Markov-switching models : empirical applications using classical and Bayesian inference

In this thesis, we present three empirical applications on finance and macroeconomics. The general modeling framework in all chapters is based on extensions of the Markov-switching model. And the statistical methodology is divided into two distinct areas; Classical and Bayesian inference.1 In the first one, we test for the presence of duration dependence in the Brazilian business cycle. The main results indicated that as the recession ages, the probability of a transition into an expansion increases (positive duration dependence in recessions). On the other hand, as the expansions ages, the probability of a transition into a recession decreases (negative duration dependence in expansions). In the second paper, we extend the research concerned with the evaluation of alternative volatility modeling and forecasting methods for Bitcoin log-returns. The in-sample estimates suggest evidence of long memory in the data series. When performing one-day ahead Value-at-Risk (VaR), our results outperform all standard single-regime GARCH models considered in the study. Finally, in the third paper, we capture different regimes in Bitcoin volatility returns and test the mean-reversion hypothesis for multi-period returns. In general, we found evidence of mean-aversion for different holding returns. We also confirmed this result for alternative specifications and also carrying the analysis for sub-sample periods

Mitigating the choice of the duration in DDMS models through a parametric link

Um dos hiperparâmetros mais importantes em modelos de mudança de regime Markoviana e duração dependente (DDMS) é a duração dos regimes. Uma vez que não existe nenhum procedimento para estimar ou testar uma dada duração para um determinado conjunto de dados, geralmente uma escolha ad hoc deve ser heuristicamente justificada. Esta é uma tarefa tipicamente difícil e é provavelmente o ponto mais delicado da modelagem via modelos DDMS, sendo motivo de críticas, dificultando a sua aplicação. Neste trabalho, propomos e estudamos uma metodologia que atenua a escolha da duração nos modelos DDMS quando o foco é a previsão. A proposta é a utilização de um link paramétrico em vez do habitual link fixo ao calcular as probabilidades de transição. Como resultado, o modelo torna-se mais flexível e escolhas do parâmetro de duração potencialmente incorretas (ou seja, erro de especificação) é compensada pelo parâmetro no link, produzindo probabilidades de transição próximas das verdadeiras e, ao mesmo tempo, melhorando a precisão da previsão no contexto de erro de especificação. A abordagem proposta é avaliada através de simulações de Monte Carlo e uma aplicação com dados reais é apresentada. Os resultados indicam que o modelo de link paramétrico supera o modelo logit, tanto em termos de previsões dentro da amostra quanto fora da amostra, tanto para valores de duração corretamente especificados como para valores de duração mal especificados.One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. This is typically a difficult task and is likely the most delicate point of the modeling procedure, allowing for criticism and ultimately hindering the use of DDMS models. In this work, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The idea is to use a parametric link instead of the usual fixed link when calculating transition probabilities. As a result, the model becomes more flexible and any potentially incorrect duration choice (i.e., misspecification) is compensated by the parameter in the link, yielding a likelihood and transition probabilities very close to the true ones while, at the same time, improving forecasting accuracy under misspecification. We evaluate the proposed approach in Monte Carlo simulations and using real data applications. Results indicate that the parametric link model outperforms the benchmark logit model, both in terms of in-sample estimation and out-ofsample forecasting, for both well-specified and misspecified duration values

Evidências de Bull e Bear Market no índice Bovespa: uma aplicação de modelos de regime markoviano e duration dependence

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Sócio Econômico, Programa de Pós-Graduação em Economia, Florianópolis, 2013.O objetivo deste trabalho é identificar tendências de alta e de baixa no índice Bovespa através de modelos de mudança de regime markoviano que incorporam dependência de duração. Conforme evidenciado pela literatura,os resultados mostraram um regime de retorno positivo e baixa volatilidade e outro com alta volatilidade e retorno negativo. Nestes modelos a probabilidade de transição não é só função do regime atual como também do número de períodos em que o processo se encontra em determinado estado. A parametrização proposta revelou que a probabilidade de troca de regime diminui com a persistência do mercado de alta e de baixa. A análise das probabilidades suavizadas destaca que as especificações captaram os principais episódios de instabilidade na bolsa brasileira.Por fim, os modelos propostos são usados na construção de uma estratégia de investimento. Abstract : This study proposes to identify Bull and Bear Market for the Brazilianstock market using a markov switching model that incorporates durationdependence. Following the existing literature, the model sorts returnsinto a high return stable state and a low return volatile state, whichare labeled as Bull and Bear Market. In these models the transitionprobabilities are functions of both the inferred current state and alsothe number of periods that the process has been in that state. The parameterizationshowed that the probability of switching out of the statesdeclines with duration in that state. The smoothed probabilities highlightedthe major instabilities phases at Brazilian stock market. Finally,the proposed models are used in order to develop an investment strategy

Mitigating the choice of the duration in DDMS models through a parametric link

One of the most important hyper-parameters in duration-dependent Markov-switching (DDMS) models is the duration of the hidden states. Because there is currently no procedure for estimating this duration or testing whether a given duration is appropriate for a given data set, an ad hoc duration choice must be heuristically justified. This is typically a difficult task and is likely the most delicate point of the modeling procedure, allowing for criticism and ultimately hindering the use of DDMS models. In this paper, we propose and examine a methodology that mitigates the choice of duration in DDMS models when forecasting is the goal. The idea is to use a parametric link instead of the usual fixed link when calculating transition probabilities. As a result, the model becomes more flexible and any potentially incorrect duration choice (i.e., misspecification) is compensated by the parameter in the link, yielding a likelihood and transition probabilities very close to the true ones while, at the same time, improving forecasting accuracy under misspecification. We evaluate the proposed approach in Monte Carlo simulations and using real data applications. Results indicate that the parametric link model outperforms the benchmark logit model, both in terms of in-sample estimation and out-of-sample forecasting, for both well-specified and misspecified duration values

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ