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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

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

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

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 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

Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation DegreeFeb 24 2019With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper ... More

Closed-End Formula for options linked to Target Volatility StrategiesFeb 23 2019Recent years have seen an emerging class of structured financial products based on options linked to dynamic asset allocation strategies. One of the most chosen approach is the so-called target volatility mechanism. It shifts between risky and riskless ... More

Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book DataFeb 21 2019Deep Learning (DL) models can be used to tackle time series analysis tasks with great success. However, the performance of DL models can degenerate rapidly if the data are not appropriately normalized. This issue is even more apparent when DL is used ... More

A Comparison of Economic Agent-Based Model Calibration MethodsFeb 15 2019Interest in agent-based models of financial markets and the wider economy has increased consistently over the last few decades, in no small part due to their ability to reproduce a number of empirically-observed stylised facts that are not easily recovered ... More

Building arbitrage-free implied volatility: Sinkhorn's algorithm and variantsFeb 12 2019We consider the classical problem of building an arbitrage-free implied volatility surface from bid-ask quotes. We design a fast numerical procedure, for which we prove the convergence, based on the Sinkhorn algorithm that has been recently used to solve ... More

Low-rank tensor approximation for Chebyshev interpolation in parametric option pricingFeb 12 2019Treating high dimensionality is one of the main challenges in the development of computational methods for solving problems arising in finance, where tasks such as pricing, calibration, and risk assessment need to be performed accurately and in real-time. ... More

Physics and Derivatives: Effective-Potential Path-Integral Approximations of Arrow-Debreu DensitiesFeb 10 2019We show how effective-potential path-integrals methods, stemming on a simple and nice idea originally due to Feynman and successfully employed in Physics for a variety of quantum thermodynamics applications, can be used to develop an accurate and easy-to-compute ... More

Static and semi-static hedging as contrarian or conformist betsFeb 07 2019In this paper, we argue that, once the costs of maintaining the hedging portfolio are properly taken into account, semi-static portfolios should more properly be thought of as separate classes of derivatives, with non-trivial, model-dependent payoff structures. ... More

A copula based Markov Reward approach to the credit spread in European UnionFeb 02 2019In this paper, we propose a methodology based on piece-wise homogeneous Markov chain for credit ratings and a multivariate model of the credit spreads to evaluate the financial risk in European Union (EU). Two main aspects are considered: how the financial ... More

Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA ComputationsJan 30 2019Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well ... More

Pricing options and computing implied volatilities using neural networksJan 25 2019This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function ... More

A Backward Simulation Method for Stochastic Optimal Control ProblemsJan 20 2019A number of optimal decision problems with uncertainty can be formulated into a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC) algorithm is a popular numerical method to approach solutions of such stochastic control problems ... More

A Probabilistic Approach to Nonparametric Local VolatilityJan 17 2019Jan 23 2019The local volatility model is a widely used for pricing and hedging financial derivatives. While its main appeal is its capability of reproducing any given surface of observed option prices---it provides a perfect fit---the essential component is a latent ... More

Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approachJan 17 2019In this work, we propose a new policy iteration algorithm for pricing Bermudan options when the payoff process cannot be written as a function of a lifted Markov process. Our approach is based on a modification of the well-known Longstaff Schwartz algorithm, ... More

Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping ProblemsJan 11 2019In this paper, we propose deep learning algorithms for ranking response surfaces, with applications to optimal stopping problems in financial mathematics. The problem of ranking response surfaces is motivated by estimating optimal feedback policy maps ... More

Forecasting interest rates through Vasicek and CIR models: a partitioning approachJan 08 2019Jan 15 2019The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates (for each maturity) based on rolling windows from observed financial market data. The novelty, apart from ... More

Timing the market: the economic value of price extremesJan 07 2019By decomposing asset returns into potential maximum gain (PMG) and potential maximum loss (PML) with price extremes, this study empirically investigated the relationships between PMG and PML. We found significant asymmetry between PMG and PML. PML significantly ... More

A volatility-of-volatility expansion of the option prices in the SABR stochastic volatility modelDec 24 2018We propose a general, very fast method to quickly approximate the solution of a parabolic Partial Differential Equation (PDEs) with explicit formulas. Our method also provides equaly fast approximations of the derivatives of the solution, which is a challenge ... More

Hierarchical adaptive sparse grids for option pricing under the rough Bergomi modelDec 20 2018The rough Bergomi (rBergomi) model, introduced recently in [4], is a promising rough volatility model in quantitative finance. This new model exhibits consistent results with the empirical fact of implied volatility surfaces being essentially time-invariant. ... More

Double Deep Q-Learning for Optimal ExecutionDec 17 2018Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach ... More

Deep neural networks algorithms for stochastic control problems on finite horizon, Part 2: numerical applicationsDec 13 2018This paper presents several numerical applications of deep learning-based algorithms that have been analyzed in [11]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance ... More

Trade Selection with Supervised Learning and OCADec 09 2018In recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. One of the key points in generating proficient methods is ... More

Continual Learning Augmented Investment DecisionsDec 06 2018Jan 25 2019Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which is based on an explicit memory structure and a feed forward neural network ... More

Lagged correlation-based deep learning for directional trend change prediction in financial time seriesNov 27 2018Nov 29 2018Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to predict example of such systems. We approach predictions ... More

Bull Bear Balance: A Cluster Analysis of Socially Informed Financial VolatilityNov 26 2018Using a method rooted in information theory, we present results that have identified a large set of stocks for which social media can be informative regarding financial volatility. By clustering stocks based on the joint feature sets of social and financial ... More

BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order BooksNov 25 2018We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data source representing trading and pricing movements. We demonstrate that uncertainty ... More

Idiosyncrasies and challenges of data driven learning in electronic tradingNov 23 2018Nov 30 2018We outline the idiosyncrasies of neural information processing and machine learning in quantitative finance. We also present some of the approaches we take towards solving the fundamental challenges we face.

Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep LearningNov 21 2018In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field ... More

Neural Network for CVA: Learning Future ValuesNov 21 2018A new challenge to quantitative finance after the recent financial crisis is the study of credit valuation adjustment (CVA), which requires modeling of the future values of a portfolio. In this paper, following recent work in [Weinan E(2017), Han(2017)], ... More

An Aspect of Optimal Regression Design for LSMCNov 20 2018Practitioners sometimes suggest to use a combination of Sobol sequences and orthonormal polynomials when applying an LSMC algorithm for evaluation of option prices or in the context of risk capital calculation under the Solvency II regime. In this paper, ... More

Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian CrisisNov 20 2018Entropy measures in their various incarnations play an important role in the study of stochastic time series providing important insights into both the correlative and the causative structure of the stochastic relationships between the individual components ... More

The ETS challenges: a machine learning approach to the evaluation of simulated financial time series for improving generation processesNov 19 2018This paper presents an evaluation framework that attempts to quantify the "degree of realism" of simulated financial time series, whatever the simulation method could be, with the aim of discover unknown characteristics that are not being properly reproduced ... More

CVA and vulnerable options pricing by correlation expansionsNov 18 2018We consider the problem of computing the Credit Value Adjustment ({CVA}) of a European option in presence of the Wrong Way Risk ({WWR}) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might ... More

Bayesian learning for the Markowitz portfolio selection problemNov 16 2018We study the Markowitz portfolio selection problem with unknown drift vector in the multidimensional framework. The prior belief on the uncertain expected rate of return is modeled by an arbitrary probability law, and a Bayesian approach from filtering ... More

Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural NetworkNov 15 2018Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical ... More

Stochastic Algorithmic Differentiation of (Expectations of) Discontinuous Functions (Indicator Functions)Nov 14 2018Nov 26 2018In this paper we present a method for the accurate estimation of the derivative (aka.~sensitivity) of expectations of functions involving an indicator function by combining a stochastic algorithmic differentiation and a regression. The method is an improvement ... More

Predicting Distresses using Deep Learning of Text Segments in Annual ReportsNov 13 2018Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. ... More

A Splitting Strategy for the Calibration of Jump-Diffusion ModelsNov 05 2018We present a detailed analysis and implementation of a splitting strategy to identify simultaneously the local-volatility surface and the jump-size distribution from quoted European prices. The underlying model consists of a jump-diffusion driven asset ... More

High-order compact finite difference scheme for option pricing in stochastic volatility with contemporaneous jump modelsOct 30 2018Mar 07 2019We extend the scheme developed in B. D\"uring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2019, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived ... More

High-order compact finite difference scheme for option pricing in stochastic volatility with contemporaneous jump modelsOct 30 2018We extend the scheme developed in B. D\"uring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2017, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived ... More

Geometrically Convergent Simulation of the Extrema of Lévy ProcessesOct 25 2018We develop a novel Monte Carlo algorithm for the simulation from the joint law of the position, the running supremum and the time of the supremum of a general L\'{e}vy process at an arbitrary finite time. We prove that the bias decays geometrically, in ... More

A Relaxed Optimization Approach for Cardinality-Constrained Portfolio OptimizationOct 24 2018A cardinality-constrained portfolio caps the number of stocks to be traded across and within groups or sectors. These limitations arise from real-world scenarios faced by fund managers, who are constrained by transaction costs and client preferences as ... More

Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing ModelsOct 22 2018We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to recalibration of model parameters (in contradiction to the model assumptions). ... More

Reverse Quantum Annealing Approach to Portfolio Optimization ProblemsOct 19 2018Oct 25 2018We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples ... More

On the sensitivity analysis of energy quanto optionsOct 12 2018In recent years there has been an advent of quanto options in energy markets. The structure of the payoff is rather a different type from other markets since it is written as a product of an underlying energy index and a measure of temperature. In the ... More

Martingale Functional Control variates via Deep LearningOct 11 2018We propose black-box-type control variate for Monte Carlo simulations by leveraging the Martingale Representation Theorem and artificial neural networks. We developed several learning algorithms for finding martingale control variate functionals both ... More

Lifting the Heston modelOct 11 2018How to reconcile the classical Heston model with its rough counterpart? We introduce a lifted version of the Heston model with n multi-factors, sharing the same Brownian motion but mean reverting at different speeds. Our model nests as extreme cases the ... More

An Introduction to fast-Super Paramagnetic ClusteringOct 05 2018We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach ... More

An Efficient Approach for Removing Look-ahead Bias in the Least Square Monte Carlo Algorithm: Leave-One-OutOct 04 2018The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz [2001] is the most widely used method for pricing options with early exercise features. The LSM estimator contains look-ahead bias, and the conventional technique of removing ... More

Semi-supervised Text Regression with Conditional Generative Adversarial NetworksOct 02 2018Nov 11 2018Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt ... More

Inverse Gaussian quadrature and finite normal-mixture approximation of generalized hyperbolic distributionOct 02 2018In this study, a numerical quadrature for the generalized inverse Gaussian distribution is derived from the Gauss--Hermite quadrature by exploiting its relationship with the normal distribution. Unlike Gaussian quadrature, the proposed quadrature exactly ... More

An extension of Heston's SV model to Stochastic Interest RatesSep 24 2018In 'A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options', Heston proposes a Stochastic Volatility (SV) model with constant interest rate and derives a semi-explicit valuation formula. Heston also ... More

Exact Solutions for a GBM-type Stochastic Volatility Model having a Stationary DistributionSep 23 2018We find various exact solutions for a new stochastic volatility (SV) model: the transition probability density, European-style option values, and (when it exists) the martingale defect. This may represent the first example of an SV model combining exact ... More

Constructing Financial Sentimental Factors in Chinese Market Using Natural Language ProcessingSep 22 2018In this paper, we design an integrated algorithm to evaluate the sentiment of Chinese market. Firstly, with the help of the web browser automation, we crawl a lot of news and comments from several influential financial websites automatically. Secondly, ... More

Pricing American Options by Exercise Rate OptimizationSep 19 2018We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal \emph{exercise ... More

Geometric Local Variance Gamma modelSep 19 2018Dec 25 2018This paper describes another extension of the Local Variance Gamma model originally proposed by P. Carr in 2008, and then further elaborated on by Carr and Nadtochiy, 2017 (CN2017), and Carr and Itkin, 2018 (CI2018). As compared with the latest version ... More

Enabling Scientific Crowds: The Theory of Enablers for Crowd-Based Scientific InvestigationSep 18 2018Evidence shows that in a significant number of cases the current methods of research do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of critical decisions at all levels, from personal ... More

A Language for Large-Scale Collaboration in Economics: A Streamlined Computational Representation of Financial ModelsSep 17 2018This paper introduces Sigma, a domain-specific computational representation for collaboration in large-scale for the field of economics. A computational representation is not a programming language or a software platform. A computational representation ... More

Kernel-based collocation methods for Heath-Jarrow-Morton models with Musiela parametrizationSep 15 2018We propose kernel-based collocation methods for numerical solutions to Heath-Jarrow-Morton models with Musiela parametrization. The methods can be seen as the Euler-Maruyama approximation of some finite dimensional stochastic differential equations, and ... More

Computing Credit Valuation Adjustment solving coupled PIDEs in the Bates modelSep 14 2018Credit value adjustment (CVA) is the charge applied by financial institutions to the counterparty to cover the risk of losses on a counterpart default event. In this paper we estimate such a premium under the Bates stochastic model (Bates [4]), which ... More

Measuring Systematic Risk with Neural Network Factor ModelSep 13 2018In this paper, we measure systematic risk with a new nonparametric factor model, the neural network factor model. The suitable factors for systematic risk can be naturally found by inserting daily returns on a wide range of assets into the bottleneck ... More