This paper presents simple formulae for the local variance gamma model of Carr and Nadtochiy, extended with a piecewise-linear local variance function. The new formulae allow to calibrate the model efficiently to market option quotes. On a small set of quotes, exact calibration is achieved under one millisecond. This effectively results in an arbitrage-free interpolation of class $C^2$. The paper proposes a good regularization when the quotes are noisy. Finally, it puts in evidence an issue of the model at-the-money, which is also present in the related one-step finite difference technique of Andreasen and Huge, and gives two solutions for it.

Probabilistic forecasting of power consumption in a middle-term horizon (months to a year) is a main challenge in the energy sector. It plays a key role in planning future generation plants and transmission grid. We propose a new model that incorporates trend and seasonality features as in traditional time-series analysis and weather conditions as explicative variables in a parsimonious machine learning approach, known as Gaussian Process. Applying to a daily power consumption dataset in North East England provided by one of the largest energy suppliers, we obtain promising results in Out-of-Sample density forecasts up to one year, even using a small dataset, with only a two-year In-Sample data. In order to verify the quality of the achieved power consumption probabilistic forecast we consider measures that are common in the energy sector as pinball loss and Winkler score and backtesting conditional and unconditional tests, standard in the banking sector after the introduction of Basel II Accords.

This paper provides an overview on stablecoins and introduces a novel terminology to help better identify stablecoins with truly disruptive potential. It provides a compact definition for stablecoins, identifying the unique features that make them distinct from previously known payment systems. Furthermore, it surveys the different use cases for stablecoins as well as the underlying economic incentives for creating them. Finally, it outlines critical regulatory considerations that constrain stablecoins and summarizes key factors that are driving their rapid development.

During the COVID-19 epidemic in Japan between March and April 2020, Internet surveys were conducted to construct panel data to investigate changes at the individual level regarding preventive behaviors and mental conditions by surveying the same respondents at different times. Specifically, the difference-in-difference (DID) method was used to explore the impact of the COVID-19 state of emergency declared by the government. Key findings were: (1) the declaration led people to stay home, while also generating anger, fear, and anxiety. (2) The effect of the declaration on the promotion of preventive behaviors was larger than the detrimental effect on mental conditions. (3) Overall, the effect on women was larger than that on men.

We provide a unified treatment of pathwise Large and Moderate deviations principles for a general class of multidimensional stochastic Volterra equations with singular kernels, not necessarily of convolution form. Our methodology is based on the weak convergence approach by Budhijara, Dupuis and Ellis. We show in particular how this framework encompasses most rough volatility models used in mathematical finance and generalises many recent results in the literature.

We present an alternative formula to price European options through cosine series expansions, under models with a known characteristic function such as the Heston stochastic volatility model. It is more robust across strikes and as fast as the original COS method.

This note shows that the cosine expansion based on the Vieta formula is equivalent to a discretization of the Parseval identity. We then evaluate the use of simple direct algorithms to compute the Shannon coefficients for the payoff. Finally, we explore the efficiency of a Filon quadrature instead of the Vieta formula for the coefficients related to the probability density function.

In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators. I approach this by training separate classifiers on the equivalent dataset from a range of countries. Using these classifiers, a three level meta learning algorithm (MLA) is developed. I develop a transform, ASG, to create a country agnostic proxy for the macro-financial indicators. Using these proposed methods, I investigate the degree to which a predictive algorithm for the US 5Y bond price, predominantly using macro-financial indicators, can outperform an identical algorithm which only uses statistics deriving from previous price.

This was an undergraduate project, subsequently the research was not exhaustive.

This paper investigates the hedging performance of pegged foreign exchange market in a regime switching (RS) model introduced in a recent paper by Drapeau, Wang and Wang (2019). We compare two prices, an exact solution and first order approximation and provide the bounds for the error. We provide exact RS delta, approximated RS delta as well as mean variance hedging strategies for this specific model and compare their performance. To improve the efficiency of the pricing and calibration procedure, the Fourier approach of this regime-switching model is developed in our work. It turns out that: 1 -- the calibration of the volatility surface with this regime switching model outperforms on real data the classical SABR model; 2 -- the Fourier approach is significantly faster than the direct approach; 3 -- in terms of hedging, the approximated RS delta hedge is a viable alternative to the exact RS delta hedge while significantly faster.

The reliable estimation of forecast uncertainties is crucial for risk-sensitive optimal decision making. In this paper, we propose implicit generative ensemble post-processing, a novel framework for multivariate probabilistic electricity price forecasting. We use a likelihood-free implicit generative model based on an ensemble of point forecasting models to generate multivariate electricity price scenarios with a coherent dependency structure as a representation of the joint predictive distribution. Our ensemble post-processing method outperforms well-established model combination benchmarks. This is demonstrated on a data set from the German day-ahead market. As our method works on top of an ensemble of domain-specific expert models, it can readily be deployed to other forecasting tasks.

Antifragility was recently defined as a property of complex systems that benefit from disorder. However, its original formal definition is difficult to apply. Our approach has been to define and test a much simpler measure of antifragility for complex systems. In this work we use our antifragility measure to analyze real data from the stock market and cryptocurrency prices. Results vary between different antifragility interpretations and for each system. Our results suggest that the stock market favors robustness rather than antifragility, as in most cases the highest and lowest antifragility values are reached either by young agents or constant ones. There are no clear correlations between antifragility and different good-performance measures, while the best performers seem to fall within a robust threshold. In the case of cryptocurrencies, there is an apparent correlation between high price and high antifragility.

Policymakers in developing countries increasingly see science, technology, and innovation (STI) as an avenue for meeting sustainable development goals (SDGs), with STI-based startups as a key part of these efforts. Market failures call for government interventions in supporting STI for SDGs and publicly-funded incubators can potentially fulfil this role. Using the specific case of India, we examine how publicly-funded incubators could contribute to strengthening STI-based entrepreneurship. India's STI policy and its links to societal goals span multiple decades -- but since 2015 these goals became formally organized around the SDGs. We examine why STI-based incubators were created under different policy priorities before 2015, the role of public agencies in implementing these policies, and how some incubators were particularly effective in addressing the societal challenges that can now be mapped to SDGs. We find that effective incubation for supporting STI-based entrepreneurship to meet societal goals extended beyond traditional incubation activities. For STI-based incubators to be effective, policymakers must strengthen the 'incubation system'. This involves incorporating targeted SDGs in specific incubator goals, promoting coordination between existing incubator programs, developing a performance monitoring system, and finally, extending extensive capacity building at multiple levels including for incubator managers and for broader STI in the country.

Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation).1 One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade.2 Multilayer networks, high-dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi-dimensional social networks has recently garnered the attention of the network science community.3 Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions.6 Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich datasets, giving more accurate and complete pictures of social interactions.