In this paper we describe three stochastic models based on a semi-Markov chains approach and its generalizations to study the high frequency price dynamics of traded stocks. The three models are: a simple semi-Markov chain model, an indexed semi-Markov chain model and a weighted indexed semi-Markov chain model. We show…
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48 results for “semi-Markov model”
In this paper we propose a semi-Markov modulated model of interest rates. We assume that the switching process is a semi-Markov process with finite state space E and the modulated process is a diffusive process. We derive recursive equations for the higher order moments of the discount factor and we describe a Monte Ca…
We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday return are described by a discrete time homogeneous semi-Markov process and the overnight returns are modeled by a Markov chain. Based on this assumptions we derived…
The paper models stock order books with varying price changes.
problem Modeling stock order books with variable price changes.
method Developed a general semi-Markov model with two and multiple states.
result Validated the model with real data from multiple companies.
We propose a statistical approach to tornadoes modeling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modeling the tornadoes intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornadoes …
In this paper, we model financial markets with semi-Markov volatilities and price covarinace and correlation swaps for this markets. Numerical evaluations of vari- nace, volatility, covarinace and correlations swaps with semi-Markov volatility are presented as well. The novelty of the paper lies in pricing of volatilit…
New method infers hidden states in continuous-time phenomena better than traditional models.
problem Traditional HSMM's are limited to discrete time grids and cannot handle irregularly spaced data.
method Formulated integro-differential forward and backward equations for CTSMC's, introduced scalable Viterbi-type algorithm.
result Efficiently solved equations for posterior marginals and path estimates.
In this paper we propose a new stochastic model based on a generalization of semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that the financial returns are described by a weighted indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able …
A Semi-Markov Chain Approach to Modeling Respiratory Patterns Prior to Extubation in Preterm Infantseess.SP
Study uses semi-Markov models to analyze respiratory patterns of preterm infants before extubation.
problem Analyzing respiratory patterns of preterm infants before and after extubation.
method Developed semi-Markov models to compare respiratory patterns of infants who succeeded extubation and those who required reintubation.
result Semi-Markov models reveal unique similarities and differences between infants who succeeded extubation and those who required reintubation.
In this paper we propose a bivariate generalization of a weighted indexed semi-Markov chains to study the high frequency price dynamics of traded stocks. We assume that financial returns are described by a weighted indexed semi-Markov chain model. We show, through Monte Carlo simulations, that the model is able to repr…
We consider the problem of constructing an appropriate multivariate model for the study of the counterparty credit risk in credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However the markovian assumption may be inappropriate for the study of the dyna…
We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov which depends also on a memory index. The index is introduced to take into account periods of high a…
New RL framework models continuous-time dynamics using neural ODEs.
problem Modeling continuous-time dynamics in semi-Markov decision processes.
method Model-based reinforcement learning with neural ODEs.
result High-performing policies developed with minimal data.
New model predicts stock volume patterns.
problem Predicting high frequency stock volume dynamics.
method Semi-Markov chain model for intraday volume changes.
result Model accurately reproduces volume evolution patterns.
The study extends asset pricing models to include time-dependent volatility and age-dependent regime switching.
problem Asset pricing in a market with time-varying interest rates and volatilities.
method Extension of Markov-modulated models to semi-Markov processes with age-dependent and time-dependent volatility.
result Option pricing in the extended model is equivalent to solving an integral equation.
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian nonparametric hidden Markov models that enhance a posteriori state-persistence in particular. This paper also introduces a new Bayesian non…
Generalized model for firm valuation considering semi-Markovian dividend growth.
problem Valuation of firms based on semi-Markovian dividend growth rates.
method Discrete time semi-Markov chain model with measurable space, new equations for price-dividend ratios, approximation methods.
result Established sufficient conditions for finiteness of fundamental prices and risks, new equations for first and second order price-dividend ratios.
New model for time series classification from single example.
problem Classifying time series patterns from limited data.
method Developed a Hidden semi-Markov Model with variable state duration.
result Different representations of state duration have distinct strengths and weaknesses.
Model predicts extubation success in preterm infants.
problem Predicting successful extubation in preterm infants to minimize complications.
method Applied Markov and semi-Markov chain models to analyze respiratory patterns.
result Up to 84% of infants who failed extubation could have been predicted prior to extubation.
Paper proposes a new method to model event sequences in information systems.
problem Analyzing event logs to understand system procedures and predict changes.
method Combines hidden semi-Markov model and classification trees learning.
result The proposed approach can identify frequent sequence patterns relevant to observable events.
Model stock price dynamics using semi-Markov processes.
problem Model stock price dynamics through a semi-Markov process.
method Use semi-Markov process with Poisson random measure, establish existence and uniqueness of solution, derive HJB equation.
result Obtain expressions for optimal controls and value function using HJB equation.
Proposes a multi-state model for evaluating life insurance conversion options.
problem Evaluating the value of conversion options in life insurance contracts.
method Age-indexed semi-Markov chains to model duration, time non-homogeneity, and ageing effects.
result Validates the model's ability to accurately evaluate conversion option values.
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particul…
Optimal strategy found for liquidating large-tick stocks.
problem Maximizing wealth in liquidating stock positions.
method Semi-Markov decision process, Laplace method, queueing theory, dynamic programming.
result Optimal liquidation policy found.
Generative model predicts daily activity sequences with duration-aware dynamics.
problem Accurately forecasting granular daily activity sequences for energy demand.
method Hierarchical semi-Markov models with duration-aware dynamics.
result Explicitly modeling activity durations improves predictive performance.
Study long-term behavior of semi-Markov modulated processes using integral functions.
problem Analyzing long-term behavior of semi-Markov modulated processes involving integral functions.
method Using ergodic semi-Markovian environment and affine stochastic recurrence equation.
result Mixture type laws emerge in long-term limit for processes.
Anomaly detection for aviation safety using SMS-VAR models.
problem Detecting anomalous flight segments in aviation systems.
method Semi-Markov switching vector autoregressive (SMS-VAR) model for anomaly detection.
result The framework can detect various types of anomalies and key parameters involved.
Algorithmic and High-Frequency Trading Problems for Semi-Markov and Hawkes Jump-Diffusion Modelsq-fin.MF
Paper develops models for better HFT and algorithmic trading.
problem Inaccurate LOB dynamics in financial markets.
method Semi-Markov and Hawkes jump-diffusion models for LOB dynamics.
result Improved trading strategies through precise model application.
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorit…
Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Modelsstat.ML
New method reduces state redundancy in HSMM for driving patterns.
problem Overestimation of states in HSMM models.
method Robust HDP-HSMM (rHDP-HSMM) method to reduce redundant states.
result Improved consistency and accurate inference of driving maneuvers.
Deep architecture such as hierarchical semi-Markov models is an important class of models for nested sequential data. Current exact inference schemes either cost cubic time in sequence length, or exponential time in model depth. These costs are prohibitive for large-scale problems with arbitrary length and depth. In th…
Developed a new statistic to test binary regime switching models.
problem Testing the model assumption of binary regime switching extension of GBM.
method Proposed a new discriminating statistics and identified an admissible class of regime switching candidate models.
result Sampling distribution of the test statistics differs significantly between different regime switching models.
Develops a new model for analyzing clinical data with irregular sampling.
problem Analyzing irregularly sampled, temporally correlated clinical data.
method Hidden Absorbing Semi-Markov Model (HASMM) with a novel EM algorithm and forward-filtering algorithm.
result Demonstrates improved diagnostic and prognostic utility in critical care settings.
Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Unlike expectation maximization (EM), our approach cor…
We study the effect of investor inertia on stock price fluctuations with a market microstructure model comprising many small investors who are inactive most of the time. It turns out that semi-Markov processes are tailor made for modelling inert investors. With a suitable scaling, we show that when the price is driven …
The paper develops methods to price derivatives in a time-varying, age-dependent market.
problem Pricing derivatives in a market with time-inhomogeneous volatility and age-dependent processes.
method Geometric Brownian motion model with time-varying volatility and age-dependent semi-Markov processes. Solves a non-local PDE and integral equation.
result Explicit expressions for derivative prices and hedging strategies are derived.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
Method detects surgical deviations during laparoscopic rectopexy.
problem Detecting deviations from standard surgical processes during laparoscopic rectopexy.
method Multi-dimensional non-linear temporal scaling with a hidden semi-Markov model.
result Over 90% accuracy in detecting deviations.
New CTBNs with clocks allow for non-exponential survival times.
problem Modeling phenomena with non-exponential survival times in continuous time.
method Introduced node-wise clocks to construct graph-coupled semi-Markov chains, enabling non-exponential survival times without auxiliary states.
result Parameter and structure inference algorithms provided, demonstrating advantages over current CTBN extensions.
New model explains price, volume, and waiting times in financial markets.
problem Understanding price, volume, and waiting times in financial markets.
method Generalized semi-Markov chains with endogenous index process and copulae for dependence.
result Model accurately reproduces empirical evidence from Italian stock market data.
Automatically discovers SMDP models in DQN representations for better reinforcement learning visualization.
problem Lack of tools to analyze and visualize the temporal abstractions learned by DRL agents.
method Develops a novel method to automatically discover an internal SMDP model in DQN representations and visualizes it using a directed graph above a t-SNE map.
result Shows evidence of hierarchical state aggregation learned by DQNs.
Develops a model to predict clinical deterioration in ICU patients.
problem Predicting clinical deterioration in critically ill patients.
method Semi-Markov Switching Linear Gaussian Model (SSLGM) with censored data.
result SSLGM significantly outperforms existing risk scores.
The study discovers digital biomarkers for Parkinson's Disease using optimized transitions and emissions in HSMM.
problem Identifying digital biomarkers for Parkinson's Disease.
method Proposed a Hidden Semi-Markov Model (HSMM) to model Parkinson's Disease patients' step and stride periodic cycles.
result The HSMM allows for more informative characterization of Parkinson's Disease patients/controls by considering the duration spent in each state.
The paper analyzes how options can improve learning in reinforcement learning.
problem Understanding when and how options benefit reinforcement learning.
method Derives upper and lower bounds on regret for a variant of UCRL with options.
result Proves that learning with options can significantly reduce regret compared to primitive actions.
The paper prices European options in a model with changing regimes and jumps.
problem Pricing European options in a model with changing regimes and jumps.
method A regime-switching jump diffusion model with semi-Markov process.
result The locally risk minimizing price of European options is found.
This paper includes an original self contained proof of well-posedness of an initial-boundary value problem involving a non-local parabolic PDE which naturally arises in the study of derivative pricing in a generalized market model. We call this market model a semi-Markov modulated market. Although a wellposedness resu…
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
problem Reduce communication frequency in networked AP systems while maintaining control performance.
method Develops a DRL-based controller that avoids explicit update timing learning, using a semi-Markov decision process (SMDP).
result Improves communication efficiency without sacrificing control performance.
Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approachcs.AI
New approach uses deep reinforcement learning for vehicle dispatching, reducing waiting times.
problem Dynamic vehicle dispatching problem in various contexts.
method Event-based semi-Markov decision process with deep q-learning.
result Deep reinforcement learning policies outperform heuristic methods in New York City data.