Journals
Quant investing in cluster portfolios
This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters.
Portfolio allocation based on expected profit and loss measures
The authors formulate the portfolio allocation problem from a trading point of view, allowing both long and short positions and taking trading and interest rate costs into account.
The price of Bitcoin: GARCH evidence from high-frequency data
This is the first paper that estimates the price determinants of Bitcoin in a generalized autoregressive conditional heteroscedasticity (GARCH) framework using high-frequency data.
A general framework for the identification and categorization of risks: an application to the context of financial markets
This paper is, to the best of the authors' knowledge, the first to develop an algorithm-based and generally applicable framework that generates an extensive and integrated identification and categorization scheme of certain risks by using text mining and…
Risk measures: a generalization from the univariate to the matrix-variate
This paper develops a method for estimating value-at-risk and conditional value-at-risk when the underlying risk factors follow a beta distribution in a univariate and a matrix-variate setting.
The economic cost of a fat finger mistake: a comparative case study from Samsung Securities’s ghost stock blunder
This paper quantifies the economic cost of Samsung Securities’s ghost stock blunder using the synthetic control method.
A descriptive analysis of the client clearing network in the European derivatives landscape
The authors present the findings of a detailed descriptive analysis of client clearing activity for derivatives in the euro area, as well as that of clearing members more broadly.
An empirical analysis of bill payment choices
The aim of this paper is to examine which payment instruments Canadians use for paying bills and to assess the factors driving their bill payment behavior.
The price of liquidity in the reinsurance of fund returns
The authors consider a new type of contract for insuring the returns of hedge funds and aim to extend downside protection to an investment portfolio beyond the first tranche of losses insured by first-loss fee structures, which have become increasingly…
Modeling realized volatility with implied volatility for the EUR/GBP exchange rate
This paper concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate.
A numerical simulation approach to study systemic risk in banking systems
The authors introduce a simple numerical algorithm to study banking systems subject to credit risk. The algorithm is based on a model that is completely defined by only two parameters.
Predicting payment migration in Canada
The authors employ historical LVTS and ACSS data and use the discrete choice demand estimation approach to uncover end users’ and financial institutions’ preferences when deciding which payment instruments and payment systems, respectively, to use.
The CTMC–Heston model: calibration and exotic option pricing with SWIFT
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
Optimal electricity distribution pricing under risk and high photovoltaics penetration
The authors model a hierarchical Stackelberg game in a competitive power market under high behind-the-meter photovoltaics penetration and demand-side uncertainty, with emphasis on the feedback loop between distributed generation via photovoltaics and…
Calibration of local-stochastic and path-dependent volatility models to vanilla and no-touch options
In this paper, the authors consider a large class of continuous semi-martingale models and propose a generic framework for their simultaneous calibration to vanilla and no-touch options.
Using payments data to nowcast macroeconomic variables during the onset of Covid-19
Economic prediction during a crisis is challenging because of the unprecedented economic impact of such an event, which increases the unreliability of traditionally used linear models that employ lagged data. The authors help to address this challenge by…
Sign prediction and sign regression
This paper proposes an approach whereby the loss function regularizes the errors in prediction in different ways.
Penalty methods for bilateral XVA pricing in European and American contingent claims by a partial differential equation model
Under some assumptions, the valuation of financial derivatives, including a value adjustment to account for default risk (the so-called XVA), gives rise to a nonlinear partial differential equation (PDE). The authors propose numerical methods for…
The impact of culture upon operational risk management guidelines in the banking sector of selected Asian countries
The central banks of different countries regulate ORM according to the specificities of their national banking industry. This paper tests the hypothesis that such regulatory openness results in legal texts that are highly influenced by the culture of the…
Gradient boosting for quantitative finance
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
Efficient representation of supply and demand curves on day-ahead electricity markets
The authors model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present the information about electricity prices and demand with far fewer parameters than the existing…
Nowcasting networks
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
From use cases to a big data benchmarking framework in clearing houses and exchanges
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
The selection of predictive variables in aggregate hydroelectric generation models
This paper provides a method to identify the best predictive variables and the appropriate predictive indexes for an aggregate hydropower storage forecasting model. To this end, we use an entropy-based approach.