Latest in q-fin.cp

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`Regression Anytime' with Brute-Force SVD TruncationAug 22 2019We propose a new least-squares Monte Carlo algorithm for the approximation of conditional expectations in the presence of stochastic derivative weights. The algorithm can serve as a building block for solving dynamic programming equations, which arise, ... More
QCNN: Quantile Convolutional Neural NetworkAug 21 2019A dilated causal one-dimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk ... More
A lognormal type stochastic volatility model with quadratic driftAug 20 2019This paper presents a novel one-factor stochastic volatility model where the instantaneous volatility of the asset log-return is a diffusion with a quadratic drift and a linear dispersion function. The instantaneous volatility mean reverts around a constant ... More
Linear Stochastic Dividend ModelAug 16 2019In this paper we propose a new model for pricing stock and dividend derivatives. We jointly specify dynamics for the stock price and the dividend rate such that the stock price is positive and the dividend rate non-negative. In its simplest form, the ... More
Modelling Crypto Asset Price Dynamics, Optimal Crypto Portfolio, and Crypto Option ValuationAug 15 2019Despite being described as a medium of exchange, cryptocurrencies do not have the typical attributes of a medium of exchange. Consequently, cryptocurrencies are more appropriately described as crypto assets. A common investment attribute shared by the ... More
Performance of tail hedged portfolio with third moment variation swapAug 14 2019The third moment variation of a financial asset return process is defined by the quadratic covariation between the return and square return processes. The skew and fat tail risk of an underlying asset can be hedged using a third moment variation swap ... More
Computational method for probability distribution on recursive relationships in financial applicationsAug 14 2019In quantitative finance, it is often necessary to analyze the distribution of the sum of specific functions of observed values at discrete points of an underlying process. Examples include the probability density function, the hedging error, the Asian ... More
Accurate Finite Difference Scheme with Hermite Interpolation for Pricing American Put Options Using a Regime Switching ModelAug 14 2019We consider a system of coupled free boundary problems for pricing American put options with regime switching. To solve this system, we first fix the optimal exercise boundary for each regime resulting in multi-variable fixed domains. We further eliminate ... More
Compact Finite Difference Scheme with Hermite Interpolation for Pricing American Put Options Based on Regime Switching ModelAug 14 2019Aug 22 2019We consider a system of coupled free boundary problems for pricing American put options with regime switching. To solve this system, we first fix the optimal exercise boundary for each regime resulting in multi-variable fixed domains. We further eliminate ... More
A zero interest rate Black-Derman-Toy modelAug 12 2019We propose a modification of the classical Black-Derman-Toy (BDT) interest rate tree model, which includes the possibility of a jump with small probability at each step to a practically zero interest rate. The corresponding BDT algorithms are consequently ... More
Fast Pricing of Energy Derivatives with Mean-reverting Jump ProcessesAug 08 2019The law of a mean-reverting (Ornstein-Uhlenbeck) process driven by a compound Poisson with exponential jumps is investigated in the context of the energy derivatives pricing. The said distribution turns out to be related to the self-decomposable gamma ... More
Solving high-dimensional optimal stopping problems using deep learningAug 05 2019Nowadays many financial derivatives which are traded on stock and futures exchanges, such as American or Bermudan options, are of early exercise type. Often the pricing of early exercise options gives rise to high-dimensional optimal stopping problems, ... More
Solving high-dimensional optimal stopping problems using deep learningAug 05 2019Aug 07 2019Nowadays many financial derivatives which are traded on stock and futures exchanges, such as American or Bermudan options, are of early exercise type. Often the pricing of early exercise options gives rise to high-dimensional optimal stopping problems, ... More
Agglomerative Fast Super-Paramagnetic ClusteringAug 02 2019We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series ... More
Agglomerative Fast Super-Paramagnetic ClusteringAug 02 2019Aug 07 2019We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series ... More
Quantifying horizon dependence of asset prices: a cluster entropy approachAug 01 2019Market dynamic is studied by quantifying the dependence of the entropy $S(\tau,n)$ of the clusters formed by the series of the prices $p_t$ and its moving average $\widetilde{p}_{t,n}$ on temporal horizon $M$. We report results of the analysis performed ... More
CVA and vulnerable options in stochastic volatility modelsJul 30 2019In this work we want to provide a general principle to evaluate the CVA (Credit Value Adjustment) for a vulnerable option, that is an option subject to some default event, concerning the solvability of the issuer. CVA is needed to evaluate correctly the ... More
A procedure for loss-optimising default definitions across simulated credit risk scenariosJul 29 2019A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative ... More
Algorithmic market making: the case of equity derivativesJul 29 2019In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic ... More
Algorithmic market making: the case of equity derivativesJul 29 2019Jul 30 2019In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic ... More
Algorithmic market making: the case of equity derivativesJul 29 2019Aug 06 2019In this article, we tackle the problem of a market maker in charge of a book of equity derivatives on a single liquid underlying asset. By using an approximation of the portfolio in terms of its vega, we show that the seemingly high-dimensional stochastic ... More
Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative PricingJul 24 2019We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for ... More
Trading via Image ClassificationJul 23 2019The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a leading financial ... More
Accelerated Share Repurchase and other buyback programs: what neural networks can bringJul 23 2019When firms want to buy back their own shares, they have a choice between several alternatives. If they often carry out open market repurchase, they also increasingly rely on banks through complex buyback contracts involving option components, e.g. accelerated ... More
A Note on Universal Bilinear PortfoliosJul 23 2019This note provides a neat and enjoyable expansion and application of the magnificent Ordentlich-Cover theory of "universal portfolios." I generalize Cover's benchmark of the best constant-rebalanced portfolio (or 1-linear trading strategy) in hindsight ... More
The Effect of Visual Design in Image ClassificationJul 22 2019Financial companies continuously analyze the state of the markets to rethink and adjust their investment strategies. While the analysis is done on the digital form of data, decisions are often made based on graphical representations in white papers or ... More
A model-free backward and forward nonlinear PDEs for implied volatilityJul 17 2019We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined. This would include at least any contingent claim written on a positive stock price whose payoff at a possibly ... More
Quant GANs: Deep Generation of Financial Time SeriesJul 15 2019Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. In this paper, we break through this barrier and present Quant GANs, a data-driven model which is inspired by the recent ... More
Neural network regression for Bermudan option pricingJul 15 2019The pricing of Bermudan options amounts to solving a dynamic programming principle , in which the main difficulty, especially in large dimension, comes from the computation of the conditional expectation involved in the continuation value. These conditional ... More
Gittins' theorem under uncertaintyJul 12 2019We study dynamic allocation problems for discrete time multi-armed bandits under uncertainty, based on the the theory of nonlinear expectations. We show that, under strong independence of the bandits and with some relaxation in the definition of optimality, ... More
Deep Reinforcement Learning in Financial MarketsJul 09 2019Jul 25 2019In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision ... More
Deep Reinforcement Learning in Financial MarketsJul 09 2019In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision ... More
Learning Threshold-Type Investment Strategies with Stochastic Gradient MethodJul 04 2019In online portfolio optimization the investor makes decisions based on new, continuously incoming information on financial assets (typically their prices). In our study we consider a learning algorithm, namely the Kiefer--Wolfowitz version of the Stochastic ... More
A weighted finite difference method for subdiffusive Black Scholes ModelJun 29 2019In this paper we focus on the subdiffusive Black Scholes model. The main part of our work consists of the finite difference method as a numerical approach to the option pricing in the considered model. We derive the governing fractional differential equation ... More
Branching Particle Pricers with Heston ExamplesJun 29 2019The use of sequential Monte Carlo within simulation for path-dependent option pricing is proposed and evaluated. Recently, it was shown that explicit solutions and importance sampling are valuable for efficient simulation of spot price and volatility, ... More
Dealing with Stochastic Volatility in Time Series Using the R Package stochvolJun 28 2019The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior ... More
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvolJun 28 2019Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial ... More
Correlators of Polynomial ProcessesJun 26 2019A process is polynomial if its extended generator maps any polynomial to a polynomial of equal or lower degree. Then its conditional moments can be calculated in closed form, up to the computation of the exponential of the so-called generator matrix. ... More
Lead-lag Relationships in Foreign Exchange MarketsJun 25 2019Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market ... More
Lead-lag Relationships in Foreign Exchange MarketsJun 25 2019Jun 26 2019Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market ... More
Semi-tractability of optimal stopping problems via a weighted stochastic mesh algorithmJun 22 2019In this article we propose a Weighted Stochastic Mesh (WSM) Algorithm for approximating the value of a discrete and continuous time optimal stopping problem. We prove that in the discrete case the WSM algorithm leads to semi-tractability of the corresponding ... More
Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive SurveyJun 18 2019Jul 06 2019With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages ... More
Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive SurveyJun 18 2019With increasing competition and pace in the financial markets, robust forecasting methods are becoming more and more valuable to investors. While machine learning algorithms offer a proven way of modeling non-linearities in time series, their advantages ... More
An agent-based model for designing a financial market that works wellJun 14 2019Designing a financial market that works well is very important for developing and maintaining an advanced economy, but is not easy because changing detailed rules, even ones that seem trivial, sometimes causes unexpected large impacts and side effects. ... More
Bayesian Estimation of Economic Simulation Models using Neural NetworksJun 11 2019Recent advances in computing power and the potential to make more realistic assumptions due to increased flexibility have led to the increased prevalence of simulation models in economics. While models of this class, and particularly agent-based models, ... More
Machine learning with kernels for portfolio valuation and risk managementJun 09 2019Jun 25 2019We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned ... More
Machine learning with kernels for portfolio valuation and risk managementJun 09 2019We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned ... More
Machine learning with kernels for portfolio valuation and risk managementJun 09 2019Jul 19 2019We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned ... More
Machine learning with kernels for portfolio valuation and risk managementJun 09 2019Aug 15 2019We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned ... More
Machine learning with kernels for portfolio valuation and risk managementJun 09 2019Jun 19 2019We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned ... More
Deep learning calibration of option pricing models: some pitfalls and solutionsJun 08 2019Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for ... More
Tensor Processing Units for Financial Monte CarloJun 06 2019Jul 24 2019Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional ... More
Tensor Processing Units for Financial Monte CarloJun 06 2019Jun 10 2019Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional ... More
A simple and efficient numerical method for pricing discretely monitored early-exercise optionsMay 31 2019Jun 03 2019We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based ... More
A simple and efficient numerical method for pricing discretely monitored early-exercise optionsMay 31 2019We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based ... More
Machine Learning Tree and Exact Integration for Pricing American Options in High DimensionMay 22 2019In this paper we modify the Gaussian Process Regression Monte Carlo (GPR-MC) method introduced by Gouden\`ege et al. proposing two efficient techniques which allow one to compute the price of American basket options. In particular, we consider basket ... More
Machine Learning Tree and Exact Integration for Pricing American Options in High DimensionMay 22 2019May 24 2019In this paper we modify the Gaussian Process Regression Monte Carlo (GPR-MC) method introduced by Gouden\`ege et al. proposing two efficient techniques which allow one to compute the price of American basket options. In particular, we consider basket ... More
Machine Learning for Pricing American Options in High-Dimensional Markovian and non-Markovian modelsMay 22 2019Jun 19 2019In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black-Scholes dynamics. The proposed techniques, called GPR ... More
Higher order approximation of call option prices under stochastic volatility modelsMay 15 2019In the present paper, a decomposition formula for the call price due to Al\`{o}s is transformed into a Taylor type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition ... More
What is the Minimal Systemic Risk in Financial Exposure Networks?May 15 2019Management of systemic risk in financial markets is traditionally associated with setting (higher) capital requirements for market participants. There are indications that while equity ratios have been increased massively since the financial crisis, systemic ... More
Approximation of Optimal Transport problems with marginal moments constraintsMay 14 2019Optimal Transport (OT) problems arise in a wide range of applications, from physics to economics. Getting numerical approximate solution of these problems is a challenging issue of practical importance. In this work, we investigate the relaxation of the ... More
PDE models for the valuation of a non callable defaultable coupon bond under an extended JDCEV modelMay 03 2019We consider a two-factor model for the valuation of a non callable defaultable bond which pays coupons at certain given dates. The model under consideration is the Jump to Default Constant Elasticity of Variance (JDCEV) model. The JDCEV model is an improvement ... More
Optimal execution with rough path signaturesMay 02 2019We present a method for obtaining approximate solutions to the problem of optimal execution, based on a signature method. The framework is general, only requiring that the price process is a geometric rough path and the price impact function is a continuous ... More
Fast Calculation of Credit Exposures for Barrier and Bermudan options using Chebyshev interpolationMay 01 2019We introduce a new method to calculate the credit exposure of Bermudan, discretely monitored barrier and European options. Core of the approach is the application of the dynamic Chebyshev method of Glau et al. (2019). The dynamic Chebyshev method delivers ... More
Statistical Learning for Probability-Constrained Stochastic Optimal ControlApr 30 2019We investigate Monte Carlo based algorithms for solving stochastic control problems with probabilistic constraints. Our motivation comes from microgrid management, where the controller tries to optimally dispatch a diesel generator while maintaining low ... More
Online reviews can predict long-term returns of individual stocksApr 30 2019Online reviews are feedback voluntarily posted by consumers about their consumption experiences. This feedback indicates customer attitudes such as affection, awareness and faith towards a brand or a firm and demonstrates inherent connections with a company's ... More
Gated deep neural networks for implied volatility surfacesApr 29 2019In this paper, we propose a gated deep neural network model to predict implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture design ... More
Gated deep neural networks for implied volatility surfacesApr 29 2019Jun 13 2019This paper presents a framework of developing neural networks for predicting implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture ... More
Gated deep neural networks for implied volatility surfacesApr 29 2019May 26 2019This paper presents a framework of developing neural networks for predicting implied volatility surfaces. Conventional financial conditions and empirical evidence related to the implied volatility are incorporated into the neural network architecture ... More
Deep Q-Learning for Nash Equilibria: Nash-DQNApr 23 2019Model-free learning for multi-agent stochastic games is an active area of research. Existing reinforcement learning algorithms, however, are often restricted to zero-sum games, and are applicable only in small state-action spaces or other simplified settings. ... More
A neural network-based framework for financial model calibrationApr 23 2019A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons ... More
ADOL - Markovian approximation of rough lognormal modelApr 19 2019In this paper we apply Markovian approximation of the fractional Brownian motion (BM), known as the Dobric-Ojeda (DO) process, to the fractional stochastic volatility model where the instantaneous variance is modelled by a lognormal process with drift ... More
Inefficiency of the Brazilian Stock Market: the IBOVESPA Future ContractsApr 19 2019We present some indications of inefficiency of the Brazilian stock market based on the existence of strong long-time cross-correlations with foreign markets and indices. Our results show a strong dependence on foreign markets indices as the S\&P 500 and ... More
Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs ModelsApr 17 2019The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis ... More
A Dynamic Bayesian Model for Interpretable Decompositions of Market BehaviourApr 17 2019We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate ... More
A Dynamic Bayesian Model for Interpretable Decompositions of Market BehaviourApr 17 2019May 09 2019We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate ... More
Deep Generative Models for Reject Inference in Credit ScoringApr 12 2019Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this ... More
Deep-learning based numerical BSDE method for barrier optionsApr 11 2019As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions). There is a close association between the solution of PDE and the solution of a backward stochastic differential equation ... More
From (Martingale) Schrodinger bridges to a new class of Stochastic Volatility ModelsApr 09 2019Following closely the construction of the Schrodinger bridge, we build a new class of Stochastic Volatility Models exactly calibrated to market instruments such as for example Vanillas, options on realized variance or VIX options. These models differ ... More
(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual AlgorithmApr 09 2019In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. ... More
(Martingale) Optimal Transport And Anomaly Detection With Neural Networks: A Primal-dual AlgorithmApr 09 2019Apr 11 2019In this paper, we introduce a primal-dual algorithm for solving (martingale) optimal transportation problems, with cost functions satisfying the twist condition, close to the one that has been used recently for training generative adversarial networks. ... More
A stochastic PDE model for limit order book dynamicsApr 05 2019We propose an analytically tractable class of models for the dynamics of a limit order book, described as the solution of a stochastic partial differential equation (SPDE) with multiplicative noise. We provide conditions under which the model admits a ... More
Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction SequencesMar 27 2019A new approach to obtaining market--directional information, based on a non--stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. In our previous work[2], ... More
Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction SequencesMar 27 2019May 01 2019A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. In our previous work[2], ... More
Machine Learning for Pricing American Options in High DimensionMar 27 2019In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning and Monte Carlo simulations. Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes ... More
Stacked Monte Carlo for option pricingMar 26 2019We introduce a stacking version of the Monte Carlo algorithm in the context of option pricing. Introduced recently for aeronautic computations, this simple technique, in the spirit of current machine learning ideas, learns control variates by approximating ... More
A fast method for pricing American options under the variance gamma modelMar 18 2019We investigate methods for pricing American options under the variance gamma model. The variance gamma process is a pure jump process which is constructed by replacing the calendar time by the gamma time in a Brownian motion with drift, which makes it ... More
Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock MarketsMar 15 2019Stock prices are influenced by numerous factors. We present a method to combine these factors and we validate the method by taking the international stock market as a case study. In today's increasingly international economy, return and volatility spillover ... More
Optimal exercise of American options under stock pinningMar 09 2019We address the problem of optimally exercising American options based on the assumption that the underlying stock's price follows a Brownian bridge whose final value coincides with the strike price. In order to do so, we solve the discounted optimal stopping ... More
Optimal exercise of American options under stock pinningMar 09 2019Aug 14 2019We address the problem of optimally exercising American options based on the assumption that the underlying stock's price follows a Brownian bridge whose final value coincides with the strike price. In order to do so, we solve the discounted optimal stopping ... More
Learning the population dynamics of technical trading strategiesMar 06 2019Apr 25 2019We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical ... More
Learning the population dynamics of technical trading strategiesMar 06 2019We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical ... More
Multiscale Features of Cross Correlation of Price and Trading VolumeMar 05 2019Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there is mutual information and entanglement between price and volume. On the other hand, we are curious about scaling features of this entanglement and ... More
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksMar 03 2019Apr 13 2019Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$. ... More
Conditional Density Estimation with Neural Networks: Best Practices and BenchmarksMar 03 2019Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$. ... More
Pricing foreign exchange options under stochastic volatility and interest rates using an RBF--FD methodMar 03 2019This paper proposes a numerical method for pricing foreign exchange (FX) options in a model which deals with stochastic interest rates and stochastic volatility of the FX rate. The model considers four stochastic drivers, each represented by an It\^{o}'s ... More
Optimal Investment-Consumption-Insurance with Durable and Perishable Consumption Goods in a Jump Diffusion MarketMar 02 2019We investigate an optimal investment-consumption and optimal level of insurance on durable consumption goods with a positive loading in a continuous-time economy. We assume that the economic agent invests in the financial market and in durable as well ... More
Gaussian Process Regression for Pricing Variable Annuities with Stochastic Volatility and Interest RateMar 01 2019Jul 22 2019In this paper we investigate price and Greeks computation of a Guaranteed Minimum Withdrawal Benefit (GMWB) Variable Annuity (VA) when both stochastic volatility and stochastic interest rate are considered together in the Heston Hull-White model. We consider ... More
Gaussian Process Regression for Pricing Variable Annuities with Stochastic Volatility and Interest RateMar 01 2019In this paper we develop an efficient approach based on a Machine Learning technique which allows one to quickly evaluate insurance products considering stochastic volatility and interest rate. Specifically, following De Spiegeleer et al., we apply Gaussian ... More
A numerical scheme for the quantile hedging problemFeb 28 2019We consider the numerical approximation of the quantile hedging price in a non-linear market. In a Markovian framework, we propose a numerical method based on a Piecewise Constant Policy Timestepping (PCPT) scheme coupled with a monotone finite difference ... More