverse polish expression to represent the features, and then use genetic programming to reconstruct it. In this paper, we propose a new method, alpha discovery neural network, which can automatically construct features by using neural network. In this work, we made several contributions. Firstly, we put forward new object function by using empirical knowledge in financial signal processing, and we also fixed its undifferentiated problem. Secondly, we use model stealing technic to learn from other prior knowledge, which can bring enough diversity into our network. Thirdly, we come up with a method to measure the diversity of different financial features. Experiment shows that ADN can produce more diversified and higher informative features than GP. Besides, if we use GP's output to serve as prior knowledge, its final achievements will be significantly improved by using ADN.

In this paper we study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkin-based method, we solve the parabolic partial diferential equation for the Black-Scholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare obtained solutions to existing semi-closed pricing formulas. Special attention is paid to the solution of Heston model at the boundary with vanishing volatility.

Urban waste heat recovery, in which low temperature heat from urban sources is recovered for use in a district heat network, has a great deal of potential in helping to achieve 2050 climate goals. For example, heat from data centres, metro systems, public sector buildings and waste water treatment plants could be used to supply ten percent of Europe's heat demand. Despite this, at present, urban waste heat recovery is not widespread and is an immature technology. To help achieve greater uptake, three policy recommendations are made. First, policy raising awareness of waste heat recovery and creating a legal framework is suggested. Second, it is recommended that pilot projects are promoted to help demonstrate technical and economic feasibility. Finally, a pilot credit facility is proposed aimed at bridging the gap between potential investors and heat recovery projects.