nep-cmp New Economics Papers
on Computational Economics
Issue of 2017‒12‒11
eight papers chosen by
Stan Miles
Thompson Rivers University

  1. A Numerical Method for Pricing Discrete Double Barrier Option by Lagrange Interpolation on Jacobi Node By Amirhossein Sobhani; Mariyan Milev
  2. Analyzing tax reforms using the Swedish Labour Income Microsimulation Model By Lundberg, Jacob
  3. A Neural Stochastic Volatility Model By Rui Luo; Weinan Zhang; Xiaojun Xu; Jun Wang
  4. A particle model for the herding phenomena induced by dynamic market signals By Hyeong-Ohk Bae; Seung-yeon Cho; Sang-hyeok Lee; Seok-Bae Yun
  5. Inferring agent objectives at different scales of a complex adaptive system By Dieter Hendricks; Adam Cobb; Richard Everett; Jonathan Downing; Stephen J. Roberts
  6. Transparent Distributed Cross-State Synchronization in Optimistic Parallel Discrete Event Simulation By Matteo Principe; Alessandro Pellegrini; Francesco Quaglia; Bruno Ciciani
  7. Fluctuation identities with continuous monitoring and their application to price barrier options By Carolyn E. Phelan; Daniele Marazzina; Gianluca Fusai; Guido Germano
  8. Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis By Dat Thanh Tran; Alexandros Iosifidis; Juho Kanniainen; Moncef Gabbouj

  1. By: Amirhossein Sobhani; Mariyan Milev
    Abstract: In this paper, a rapid and high accurate numerical method for pricing discrete single and double barrier knock-out call options is presented. According to the well-known Black-Scholes framework, the price of option in each monitoring date could be calculate by computing a recursive integral formula upon the heat equation solution. We have approximated these recursive solutions with the aim of Lagrange interpolation on Jacobi polynomials node. After that, an operational matrix, that makes our computation significantly fast, has been driven. The most important feature of this method is that its CPU time dose not increase when the number of monitoring dates increases. The numerical results confirm the accuracy and efficiency of the presented numerical algorithm.
    Date: 2017–12
  2. By: Lundberg, Jacob (Department of Economics)
    Abstract: Labour income taxation is a central policy topic because labour income makes up the majority of national income and most taxes are in the end taxes on labour. In order to quantify how behavioural responses of labour income earners affect tax revenue, the Swedish Labour Income Microsimulation Model (SLIMM) is constructed and used to evaluate tax reforms. The model simulates taxable income responses, participation responses and income effects. Elasticities are calibrated to match midpoints of estimates found in the quasiexperimental literature. SLIMM is solidly microfounded and uses administrative register data. The model is used to analyze changes to the earned income tax credit (EITC), municipal income taxes and the central government income tax paid by high-income earners. The simulations indicate that the EITC has increased employment by 128,000 and has a degree of self-financing of 21 percent. Almost half of the revenue increase from higher municipal tax rates would disappear due to behavioural responses. Tax cuts for the richest fifth of working Swedes are completely self-financing.
    Keywords: income taxation; behavioural responses; dynamic scoring; microsimulation; tax reform
    JEL: H21 H24
    Date: 2017–11–07
  3. By: Rui Luo; Weinan Zhang; Xiaojun Xu; Jun Wang
    Abstract: In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms stronge baseline methods, including the deterministic models, such as GARCH and its variants, and the stochastic MCMC-based models, and the Gaussian-process-based, on the average negative log-likelihood measure.
    Date: 2017–11
  4. By: Hyeong-Ohk Bae; Seung-yeon Cho; Sang-hyeok Lee; Seok-Bae Yun
    Abstract: In this paper, we study the herding phenomena in financial markets arising from the combined effect of (1) non-coordinated collective interactions between the market players and (2) concurrent reactions of market players to dynamic market signals. By interpreting the expected rate of return of an asset and the favorability on that asset as position and velocity in phase space, we construct an agent-based particle model for herding behavior in finance. We then define two types of herding functionals using this model, and show that they satisfy a Gronwall type estimate and a LaSalle type invariance property respectively, leading to the herding behavior of the market players. Various numerical tests are presented to numerically verify these results.
    Date: 2017–12
  5. By: Dieter Hendricks; Adam Cobb; Richard Everett; Jonathan Downing; Stephen J. Roberts
    Abstract: We introduce a framework to study the effective objectives at different time scales of financial market microstructure. The financial market can be regarded as a complex adaptive system, where purposeful agents collectively and simultaneously create and perceive their environment as they interact with it. It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level. We conjecture that agent classes may in fact operate at different time scales and thus act differently in response to the same perceived market state. Given scale-specific temporal state trajectories and action sequences estimated from aggregate market behaviour, we use Inverse Reinforcement Learning to compute the effective reward function for the aggregate agent class at each scale, allowing us to assess the relative attractiveness of feature vectors across different scales. Differences in reward functions for feature vectors may indicate different objectives of market participants, which could assist in finding the scale boundary for agent classes. This has implications for learning algorithms operating in this domain.
    Date: 2017–12
  6. By: Matteo Principe (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy); Alessandro Pellegrini (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy); Francesco Quaglia (Department DICII, University Tor Vergata, Rome, Italy); Bruno Ciciani (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)
    Abstract: In this report we tackle transparent deploy and seamless execution of sequentially-coded Parallel Discrete Event Simulation (PDES) models on distributed computing architectures. We present an innovative distributed synchronization protocol which allows, in conjunction with ad-hoc Operating System memory management facilities, to access the simulation state of any concurrent Logical Process (LP) running on any node of the distributed computing environment, as if it were locally hosted by a unique node - more specifically, by a unique address space. By relying on our facilities, the simulation model developer is not required to implement neither explicit message passing, nor to rely on annotations or specific programming constructs. He can simply code the accesses to the LPs' states in place (e.g. via pointers), which significantly simplifies the software development process. The burden of synchronization and correct handling of these accesses is demanded from our user-space and kernel-space runtime environment. Our proposal targets Linux on x86 64 systems and has been integrated within the ROOT-Sim open-source optimistic simulation platform, although its design principles, and most parts of the developed software, are of general relevance.
    Keywords: Distributed Simulation ; High-Performance Computing ; PDES ; Programming Models
    Date: 2017
  7. By: Carolyn E. Phelan; Daniele Marazzina; Gianluca Fusai; Guido Germano
    Abstract: We present a numerical scheme to calculate fluctuation identities for exponential L\'evy processes in the continuous monitoring case. This includes the Spitzer identities for touching a single upper or lower barrier, and the more difficult case of the two-barriers exit problem. These identities are given in the Fourier-Laplace domain and require numerical inverse transforms. Thus we cover a gap in the literature that has mainly studied the discrete monitoring case; indeed, there are no existing numerical methods that deal with the continuous case. As a motivating application we price continuously monitored barrier options with the underlying asset modelled by an exponential L\'evy process. We perform a detailed error analysis of the method and develop error bounds to show how the performance is limited by the truncation error of the sinc-based fast Hilbert transform used for the Wiener-Hopf factorisation. By comparing the results for our new technique with those for the discretely monitored case (which is in the Fourier-$z$ domain) as the monitoring time step approaches zero, we show that the error convergence with continuous monitoring represents a limit for the discretely monitored scheme.
    Date: 2017–11
  8. By: Dat Thanh Tran; Alexandros Iosifidis; Juho Kanniainen; Moncef Gabbouj
    Abstract: Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale Limit Order Book (LOB) dataset show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.
    Date: 2017–12

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