Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

Maruotti, Antonello; Punzo, Antonio; Bagnato, Luca

doi:10.1093/jjfinec/nby019

Cited by 20 publications

(9 citation statements)

References 52 publications

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“…When it comes to assess the relative predictive accuracy of HMM vs. alternative prediction models, our results appear to be weaker than some recent literature concerning other asset classes; see, e.g., Catania et al [54], Koki et al [27], and Hotz-Behofsits et al [55] with reference to cryptocurrencies, Luo et al [23] and Ma et al [24] with reference to commodity realized volatility, Guidolin and Pedio [56] with reference to risk-free interest rates, and Maruotti et al [57] for stock returns, for which HMMs forecast very accurately both moments and densities. However, this weaker performance may also depend on the fact that, in our paper, we have entertained only time-homogeneous HMMs.…”

Section: Lightcontrasting

confidence: 93%

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

Guidolin

Pedio

2022

Forecasting

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In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts.

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Section: Lightcontrasting

confidence: 93%

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

Guidolin

Pedio

2022

Forecasting

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“…HSMM was first proposed by Baum et al [45] and has been successfully used in many applications, including word recognition task [46], daily return series modeling in financial market [47,48], equipment health diagnosis and prognosis [49], activity recognition and abnormality detection [50], DNA analysis [51], and online failure prediction [52]. It is worth noting that in our work, we referred to the basic idea of online failure prediction in a commercial telecommunication system by Salfner et al [33,52].…”

Section: Hidden Semi-markov Modelmentioning

confidence: 99%

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Qiao

Zhang

Deng

2020

Discrete Dynamics in Nature and Society

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Social unrest events are common happenings in modern society which need to be proactively handled. An effective method is to continuously assess the risk of upcoming social unrest events and predict the likelihood of these events. Our previous work built a hidden Markov model- (HMM-) based framework to predict indicators associated with country instability, leaving two shortcomings which can be optimized: omitting event participants’ interaction and implicitly learning the state residence time. Inspired by this, we propose a new prediction framework in this paper, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). The feature called BoEAG (Bag-of-Event-Association-subGraph) is constructed based on frequent subgraph mining and the bag of word model. The new framework leverages the large-scale digital history events captured from GDELT (Global Data on Events, Location, and Tone) to characterize the transitional process of the social unrest events’ evolutionary stages, uncovering the underlying event development mechanics and formulating the social unrest event prediction as a sequence classification problem based on Bayes decision. Experimental results with data from five main countries in Southeast Asia demonstrate the effectiveness of the new method, which outperforms the traditional HMM by 5.3% to 16.8% and the logistic regression by 11.2% to 43.6%.

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“…Compared to the normal distribution, the leptokurtic-normal has an additional parameter β governing the excess kurtosis and, advantageously with respect to other heavy-tailed elliptical distributions, its parameters correspond to quantities of direct interest (mean, covariance matrix, and excess kurtosis). Such a distribution was successfully applied in the modelling of biometric and financial data (Bagnato et al, 2017 andMaruotti et al, 2019).…”

Section: The Modelmentioning

confidence: 99%

Unconstrained representation of orthogonal matrices with application to common principle components

Bagnato¹,

Punzo²

2019

Preprint

Self Cite

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Many statistical problems involve the estimation of a (d × d) orthogonal matrix Q. Such an estimation is often challenging due to the orthonormality constraints on Q. To cope with this problem, we propose a very simple decomposition for orthogonal matrices which we abbreviate as PLR decomposition. It produces a one-to-one correspondence between Q and a (d × d) unit lower triangular matrix L whose d (d − 1) /2 entries below the diagonal are unconstrained real values. Once the decomposition is applied, regardless of the objective function under consideration, we can use any classical unconstrained optimization method to find the minimum (or maximum) of the objective function with respect to L. For illustrative purposes, we apply the PLR decomposition in common principle components analysis (CPCA) for the maximum likelihood estimation of the common orthogonal matrix when a multivariate leptokurtic-normal distribution is assumed in each group. Compared to the commonly used normal distribution, the leptokurtic-normal has an additional parameter governing the excess kurtosis; this makes the estimation of Q in CPCA more robust against mild outliers. The usefulness of the PLR decomposition in leptokurtic-normal CPCA is illustrated by two biometric data analyses.

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Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

Cited by 20 publications

References 52 publications

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Unconstrained representation of orthogonal matrices with application to common principle components

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