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Blagov, Boris [Verfasser], and Michael [Akademischer Betreuer] Funke. "Four Essays on Markov-Switching DSGE and Markov-Switching VAR Models / Boris Blagov. Betreuer: Michael Funke." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2016. http://d-nb.info/1103233408/34.
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Mazali, Rogério. "Improving mutual fund market timing measures: a markov switching approach." reponame:Repositório Institucional do FGV, 2001. http://hdl.handle.net/10438/55.
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Previous issue date: 2001-07-31Market timing performance of mutual funds is usually evaluated with linear models with dummy variables which allow for the beta coefficient of CAPM to vary across two regimes: bullish and bearish market excess returns. Managers, however, use their predictions of the state of nature to deÞne whether to carry low or high beta portfolios instead of the observed ones. Our approach here is to take this into account and model market timing as a switching regime in a way similar to Hamilton s Markov-switching GNP model. We then build a measure of market timing success and apply it to simulated and real world data.
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Koh, You Beng, and 辜有明. "Bayesian analysis in Markov regime-switching models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521644.
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van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model.published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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Santos, João Ramiro Rodrigues Simões dos. "Credit cycle identification: A Markov-switching application." Master's thesis, NSBE - UNL, 2014. http://hdl.handle.net/10362/11723.
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and EconomicsThis project aims to study credit dynamics and to identify phases of credit cycles at the country level. We applied a Markov-switching (MS) autoregressive framework and a MS with regime-invariant macroeconomic variables to a broad concept of credit, domestic credit. We used a sample of 10 developed countries. MS identification power is assessed using smooth probabilities of low growth states, collected as a by-product of models estimation, against historical databases of crisis events. Conclusions support that MS is accurate in identifying credit cycle phases, and that domestic credit is a good variable for such identification. Additionally, Credit Gap, excess growth over GDP and Broad Money contribute positively to the MS predictions.
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Karadag, Mehmet Ali. "Analysis Of Turkish Stock Market With Markov Regime Switching Volatility Models." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609787/index.pdf.
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In this study, both uni-regime GARCH and Markov Regime Switching GARCH (SW-GARCH) models are examined to analyze Turkish Stock Market volatility. We investigate various models to find out whether SW-GARCH models are an
improvement on the uni-regime GARCH models in terms of modelling and forecasting Turkish Stock Market volatility. As well as using seven statistical loss functions, we apply Superior Predictive Ability (SPA) test of Hansen (2005) and
Reality Check test (RC) of White (2000) to compare forecast performance of various models.
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Hrabovska, Yevheniia <1994>. "A Markov-Switching Model for Bubble Detection in the Stock Market." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10797.
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In this study I propose a model for the behaviour of the real stock market prices which allows for the existence of speculative bubbles. The bubble is assumed to follow a Markov-switching process with explosive and collapsing regimes. Inference on the model is performed by using observations on the deviations of the log prices from fundamentals. The fundamental prices are assumed to be a function of the discounted future dividends. Data used for estimation includes major stock market indices: SP 500, NASDAQ, Euro Stoxx 50 and major US companies.
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Humala, Acuña Alberto. "Markov switching modelling of interest rate pass-through." Thesis, University of Warwick, 2005. http://wrap.warwick.ac.uk/34676/.
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The first paper, "Interest rate pass-through and financial crises: do switching regimes matter? The case of Argentina", analyses the dynamic relationship between a money market (interbank) rate and different short-term lending rates by measuring their passthrough. Neither linear single-equation modelling nor linear multi-equation systems capture efficiently this relationship. Several financial crises alter the speed and degree of response to interbank rate shocks. Hence, a Markov switching VAR model shows the pass-through increases considerably for all market interest rates in a high-volatility scenario. The model identifies correctly the periods in which regime shifts occur, and associates them to financial crises. The second paper, "Modelling interest rate pass-through with endogenous switching regimes in Argentina", extends the scope of the Markov switching modelling by including time-varying transition probabilities. Interest rate spreads are used as leading indicators. The model allows devaluation expectations and country risks, (measured by rate spreads) to signal regime switching. Estimation results suggest that the passthrough tends to overshoot with financial instability, but to decrease if that condition is sufficiently large and long-lived. Likewise, results show a quite heterogeneous credit market, with a highly efficient transmission mechanism in the corporate segment, but considerably less in the consumer segment. The final paper, "Regime switching in interest rate pass-through and dynamic bank modelling with risks", builds a theoretical model of dynamic bank optimisation, which provides rationale to a regime-switching behaviour in the interest rate pass-through. It is shown that a regime-switching interbank rate induces a nonlinear behaviour in lending and deposit rates and (by further introducing interbank-alike regime-switching risk premiums) in the pass-through. Thus, the pass-through process is consistent with a nonlinear behaviour even if there are no asymmetric adjustment costs in the response to interbank rate shocks. An empirical application to France and Germany provide results that support these conclusions.
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Cheng, Jie. "An Extended Class of Markov Switching Autoregressive Models." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508540.
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Dutra, Livia Maria. "Exact Bayesian inference for Markov switching Cox processes." Universidade Federal de Minas Gerais, 2015. http://hdl.handle.net/1843/BUBD-9WGFNQ.
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Statistical modelling of point patterns is an important and common problem in several applications. An important point process, and a generalisation of the Poisson process, is the Cox process, where the intensity function is itself stochastic. We focus on Cox processes in which the intensity function is driven by a nite state space continuous-time Markov chain. We refer to these as Markov switching Cox processes (MSCP). We investigate some probabilistic properties of these processes, three new theorems for these processes are derived and we develop a Bayesian methodology to perform exact inference based on MCMC algorithms. Since the likelihood function is tractable, it facilitates the development of an exact methodology. Simulated studies are presented in order to investigate the efficiency of the methodology on the estimation of MSCP's intensity function and the parameters indexing its law. Finally, an analysis with real data is performed.A modelagem estatística de dados pontuais é um problema importante e comum em diversas aplicações. Um importante processo pontual, e uma generalização do processo de Poisson, é o processo de Cox, em que a sua função intensidade é também estocástica. O presente trabalho se concentra nos processos de Cox em que sua função intensidade é uma cadeia de Markov em tempo contínuo com espaço de estados nito. Estes processos s~ao referidos como processos de Cox com mudanças Markovianas (PCMM). Algumas propriedades probabilísticas desses processos são investigadas, três novos teoremas enunciados e é desenvolvida uma metodologia Bayesiana para realizar inferência exata, baseada em algoritmos MCMC. O desenvolvimento de uma metodologia exata é facilitado, uma vez que a função de verossimilhança é tratável. São apresentados estudos simulados a m de investigar a e ciência da metodologia para estimação da função intensidade dos PCMM's e dos parâmetros relacionados a ela. Ao fim, realiza-se uma análise com dados reais.
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Fan, Qianzhu. "Stochastic heat equations with Markovian switching." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/stochastic-heat-equations-with-markovian-switching(8958d026-671e-4c63-a639-b4a7b120a968).html.
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This thesis consists of three parts. In the first part, we recall some background theory that will be used throughout the thesis. In the second part, we studied the existence and uniqueness of solutions of the stochastic heat equations with Markovian switching. In the third part, we investigate the properties of solutions, such as Feller property, strong Feller property and stability.
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Stockel, Jakob, and Niklas Skantz. "Regime shifts in the Swedish housing market - A Markov-switching model analysis." Thesis, KTH, Fastigheter och byggande, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-190178.
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Problem statement: Accurate and reliable forecasts of trends in the housing market can be useful information for market participants as well as policy makers. This information may be useful to minimize risk related to market uncertainty. Since the burst of the housing bubble in the early 1990s the price level of single-family houses has risen sharply in Sweden. The Swedish housing market has experienced an unusually long period of high growth rates in transaction prices which has opened up for discussions about the risk of another housing bubble. Business and property cycles have shown to contain asymmetries, which linear models are unable to pick up and therefore inappropriate to analyze cycles. Approach: Therefore, this study uses non-linear models which are able to pick up the asymmetries. The estimated models are variations of the Markov-switching regression model, i.e. the Markov-switching autoregressive (MS-AR) model and the Markov-switching dynamic regression (MS-DR) model. Results: Our ndings show that the MS-AR(4) model allowing for varying variance across regimes estimated using the growth rate of FASTPI produce superior forecasts over other MSAR models as well as variations of the MS-DR model. The average expected duration to remain in a positive growth regime is between 6.3 and 7.3 years and the average expected duration to remain in a negative growth regime is between 1.2 to 2.5 years. Conclusion: The next regime shift in the Swedish housing market is projected to occur between 2018 and 2019, counting the contraction period in 2012 as the most recent negative regime. Our ndings support other studies ndings which indicate that the longer the market has remained in one state, the greater is the risk for a regime shift.Problemformulering: Noggranna och tillforlitliga prognoser om utvecklingen pa bostadsmarknaden kan vara anvandbar information for marknadsaktorer samt beslutsfattare. Denna information kan vara anvandbar for att minimera risken relaterad till osakerheten pa marknaden. Sen bostadsbubblan sprack i borjan av 1990-talet har prisnivan for smahus okat kraftigt i Sverige. Den svenska bostadsmarknaden har upplevt en ovanligt lang period av hog tillvaxt i transaktionspriser som har oppnat upp for diskussioner om risken for en ny bostadsbubbla. Konjunkturoch fastighetscykler har visat sig innehalla asymmetrier som linjara modeller inte kan uppfanga och darfor visat sig vara olampliga for att analysera cykler. Tillvagagangssatt: Darfor anvander den har studien icke-linjara modeller som kan uppfanga dessa asymmetrier. De skattade modellerna ar variationer av Hamiltons Markov-switchingmodell, dvs. en autoregressiv Markov-switchingmodell (MS-AR) och en dynamisk Markov-switchingmodell (MS-DR). Resultat: Resultatet visar att MS-AR(4)-modellen som tar hansyn till varierande varians over regimerna estimerad med tillvaxten av FASTPI producerar overlagsna prognoser jamfort med andra MS-AR-modeller samt variationer av MS-DR-modellen. Den genomsnittliga forvantade varaktigheten att benna sig i en positiv regim ar mellan 6,3 och 7,3 ar och den genomsnittliga forvantade varaktigheten att benna sig i en negativ regim ar mellan 1,2 till 2,5 ar. Slutsats: Nasta regimskifte pa den svenska bostadsmarknaden beraknas ske mellan 2018 och 2019, antaget att nedgangen under 2012 ar den senaste negativa regimen. Resultatet stodjer tidigare studier, som tyder pa att ju langre marknaden har varit i ett tillstand, desto storre ar risken for ett regimskifte.
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Spagnolo, Fabio. "Nonlinear error-correction models with regime switching." Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368915.
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Chen, Max. "Business cycles and asset allocation : a Markov switching approach /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7514.
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Zheng, Fei. "Learning and smoothing in switching Markov models with copulas." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC066/document.
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Les modèles de Markov à sauts (appelés JMS pour Jump Markov System) sont utilisés dans de nombreux domaines tels que la poursuite de cibles, le traitement des signaux sismiques et la finance, étant donné leur bonne capacité à modéliser des systèmes non-linéaires et non-gaussiens. De nombreux travaux ont étudié les modèles de Markov linéaires pour lesquels bien souvent la restauration de données est réalisée grâce à des méthodes d’échantillonnage statistique de type Markov Chain Monte-Carlo. Dans cette thèse, nous avons cherché des solutions alternatives aux méthodes MCMC et proposons deux originalités principales. La première a consisté à proposer un algorithme de restauration non supervisée d’un JMS particulier appelé « modèle de Markov couple à sauts conditionnellement gaussiens » (noté CGPMSM). Cet algorithme combine une méthode d’estimation des paramètres basée sur le principe Espérance-Maximisation (EM) et une méthode efficace pour lisser les données à partir des paramètres estimés. La deuxième originalité a consisté à étendre un CGPMSM spécifique appelé CGOMSM par l’introduction des copules. Ce modèle, appelé GCOMSM, permet de considérer des distributions plus générales que les distributions gaussiennes tout en conservant des méthodes de restauration optimales et rapides. Nous avons équipé ce modèle d’une méthode d’estimation des paramètres appelée GICE-LS, combinant le principe de la méthode d’estimation conditionnelle itérative généralisée et le principe des moindre-carrés linéaires. Toutes les méthodes sont évaluées sur des données simulées. En particulier, les performances de GCOMSM sont discutées au regard de modèles de Markov non-linéaires et non-gaussiens tels que la volatilité stochastique, très utilisée dans le domaine de la financeSwitching Markov Models, also called Jump Markov Systems (JMS), are widely used in many fields such as target tracking, seismic signal processing and finance, since they can approach non-Gaussian non-linear systems. A considerable amount of related work studies linear JMS in which data restoration is achieved by Markov Chain Monte-Carlo (MCMC) methods. In this dissertation, we try to find alternative restoration solution for JMS to MCMC methods. The main contribution of our work includes two parts. Firstly, an algorithm of unsupervised restoration for a recent linear JMS known as Conditionally Gaussian Pairwise Markov Switching Model (CGPMSM) is proposed. This algorithm combines a parameter estimation method named Double EM, which is based on the Expectation-Maximization (EM) principle applied twice sequentially, and an efficient approach for smoothing with estimated parameters. Secondly, we extend a specific sub-model of CGPMSM known as Conditionally Gaussian Observed Markov Switching Model (CGOMSM) to a more general one, named Generalized Conditionally Observed Markov Switching Model (GCOMSM) by introducing copulas. Comparing to CGOMSM, the proposed GCOMSM adopts inherently more flexible distributions and non-linear structures, while optimal restoration is feasible. In addition, an identification method called GICE-LS based on the Generalized Iterative Conditional Estimation (GICE) and the Least-Square (LS) principles is proposed for GCOMSM to approximate any non-Gaussian non-linear systems from their sample data set. All proposed methods are tested by simulation. Moreover, the performance of GCOMSM is discussed by application on other generable non-Gaussian non-linear Markov models, for example, on stochastic volatility models which are of great importance in finance
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Elidrissi, Imane <1991>. "applying Markov Chain switching model to Systemic Risk measures." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6943.
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Chen, Ping. "Asset-liability management under regime-switching models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43223928.
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Zhu, Jinxia. "Ruin theory under Markovian regime-switching risk models." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/b40203980.
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Sajjad, Rasoul. "Value-at-risk in a Markov regime-switching GARCH framework." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438117.
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Moreira, Rafael Henrique Rodrigues. "Modelos multivariados com Markov Switching aplicados à política monetária brasileira." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/12/12140/tde-11072007-140949/.
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RESUMO No início de 1995 foi adotado no Brasil o Plano Real, tendo como um dos seus tripés de sustentação a busca pelo combate ao processo inflacionário crônico brasileiro que já se estendia por um longo período. Assim, a política monetária passou a ter um papel importante na determinação das variáveis macroeconômicas. Este trabalho busca analisar uma regra de política monetária que capte as variações ocorridas em todo o período do Plano Real, se estendendo até meados de 2005, bem como se deram as relações entre as variáveis econômicas neste período. A especificação proposta consiste na estimação de modelos não-lineares distintos dependendo do estado da economia (em crise ou fora de crise). Utilizamos um modelo com chaveamento Markoviano para a dinâmica da taxa de juros nominal onde a determinação de períodos de crise é feita por uma variável nãoobservada. Além disso, procuramos adotar dois algoritmos distintos de estimação, Expectation-Maximization (EM) e Monte Carlo Markov Chain (MCMC), concluindo que a análise para ambos é bastante próxima, sendo identificados os mesmos períodos entre regimes. Finalmente, motivamos a estimação através de modelos econômicos teóricos cujas dinâmicas são compatíveis com uma regra de fixação de juros não-linear, avaliando os padrões de resposta a impulso condicionados ao estado da economia (regimes de estabilidade e crise econômica).ABSTRACT In the beginning of 1995, continuing the process of inflation combat, the monetary policy should have been an important role in the determinacy of macroeconomics variables. This work has a target analyzing a monetary rule that reflects the occurred variations in every Real Plan?s period. The specification proposed by the authors consists in an estimation of two independent nonlinear models for different states of the nature (crises or not crises). Here we estimate a model where the dynamic of the nominal interest rate follows a Markov Switching process and the regimes are unobservable variables. In addition, we try adopting two different algorithms to estimation; Expectation-Maximization (EM) and Monte Carlo Markov Chain (MCMC), concluded that the results are very similar. Finally, we motivate the estimations analyzing models where the theoretical dynamics of the economy are compatible with a nonlinear interest rate rule, analyzing the impulse response conditioned to state of economy (regimes of crises or not crises).
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Valladares, Frederico Estrella Carneiro. "Real exchange rate misalignments : an application of Markov switching models." reponame:Repositório Institucional do FGV, 2002. http://hdl.handle.net/10438/7951.
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Fardel, Victor <1990>. "Markov-Switching copula models for dependence analysis in time series." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4815.
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Favotto, Alessandro <1989>. "Miglioramento del Marginal Expected Shortfall con un modello Markov Switching." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5168.
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Il seguente lavoro analizza il Marginal Expected Shortfall elaborato da diversi autori per la misurazione del rischio sistemico. Tale misura esprime il contributo al rischio sistemico complessivo che una singola impresa finanziaria trasmette al sistema finanziario, attraverso il valore atteso dei rendimenti equity dell’impresa condizionati all’evento sistemico, ossia il rendimento di mercato al di sotto di una determinata soglia, fissata esogenamente e spesso pari al suo VaR. In particolare la seguente analisi si propone di ricavare in maniera endogena il valore di tale soglia applicando ai dati un modello a cambiamento di regime (Markov Switching model) così da poter ottenere un MES legato al regime definito “di crisi”. Calcolata tale misura per tutto il campione di imprese finanziarie si è composto un ranking basato sul periodo 2006-2007 che mostra quali di queste fossero le più rischiose dal punto di vista sistemico nel periodo pre-crisi e che effettivamente poi sono incorse in gravi difficoltà. I risultati così ottenuti sono poi confrontati con quelli di altri autori che hanno utilizzato il metodo basato sulla soglia esogena.
The following work examines the Marginal Expected Shortfall developed by various researchers to measure systemic risk. This measure indicates the contribution to the overall systemic risk of an individual financial firm, by computing the expected equity return loss conditional to a systemic event, i.e. market return below a given threshold; the threshold is exogenous and often equal to firm’s q%-VaR. In particular the analysis proposes to get in a endogenous way the value of that threshold through the application of a Markov Switching Model, in order to obtain a MES related to the so-called “crisis” regime. After that, a ranking of the financial institutions included in the sample based on the results of the 2006-2007 period shows the systemically riskiest firms; this ranking largely coincides with the firms that actually fared worst during the 2007-2008 crisis. A comparison is then made with the results of other authors who applied instead an exogenous threshold.
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Emery, Martin Banking & Finance Australian School of Business UNSW. "Studies into global asset allocation strategies using the markov-switching model." Publisher:University of New South Wales. Banking & Finance, 2008. http://handle.unsw.edu.au/1959.4/43098.
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This thesis presents the potential opportunities of global asset allocation and the possible enhancement of these opportunities from using a Markov Switching Model. The thesis extends upon previous conditional asset pricing studies in global asset allocation, such as those done by Ilamnen (1995), Harvey, Solnik and Zhou (1992) and Bilson (1993), where expected future returns are forecast based on conditional variables. The finding of these studies, and many others, are combined with the works on Markov Switching models and market segmentation theories to create a uniform structure for analysing regime switching properties in currencies, international equities and international bond markets. This thesis is segregated into 4 major sections. The chapters 1-4 develop a unified framework that is used in the analysis of markets. The chapters 5-7 are focused on currencies, international equities and international bonds. For each market a model is constructed that is based upon the structure proposed by Frankel and Froot (1988). In this model the market is segmented into two groups ?? value based investors and momentum based investors. To replicate this structure, a two regime Markov Switching model is used, where one regime is constructed as a value regime and the second is constructed as a momentum regime. These models are then compared to linear versions of the models, to see whether there is any additional benefit to the application of regime switching methods. In conjunction with testing the potential benefits of the Markov Regime Switching process, this study also investigates the very nature, or characteristics of regime switching in the international markets. This is undertaken though some alternate models and enhancements to see whether there is any predictability, or characterisations can be made of the switching process. To ensure a comprehensive analysis, several analytical methods have been used, including extensive econometric modelling, statistical analysis of forecasts and portfolio back testing. A number of conclusions can be drawn from the results. Firstly it appears that there is substantial evidence of regime switching in international markets, such as that shown in a Frankel-Froot framework. This in turn has major implication for the understanding of the way in which international markets function, and further the empirical evidence supports many of the anecdotal observations of market based participants. Secondly, there appears to be a strong level of economic relevance to the modelling. The models are shown to generate a theoretical economic profit, which shows that the international markets are only semi efficient. Further, forecasts generated from the Markov Switching models outperform the linear counterparts in economic significance in portfolio tests. However, for both equities and bonds, the general accuracy of the forecast tends to be inferior to the linear counterparts. Finally, the nature of regime switching is investigated in detail, particularly in reference to 3 potential drivers ?? greed, fear and success. The evidence shows that these can help explain the characteristics of regime switching, as in some cases potentially adding economic value. However, it seems that success is more important than a broader economic environment.
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Chen, Ping, and 陈平. "Asset-liability management under regime-switching models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43223928.
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Schwendener, Alvin. "Regime-Switching Modell für die Schätzung von Marktdynamiken." St. Gallen, 2006. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01654086001/$FILE/01654086001.pdf.
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Zhu, Jinxia, and 朱金霞. "Ruin theory under Markovian regime-switching risk models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40203980.
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Browne, Perry James. "The filtering of linear dynamic models with switching coefficients." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295975.
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Cheung, Ka-chun. "Optimal asset allocation problems under the discrete-time regime-switching model." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31311234.
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Castoe, Minna, and Teo Raspudic. "Option Pricing Under the Markov-switching Framework Defined by Three States." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48808.
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An exact solution for the valuation of the options of the European style can be obtained using the Black-Scholes model. However, some of the limitations of the Black-Scholes model are said to be inconsistent such as the constant volatility of the stock price which is not the case in real life. In this thesis, the Black-Scholes model is extended to a model where the volatility is fully stochastic and changing over time, modelled by Markov chain with three states - high, medium and low. Under this model, we price options of both types, European and American, using Monte Carlo simulation.
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Cavicchioli, Maddalena <1985>. "Essays on Markov Switching models with applications in economics and finance." Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4602.
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In questa tesi studiamo alcuni problemi legati a modelli Markov Switching (MS) e alle loro applicazioni in Economia e Finanza. Lo scopo del nostro studio è proporre soluzioni per la selezione del modello e per la stima di serie storiche multivariate soggette a cambiamenti di regime. Nel primo Capitolo presentiamo la letteratura che tratta di sistemi dinamici per modellare serie storiche con cambiamenti di regime. Nel secondo Capitolo studiamo il problema della determinazione del numero di regimi nell’ambito di modelli MS-VARMA e proponiamo alcuni metodi per la scelta del modello basati sulla funzione di autocovarianza e sulla rappresentazione stabile del sistema. Questi metodi sono poi applicati all’analisi del ciclo economico. Nel Capitolo 3 introduciamo modelli a cambiamento di regime per la volatilità di dati finanziari e proponiamo un metodo unificato per la stima di modelli MS-GARCH e modelli a volatilità stocastica con MS (teorema di dualità). Nel quarto Capitolo esploriamo altre questioni che riguardano i modelli MS come la stima e la loro rappresentazione spettrale. Riguardo al problema della stima, otteniamo semplici formule matriciali per la stima di massima verosimiglianza dei parametri per modelli MS-VAR e MS-VAR con effetti ARCH. Questo permette di determinare in maniera esplicita la matrice di varianza-covarianza degli stimatori, e quindi offre una possibilità concreta per l’uso dei test statistici classici. Riguardo al secondo aspetto, studiamo varie proprietà della funzione di densità spettrale di modelli MS-VAR e otteniamo espressioni in forma chiusa per la densità spettrale. La tesi è completata da diversi esercizi di simulazione e applicazioni a dati macroeconomici e finanziari.In this thesis we discuss problems emerging in the application of Markov Switching (MS) models both in Economics and Finance. The aim of the study is to propose solutions for model selection and estimation of multiple time series subject to regime shifts. In Chapter 1 we review the literature about dynamic systems for modeling time series with changes in regimes. In the second Chapter we investigate the problem of determining the number of regimes in MS-VARMA models and describe methods for model selection based on the autocovariance function and on stable representation of the system. Application to business cycle analysis is conducted. In Chapter 3 we introduce MS models for volatility of financial data and propose a unified framework for estimating MS-GARCH and MS-Stochastic Volatility models (duality result). In the fourth Chapter we explore other questions concerning with MS models as estimation and spectral representation. With regards to the first, we obtain simple matrix formulae for maximum likelihood estimates of parameters in the class of MS-VAR and conditional heteroskedastic models. This allows us to determine explicitly the asymptotic variance-covariance matrix of the estimators, thus giving a concrete possibility for the use of classical testing procedure. Concerning the second, we study the properties of spectral density function for MS-VAR models and derive close-form formulae for the spectral density. Several simulation exercises and applications to macroeconomic and financial data complete the work.
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Frühwirth-Schnatter, Sylvia. "MCMC Estimation of Classical and Dynamic Switching and Mixture Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/698/1/document.pdf.
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In the present paper we discuss Bayesian estimation of a very general model class where the distribution of the observations is assumed to depend on a latent mixture or switching variable taking values in a discrete state space. This model class covers e.g. finite mixture modelling, Markov switching autoregressive modelling and dynamic linear models with switching. Joint Bayesian estimation of all latent variables, model parameters and parameters determining the probability law of the switching variable is carried out by a new Markov Chain Monte Carlo method called permutation sampling. Estimation of switching and mixture models is known to be faced with identifiability problems as switching and mixture are identifiable only up to permutations of the indices of the states. For a Bayesian analysis the posterior has to be constrained in such a way that identifiablity constraints are fulfilled. The permutation sampler is designed to sample efficiently from the constrained posterior, by first sampling from the unconstrained posterior - which often can be done in a convenient multimove manner - and then by applying a suitable permutation, if the identifiability constraint is violated. We present simple conditions on the prior which ensure that this method is a valid Markov Chain Monte Carlo method (that is invariance, irreducibility and aperiodicity hold). Three case studies are presented, including finite mixture modelling of fetal lamb data, Markov switching Autoregressive modelling of the U.S. quarterly real GDP data, and modelling the U .S./U.K. real exchange rate by a dynamic linear model with Markov switching heteroscedasticity. (author's abstract)Series: Forschungsberichte / Institut für Statistik
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Fitzpatrick, Matthew Anthony. "Multi-regime models involving Markov chains." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/14530.
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In this work, we explore the theory and applications of various multi-regime models involving Markov chains. Markov chains are an elegant way to model path-dependent data. We study a series of problems with non-homogeneous data and the various ways that Markov chains come into play. Non-homogeneous data can be modelled using multi-regime models, which apply a distinct set of parameters to distinct population sub-groups, referred to as regimes. Such models essentially allow for a practitioner to understand the nature (and in some cases the existence) of particular regimes within the data without the need to split the population into assumed sub-groups. For example, the problem of modelling business outcomes in different economic states without explicitly using economic variables. Different regimes can apply to an entire population at different times they can apply to different subsections of the population over the whole observed time. Markov chains are involved via the estimation procedure or within models for the observed data. In our first two problems, we utilise the properties of Markov chains to discover and establish efficiencies in the estimation algorithms. In our third problem, we are analysing mixtures of Markov chains. We prove that the log-likelihood ratio test statistic for the test between 1 and 2 mixture components diverges to infinity in probability. In our fourth problem, we look at a simple case, where each Markov chain component has two states, one of which is absorbing, we derive the exact limiting distribution of the log-likelihood ratio test statistic. Although this work is largely focussed on addressing the theoretical issues of each problem, the motivation behind each of the problems studied comes from real datasets, which possess levels of complexity that are insufficiently described through more standard procedures.
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Awirothananon, Thatphong. "Information Criterion and Joint Determination of the Numbers of Regimes and Variables in Markov Switching Model: Simulation and Empirical Application." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/367009.
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This study has three purposes. The first one is to examine the performance of four information criteria in the context of joint determination of the numbers of regimes and variables in Markov switching model (hereafter called the MS model). These criteria are Akaike (1974) information criterion (hereafter called AIC), Schwarz (1978) information criterion (hereafter called SIC), HQC (Hannan & Quinn 1979), and Markov switching criterion: MSC (Smith, Naik & Tsai 2006). The second purpose is to investigate further whether the numbers of regimes and variables in aggregate time series are similar to those in individual time series. Third, to verify the simulation results from the second objective, this study applies the MS model to both aggregate and individual time series in reality.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith Business School
Griffith Business School
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Darmanjian, Shalom. "Switching Hidden-Markov Model and hardware implementation for a Brain-Machine Interface." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0009426.
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Hayashi, Miwa. "Hidden Markov Models for analysis of pilot instrument scanning and attention switching." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28912.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 132-134).
(cont.) high workload. The results of another flight simulation experiment demonstrated how the pilots' attention budgeting among these tasks estimated by HMM analysis, combined with the pilots' eye-movement statistical results, could enhance a cockpit display format study. The experiments demonstrated what additional insights can be obtained by incorporating HMM analysis into the analysis of pilots' eye movements.
Pilots' eye movements provide researchers rich information about the pilots' cognitive process during flight. Indeed, many researchers have included pilots' eye-movement measures in their flight simulator experiments. Currently, however, due to the lack of a reasonable model of pilots' scanning process, most researchers must rely on simple statistical analysis of eye-movement data, such as mean fixation durations on each instrument. The problem is that such statistical analyses often involve time-averaging operations, and so the information regarding the sequence of instrument scanning, the richest part of the data that reflects the pilot's moment-to-moment thought and attention processes, often has been lost or not fully utilized. The thesis proposes a new analysis tool based on Hidden Markov Models (HMMs). This analysis exploits pilots' instrument-crosschecking eye movements within an instrument group related to the vertical-, horizontal-, or airspeed-tracking task. From the pilots' eye-movement data, the HMM estimates the most likely sequence of underlying tracking tasks that the pilot attended to. HMM analysis is especially useful when some instruments overlap among multiple tracking tasks (e.g., the attitude indicator overlaps among all three tracking tasks) because it can utilize the sequential information from the instrument scanning to compute the likelihood of each of the possible tracking tasks. The actual pilot eye-movements data collected during ILS approach simulation experiments indicated that some experienced pilots may attend to more than three tasks during flight, with the fourth one being a monitoring task, while some inexperienced pilots may attend to only two, dropping one of the tracking tasks probably due to
by Miwa Hayashi.
Ph.D.
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Kandemir, Kocaaslan Ozge. "An empirical investigation of the U.S. GDP growth : a Markov switching approach." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/3250/.
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This thesis is composed of three separate yet related empirical studies. In Chapter 2, we empirically investigate the effects of inflation uncertainty on output growth for the U.S. economy using both monthly and quarterly data over 1960-2009. Employing a Markov regime switching approach, we show that inflation uncertainty obtained from a Markov regime switching GARCH model exerts a negative and regime dependent impact on growth. We show that the negative impact of inflation uncertainty on growth is almost 2 times higher during the low growth regime than that during the high growth regime. We verify the robustness of our findings using quarterly data. In Chapter 3, we empirically examine whether there are asymmetries in the real effects of monetary policy shocks across business cycle and whether financial depth plays an important role in dampening the effects of monetary policy shocks on output growth using quarterly U.S. data over the period 1981:QI--2009:QII. Applying an instrumental variables estimation in Markov regime switching methodology, we document that the impact of monetary policy changes on growth is stronger during recessions. We also find that financial development is very prominent in dampening the real effects of monetary policy shocks especially during the periods of recession. In Chapter 4, we empirically search for the causal link between energy consumption and economic growth employing a Markov switching Granger causality analysis. We carry out our investigation using quarterly U.S. real GDP and total energy consumption data over the period 1975:QI--2009:QIV. We find that there are changes in the causal relation between energy consumption and economic growth. Our results show that energy consumption has predictive content for real economic activity. The causality running from energy consumption to output growth seems to be strongly apparent only during the periods of recession and energy crisis. We also reveal that output growth has predictive power for energy consumption and this power evidently arises during the periods of expansion.
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Dridi, Mohamed Azzeddine <1985>. "Markov-switching correlation models for contagion analysis in commodity and stock markets." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4833.
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Bulfone, Giacomo <1996>. "CDS spreads determinants and COVID-19 pandemic: A Bayesian Markov-switching model." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17624.
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A deep understanding of the CDS spreads determinants is crucial for both policy makers interested in preserving the stability of the financial system and of financial insiders interested in managing credit and financial risks. The literature is mainly focused on the pre-subprime crisis, and either consider linear models with a large number of covariates or nonlinear models, such as regime Markov switching models, with a small number of explanatory variables and two regimes only. The aim of this thesis is to investigate the determinants of the European iTraxx corporate index considering a large set of explanatory variables within a Markov switching model framework. The focus is on the post 2007-2009 crisis and more precisely on the period from October 2011 to April 2020 which includes the recent COVID-19 pandemic events. The dataset includes financial and economic variables usually employed in CDS spreads analysis and some new explanatory variables such as the Baltic Dry Index as a proxy for the economic activity and lagged values of the iTraxx index. The analysis is conducted in two steps. First a multivariate regression model is estimated via OLS method on a rolling window to provide some evidence of variation in the parameters. Second, stability tests are also used to detect structural breaks in the linear relationship and to motivate the use of nonlinear models. Finally, the in-sample and out-of-sample analysis of the forecasting performances of different Markov switching models has been performed. The empirical results suggest that: more than 2 regimes should be used after the COVID-19 pandemic to model CDS spreads; the impact of the covariates varies across regimes; and that the economic activity index has some predictive power for changes in the iTraxx index.
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Cheung, Ka-chun, and 張家俊. "Optimal asset allocation problems under the discrete-time regime-switching model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31311234.
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Schweizer, Andreas. "Analysis and optimisation of stable matching in combined input and output queued switches." Western Australian Telecommunications Research Institute, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0078.
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Output queues in network switches are known to provide a suitable architecture for scheduling disciplines that need to provide quality of service (QoS) guarantees. However, todays memory technology is incapable of meeting the speed requirements. Combined input and output queued (CIOQ) switches have emerged as one alternative to address the problem of memory speed. When a switch of this architecture uses a stable matching algorithm to transfer packets across the switch fabric, an output queued (OQ) switch can be mimicked exactly with a speedup of only two. The use of a stable matching algorithm typically requires complex and time-consuming calculations to ensure the behaviour of an OQ switch is maintained. Stable matching algorithms are well studied in the area in which they originally appeared. However, little is presently known on how the stable matching algorithm performs in CIOQ switches and how key parameters are affected by switch size, traffic type and traffic load. Knowledge of how these conditions affect performance is essential to judge the practicability of an architecture and to provide useful information on how to design such switches. Until now, CIOQ switches were likely to be dismissed due to the high complexity of the stable matching algorithm when applied to other applications. However, the characteristics of a stable matching algorithm in a CIOQ switch have not been thoroughly analysed. The principal goal of this thesis is to identify the conditions the stable matching algorithm encounters in a CIOQ switch under realistic operational scenarios. This thesis provides accurate mathematical models based on Markov chains to predict the value of key parameters that affect the complexity and runtime of a stable matching algorithm in CIOQ switches. The applicability of the models is then backed up by simulations. The results of the analysis quantify critical operational parameters, such as the size and number of preference lists and runtime complexity. These provide detailed insights into switch behaviour and useful information for switch designs. Major conclusions to be drawn from this analysis include that the average values of the key parameters of the stable matching algorithm are feasibly small and do not strongly correlate with switch size, which is contrary to the behaviour of the stable matching ii algorithm in its original application. Furthermore, although these parameters have wide theoretical ranges, the mean values and standard deviations are found to be small under operational conditions. The results also suggest that the implementation becomes very versatile as the completion time of the stable matching algorithm is not strongly correlated to the network traffic type; that is, the runtime is minimally affected by the nature of the traffic.
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Hurth, Tobias. "Invariant densities for dynamical systems with random switching." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52274.
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We studied invariant measures and invariant densities for dynamical systems with random switching (switching systems, in short). These switching systems can be described by a two-component Markov process whose first component is a stochastic process on a finite-dimensional smooth manifold and whose second component is a stochastic process on a finite collection of smooth vector fields that are defined on the manifold. We identified sufficient conditions for uniqueness and absolute continuity of the invariant measure associated to this Markov process. These conditions consist of a Hoermander-type hypoellipticity condition and a recurrence condition. In the case where the manifold is the real line or a subset of the real line, we studied regularity properties of the invariant densities of absolutely continuous invariant measures. We showed that invariant densities are smooth away from critical points of the vector fields. Assuming in addition that the vector fields are analytic, we derived the asymptotically dominant term for invariant densities at critical points.
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Bergstrom, Peter D. Jr. "Markov chain models for all-optical shared memory packet switches." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15361.
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Giroud, Xavier. "A Markov-Switching Equilibrium Correction Model for Intraday Futures and Stock Index Returns." St. Gallen, 2004. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/99630345001/$FILE/99630345001.pdf.
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Spagnolo, Nicola. "Nonlinearity testing, model selection and forecasting in the prescence of Markov regime switching." Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368914.
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Fairbrother, Mark. "Markov-Switching models and resultant equity implied volatility surfaces: a South African application." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/10450.
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Includes bibliographical references.Standard Geometric Brownian Motion is the stock model underlying Black-Scholes famous option pricing formula. There are however numerous problems with this stock model as certain features do not follow some empirical stylised facts we see from the observation of actual asset prices. In particular, the constant parameter idea behind Geometric Brownian Motion is flawed. It is argued that information flow dictates stock price movements and information is a function macro-economic regimes shifts. As such, we propose an alternative model, one in which the parameters in the Standard Geometric Brownian Motion change according to an underlying Hidden Markov Process. This new model, termed a Markov-Switching model, is presented in extensive detail. Parameter Estimation methods, Simulation Methods and Option Pricing Theory are explored. Summary algorithms are presented so that this dissertation may be used as a good reference guide for those wishing to apply Markov-Switching Models. The model is tested by fitting the model on South African data and using the discussed option theory to create various implied volatility surfaces. The surfaces produced appear to obey some of the empirical observations and theoretical ideas around expected implied volatility surfaces, indicating that the Markov-Switching model has some value for option pricing.
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Mazviona, Batsirai Winmore. "Volatility forecasting using Double-Markov switching GARCH models under skewed Student-t distribution." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/12344.
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Includes bibliographical references.This thesis focuses on forecasting the volatility of daily returns using a double Markov switching GARCH model with a skewed Student-t error distribution. The model was applied to individual shares obtained from the Johannesburg Stock Exchange (JSE). The Bayesian approach which uses Markov Chain Monte Carlo was used to estimate the unknown parameters in the model. The double Markov switching GARCH model was compared to a GARCH(1,1) model. Value at risk thresholds and violations ratios were computed leading to the ranking of the GARCH and double Markov switching GARCH models. The results showed that double Markov switching GARCH model performs similarly to the GARCH model based on the ranking technique employed in this thesis.
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Ye, Lingyun. "MARKOV REGIME-SWITCHING MODELS." 2012. http://hdl.handle.net/10222/15126.
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A regime-switching model is a time-series model in which parameters change values
according to the regime at present time. While regime-switching models have been very
popular in applied work, there is a lack of literature for simulation studies. New methods
based on regime-switching models are often proposed with neither a proof of convergence
nor simulations to demonstrate their basic properties. In this thesis, a detailed simulation
study of regime-switching models is conducted. A strategy to generate initial search values
in the parameter estimation of regime-switching models is proposed. It is shown that this
method can dramatically reduce the number of restarts of the optimizer. Even in 3-regime
models (with 15 unknown parameters), parameters can be estimated reasonably well with
only 5 restarts.
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Liao, Li-na, and 廖麗娜. "Double Markov Switching GARCH Models." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37885659092168474199.
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碩士逢甲大學
統計與精算所
94
In this paper we consider a double Markov switching GARCH model with fat-tailed error distribution for analyzing asymmetric effects on mean and volatility in financial markets. The characteristic of our model is that a regime variable from one state to another is an unobserved variable which is assumed to be a first-order Markov process. We use Markov chain Monte Carlo methods to make statistical inference. In simulation study, we set sensitivity analysis for transition probabilities and then compare these results. As to empirical study, we apply for our DMS-GARCH model with an exogenous variable to capture the asymmetric mean and volatility spillover effects. We consider six daily stock market indices including the CAC 40 of France, ADX 30 of Germany, Milan MIBTel Index of Italy, FTSE 100 of United Kingdom, the Toronto SE 300 of Canada, and Nikkei 225 Index of Japan and employ the daily return on US Standard and Poor''s 500 Index (S&P 500) as an exogenous variable. The data cover the period from 4 January 1999 to 28 April 2006. We also forecast VaR and use two hypothesis-testing methods for evaluating the accuracy of VaR models. These results tell us that our DMS-GARCH model with an exogenous variable performs much better than other considered models.
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Lin, Jia-tien, and 林家田. "Option Pricing with Markov Switching VAR Process." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/37353688316936288372.
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碩士國立高雄第一科技大學
風險管理與保險所
97
The purpose of this study is to investigate options which derivation from multivariate asset prices with Markov switching VAR(p). We employ the VARMA(p,q) model of Wang(2009) to obtain the option prices under risk neutral probability measure Q, Duan(1995), as q=0. However, the system changes in asset prices, there is a nonlinear adjustment, therefore joining the Markov-switching such that the model more in line with reality conditions. Carr and Madan(1998) point to give the characteristic function for concerning distribution, then we are able to get the option price. We use the probability function and derive the characteristic function of MS(2)-VAR(1) model. Therefore, the option considering nonlinearity is obtained.
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Huang, Jiun-Yan, and 黃俊諺. "Optimal Sales Promotion Strategy - Markov Switching Approach." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/77043446389566517164.
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碩士國立交通大學
經營管理研究所
96
This paper investigates the optimal sales promotional strategy for the fiercely competitive FMCG (Fast Moving Consuming Goods) industry. We propose a Markov Switching Autoregressive model that incorporates AR(1) retailing demand process to capture nonlinear structure among promotional budget allocation, evaluation of promotion performance, and optimal promotion frequency within a given time span. The past promotion investment is evaluated first by comparing the changes in promotional budget allocation. We then apply Markov switching feedback rules to figure out the proper length of equilibrium state with and/or without promotion. Finally, effective decision rules on magnitude, duration, and frequency of promotional strategy are induced. We apply three product categories with 39 months time-series data from a multinational packaged food company. The result shows that most past decisions on promotional budget allocation are non-optimal – most promotion investments were either extended too long or allocated too low in stimulating sales. Implications for the brand- or category- manager in removing those non-optimal promotional policies are suggested.