12,890 research outputs found
A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data
Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange
Identification des régimes et regroupement des séquences pour la prévision des marchés financiers
Abstract : Regime switching analysis is extensively advocated to capture complex behaviors
underlying financial time series for market prediction. Two main disadvantages in
current approaches of regime identification are raised in the literature: 1) the lack of
a mechanism for identifying regimes dynamically, restricting them to switching among
a fixed set of regimes with a static transition probability matrix; 2) failure to utilize
cross-sectional regime dependencies among time series, since not all the time series are
synchronized to the same regime. As the numerical time series can be symbolized into
categorical sequences, a third issue raises: 3) the lack of a meaningful and effective
measure of the similarity between chronological dependent categorical values, in order
to identify sequence clusters that could serve as regimes for market forecasting. In this
thesis, we propose a dynamic regime identification model that can identify regimes
dynamically with a time-varying transition probability, to address the first issue. For
the second issue, we propose a cluster-based regime identification model to account
for the cross-sectional regime dependencies underlying financial time series for market
forecasting. For the last issue, we develop a dynamic order Markov model, making
use of information underlying frequent consecutive patterns and sparse patterns, to
identify the clusters that could serve as regimes identified on categorized financial time
series. Experiments on synthetic and real-world datasets show that our two regime
models show good performance on both regime identification and forecasting, while
our dynamic order Markov clustering model also demonstrates good performance on
identifying clusters from categorical sequences.L'analyse de changement de rĂ©gime est largement prĂ©conisĂ©e pour capturer les comportements complexes sous-jacents aux sĂ©ries chronologiques financiĂšres pour la prĂ©diction du marchĂ©. Deux principaux problĂšmes des approches actuelles d'identifica-tion de rĂ©gime sont soulevĂ©s dans la littĂ©rature. Il sâagit de: 1) l'absence d'un mĂ©canisme d'identification dynamique des rĂ©gimes. Ceci limite la commutation entre un ensemble fixe de rĂ©gimes avec une matrice de probabilitĂ© de transition statique; 2) lâincapacitĂ© Ă utiliser les dĂ©pendances transversales des rĂ©gimes entre les sĂ©ries chronologiques, car toutes les sĂ©ries chronologiques ne sont pas synchronisĂ©es sur le mĂȘme rĂ©gime. Ătant donnĂ© que les sĂ©ries temporelles numĂ©riques peuvent ĂȘtre symbolisĂ©es en sĂ©quences catĂ©gorielles, un troisiĂšme problĂšme se pose: 3) l'absence d'une mesure significative et efficace de la similaritĂ© entre les sĂ©ries chronologiques dĂ©pendant des valeurs catĂ©gorielles pour identifier les clusters de sĂ©quences qui pourraient servir de rĂ©gimes de prĂ©vision du marchĂ©. Dans cette thĂšse, nous proposons un modĂšle d'identification de rĂ©gime dynamique qui identifie dynamiquement des rĂ©gimes avec une probabilitĂ© de transition variable dans le temps afin de rĂ©pondre au premier problĂšme. Ensuite, pour adresser le deuxiĂšme problĂšme, nous proposons un modĂšle d'identification de rĂ©gime basĂ© sur les clusters. Notre modĂšle considĂšre les dĂ©pendances transversales des rĂ©gimes sous-jacents aux sĂ©ries chronologiques financiĂšres avant dâeffectuer la prĂ©vision du marchĂ©. Pour terminer, nous abordons le troisiĂšme problĂšme en dĂ©veloppant un modĂšle de Markov d'ordre dynamique, en utilisant les informations sous-jacentes aux motifs consĂ©cutifs frĂ©quents et aux motifs clairsemĂ©s, pour identifier les clusters qui peuvent servir de rĂ©gimes identifiĂ©s sur des sĂ©ries chronologiques financiĂšres catĂ©gorisĂ©es. Nous avons menĂ© des expĂ©riences sur des ensembles de donnĂ©es synthĂ©tiques et du monde rĂ©el. Nous dĂ©montrons que nos deux modĂšles de rĂ©gime prĂ©sentent de bonnes performances Ă la fois en termes d'identification et de prĂ©vision de rĂ©gime, et notre modĂšle de clustering de Markov d'ordre dynamique produit Ă©galement de bonnes performances dans l'identification de clusters Ă partir de sĂ©quences catĂ©gorielles
Factor Analysed Hidden Markov Models for Conditionally Heteroscedastic Financial Time Series
In this article we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co-movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor models with hidden Markov chain models (HMM) we derive a dynamical local model for segmentation and prediction of multivariate conditionally heteroscedastic financial time series. We concentrate, more precisely on situations where the factor variances are modeled by univariate GQARCH processes. The intuition behind our approach is the use a piece-wise multivariate and linear process - which we can also regard as a mixed-state dynamic linear system - for modeling the regime switches. In particular, we supposed that the observed series can be modeled using a time varying parameter model with the assumption that the evolution of these parameters is governed by a first-order hidden Markov process. The EM algorithm that we have developed for the maximum likelihood estimation, is based on a quasi-optimal switching Kalman filter approach combined with a Viterbi approximation which yield inferences about the unobservable path of the common factors, their variances and the latent variable of the state process. Extensive Monte Carlo simulations and preliminary experiments obtained with daily foreign exchange rate returns of eight currencies show promising results
Filtering and forecasting commodity futures prices under an HMM framework
We propose a model for the evolution of arbitrage-free futures prices under a regime-switching framework. The estimation of model parameters is carried out using the hidden Markov filtering algorithms. Comprehensive numerical experiments on real financial market data are provided to illustrate the effectiveness of our algorithm. In particular, the model is calibrated with data from heating oil futures and its forecasting performance as well as statistical validity is investigated. The proposed model is parsimonious, self-calibrating and can be very useful in predicting futures prices. © 2013 Elsevier B.V
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
The financial crisis of 2008 generated interest in more transparent,
rules-based strategies for portfolio construction, with Smart beta strategies
emerging as a trend among institutional investors. While they perform well in
the long run, these strategies often suffer from severe short-term drawdown
(peak-to-trough decline) with fluctuating performance across cycles. To address
cyclicality and underperformance, we build a dynamic asset allocation system
using Hidden Markov Models (HMMs). We test our system across multiple
combinations of smart beta strategies and the resulting portfolios show an
improvement in risk-adjusted returns, especially on more return oriented
portfolios (up to 50 in excess of market annually). In addition, we propose
a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM)
algorithm that performs feature selection simultaneously with the training of
the HMM, to improve regime identification. We evaluate our systematic trading
system with real life assets using MSCI indices; further, the results (up to
60 in excess of market annually) show model performance improvement with
respect to portfolios built using full feature HMMs
Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions
This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches
BUSINESS CYCLE AND SECTORAL FLUCTUATIONS: A NONLINEAR MODEL FOR CĂTE DâIVOIRE
Although the share of service sector in CĂŽte dâIvoireâs real GDP is higher than other sectors, it is widely recognized that the Ivorian economy is mainly based on agricultural sector and thus the fluctuations in this âmotorâ sector could have a huge impact on the growth process of the country. We examine the issue of existence, identification and interaction of business cycle in CĂŽte dâIvoireâs GDP and compare its fluctuations with the disaggregated main economic sectors (agriculture, industry and service), by using a univariate Markov regime switching model and its multivariate version over the period 1970-2001. We found similarities and simultaneity of business cycle between the sectors of the economy. While the real GDPâs business cycle can be characterized, according to its mean duration (around 10 years) as a Juglar type cycle, the sectorsâ cycles mean duration is shorter from 4 to 5 years.Business cycles, Economic Growth, Markov switching, Structural breaks, Time series analysis
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