An Introduction to Regime Switching Time Series Models

Lange, Theis; Rahbek, Anders

doi:10.1007/978-3-540-71297-8_38

Cited by 30 publications

(20 citation statements)

References 90 publications

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“…RSMs have been applied to pension funds optimization (Hainaut, 2014) and weather derivatives (Elias et al, 2014). For recent surveys on the application of RSMs in empirical finance, we refer to Lange andRahbek (2009) andGuidolin (2011). These models have broader fields of application, covering manpower systems, where both observable and latent sources of dynamic heterogeneity should be accounted for (Guerry, 2011), and reliability analysis (Zhou et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Clustering financial time series: New insights from an extended hidden Markov model

Dias

Vermunt

Ramos

2015

European Journal of Operational Research

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In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and timevarying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.

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Section: Introductionmentioning

confidence: 99%

Clustering financial time series: New insights from an extended hidden Markov model

Dias

Vermunt

Ramos

2015

European Journal of Operational Research

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“…Likewise, some of the most challenging regression tasks include a 'switch' between different model generating processes and dummy attributes [108]. Indeed, similar challenges exist in related fields, such as regime switching in econometrics (e.g., [116]). To date, however, there has not been much emphasis placed on performance under reduced labelling budgets, where this remains a key requirement for streaming data scenarios.…”

Section: Resultsmentioning

confidence: 96%

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Heywood

2014

Genet Program Evolvable Mach

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Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal 'start' or 'end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.

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“…In what follows we refer to some of the papers which appear to be quite close to the subject of our work. A concept of regime switching time series models is widely used in econometrics [10]: these models allow parameters of the conditional mean and variance to vary according to some finite-valued stochastic process with states or regimes, similarly to the clMMMC proposed here. However, the observations are assumed to be generated by a deterministic process with random noise, and the switching process S is either a Markov chain independent of the past observations (like in the olMMMC) or is a deterministic function of the past observations.…”

Section: Related Literature On Hmms the Literature On Estimating Hmmmentioning

confidence: 99%

Recovering Markov models from closed-loop data

2019

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Situations in which recommender systems are used to augument decision making are becoming prevalent in many application domains. Almost always, these prediction tools (recommenders) are created with a view to affecting behavioural change. Clearly, successful applications actuating behavioural change, affect the original model underpinning the predictor, leading to an inconsistency. This feedback loop is often not considered in standard so-called Big Data learning techniques which rely upon machine learning/statistical learning machinery. The objective of this paper is to develop tools that recover unbiased user models in the presence of recommenders. More specifically, we assume that we observe a time series which is a trajectory of a Markov chain R modulated by another Markov chain S, i.e. the transition matrix of R is unknown and depends on the current state of S. The transition matrix of the latter is also unknown. In other words, at each time instant, S selects a transition matrix for R within a given set which consists of known and unknown matrices. The state of S, in turn, depends on the current state of R thus introducing a feedback loop. We propose an Expectation-Maximization (EM) type algorithm, which estimates the transition matrices of S and R. Experimental results are given to demonstrate the efficacy of the approach.

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An Introduction to Regime Switching Time Series Models

Cited by 30 publications

References 90 publications

Clustering financial time series: New insights from an extended hidden Markov model

Clustering financial time series: New insights from an extended hidden Markov model

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Recovering Markov models from closed-loop data

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