A novel ensemble method for retrieving properties of warm cloud in 3-D using ground-based scanning radar and zenith radiances

We present a novel method for retrieving high-resolution, three-dimensional (3-D) nonprecipitating cloud fields in both overcast and broken-cloud situations. The method uses scanning cloud radar and multiwavelength zenith radiances to obtain gridded 3-D liquid water content (LWC) and effective radius (re) and 2-D column mean droplet number concentration (Nd). By using an adaption of the ensemble Kalman filter, radiances are used to constrain the optical properties of the clouds using a forward model that employs full 3-D radiative transfer while also providing full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from a challenging cumulus cloud field produced by a large-eddy simulation snapshot. Uncertainty due to measurement error in overhead clouds is estimated at 20% in LWC and 6% in re, but the true error can be greater due to uncertainties in the assumed droplet size distribution and radiative transfer. Over the entire domain, LWC and re are retrieved with average error 0.05–0.08 g m-3 and ~2 μm, respectively, depending on the number of radiance channels used. The method is then evaluated using real data from the Atmospheric Radiation Measurement program Mobile Facility at the Azores. Two case studies are considered, one stratocumulus and one cumulus. Where available, the liquid water path retrieved directly above the observation site was found to be in good agreement with independent values obtained from microwave radiometer measurements, with an error of 20 g m-2.

Satellite-supported flood forecasting in river networks: a real case study

Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of stand-alone EO-based operational flood forecasting.

Assessing the performance of dynamical trajectory estimates

Estimating trajectories and parameters of dynamical systems from observations is a problem frequently encountered in various branches of science; geophysicists for example refer to this problem as data assimilation. Unlike as in estimation problems with exchangeable observations, in data assimilation the observations cannot easily be divided into separate sets for estimation and validation; this creates serious problems, since simply using the same observations for estimation and validation might result in overly optimistic performance assessments. To circumvent this problem, a result is presented which allows us to estimate this optimism, thus allowing for a more realistic performance assessment in data assimilation. The presented approach becomes particularly simple for data assimilation methods employing a linear error feedback (such as synchronization schemes, nudging, incremental 3DVAR and 4DVar, and various Kalman filter approaches). Numerical examples considering a high gain observer confirm the theory.

A joint state and parameter estimation scheme for nonlinear dynamical systems

We present a novel algorithm for concurrent model state and parameter estimation in nonlinear dynamical systems. The new scheme uses ideas from three dimensional variational data assimilation (3D-Var) and the extended Kalman filter (EKF) together with the technique of state augmentation to estimate uncertain model parameters alongside the model state variables in a sequential filtering system. The method is relatively simple to implement and computationally inexpensive to run for large systems with relatively few parameters. We demonstrate the efficacy of the method via a series of identical twin experiments with three simple dynamical system models. The scheme is able to recover the parameter values to a good level of accuracy, even when observational data are noisy. We expect this new technique to be easily transferable to much larger models.

Are mortgage lenders guilty of the housing bubble? A UK perspective

Existing theoretical models of house prices and credit rely on continuous rationality of consumers, an assumption that has been frequently questioned in recent years. Meanwhile, empirical investigations of the relationship between prices and credit are often based on national-level data, which is then tested for structural breaks and asymmetric responses, usually with subsamples. Earlier author argues that local markets are structurally different from one another and so the coefficients of any estimated housing market model should vary from region to region. We investigate differences in the price–credit relationship for 12 regions of the UK. Markov-switching is introduced to capture asymmetric market behaviours and turning points. Results show that credit abundance had a large impact on house prices in Greater London and nearby regions alongside a strong positive feedback effect from past house price movements. This impact is even larger in Greater London and the South East of England when house prices are falling, which are the only instances where the credit effect is more prominent than the positive feedback effect. A strong positive feedback effect from past lending activity is also present in the loan dynamics. Furthermore, bubble probabilities extracted using a discrete Kalman filter neatly capture market turning points.

Effect Of Different Hydrological Model Structures On The Assimilation Of Distributed Uncertain Observations

The reliable evaluation of the flood forecasting is a crucial problem for assessing flood risk and consequent damages. Different hydrological models (distributed, semi-distributed or lumped) have been proposed in order to deal with this issue. The choice of the proper model structure has been investigated by many authors and it is one of the main sources of uncertainty for a correct evaluation of the outflow hydrograph. In addition, the recent increasing of data availability makes possible to update hydrological models as response of real-time observations. For these reasons, the aim of this work it is to evaluate the effect of different structure of a semi-distributed hydrological model in the assimilation of distributed uncertain discharge observations. The study was applied to the Bacchiglione catchment, located in Italy. The first methodological step was to divide the basin in different sub-basins according to topographic characteristics. Secondly, two different structures of the semi-distributed hydrological model were implemented in order to estimate the outflow hydrograph. Then, synthetic observations of uncertain value of discharge were generated, as a function of the observed and simulated value of flow at the basin outlet, and assimilated in the semi-distributed models using a Kalman Filter. Finally, different spatial patterns of sensors location were assumed to update the model state as response of the uncertain discharge observations. The results of this work pointed out that, overall, the assimilation of uncertain observations can improve the hydrologic model performance. In particular, it was found that the model structure is an important factor, of difficult characterization, since can induce different forecasts in terms of outflow discharge. This study is partly supported by the FP7 EU Project WeSenseIt.

Emerging markets yield curve dynamics

This work extendes Diebold, Li and Yueís (2006) about global yield curve and proposes to extend the study by including emerging countries. The perception of emerging market su§ers ináuence of external factors or global factors, is the main argument of this work. We expect to obtain stylized facts.that obey similar pattern found by those authors. The results indicate the existence of global level and global slope factors. These factors represent an important fraction in the bond yield determination and show a decreasing trend of the global level factor low ináuence of global slope factor in these countries when they are compared with developed countries. Keywords: Kalman Filter, Emerging Markets, Yield Curve, and Bond.

Estrutura a termo da taxa de juros no Brasil e previsibilidade de ciclos econômicos

O objetivo deste trabalho é caracterizar a Curva de Juros Mensal para o Brasil através de três fatores, comparando dois tipos de métodos de estimação: Através da Representação em Espaço de Estado é possível estimá-lo por dois Métodos: Filtro de Kalman e Mínimos Quadrados em Dois Passos. Os fatores têm sua dinâmica representada por um Modelo Autorregressivo Vetorial, VAR(1), e para o segundo método de estimação, atribui-se uma estrutura para a Variância Condicional. Para a comparação dos métodos empregados, propõe-se uma forma alternativa de compará-los: através de Processos de Markov que possam modelar conjuntamente o Fator de Inclinação da Curva de Juros, obtido pelos métodos empregados neste trabalho, e uma váriavel proxy para Desempenho Econômico, fornecendo alguma medida de previsão para os Ciclos Econômicos.

Economic cycles and term structure: application to Brazil

The objective of this work is to describe the behavior of the economic cycle in Brazil through Markov processes which can jointly model the slope factor of the yield curve, obtained by the estimation of the Nelson-Siegel Dynamic Model by the Kalman filter and a proxy variable for economic performance, providing some forecasting measure for economic cycles

A taxa natural de juros no Brasil

Neste artigo, foi estimada a taxa natural de juros para a economia brasileira entre o final de 2001 e segundo trimestre de 2010 com base em dois modelos, sendo o primeiro deles o proposto por Laubach e Williams e o segundo proposto por Mesónnier e Renne, que trata de uma versão alterada do primeiro, que segundo os autores perimite uma estimação mais transparente e robusta. Em ambos os modelos, a taxa natural de juros é estimada em conjunto com o produto potencial, através de filtro de Kalman, no formato de um modelo Espaço de Estado. As estimativas provenientes dos dois modelos não apresentam diferenças relevantes, o que gera maior confiabilidade nos resultados obtidos. Para o período de maior interesse deste estudo (pós-2005), dada a existência de outras análises para período anterior, as estimativas mostram que a taxa natural de juros está em queda na economia brasileira desde 2006. A mensuração da taxa natural de juros, adicionalmente, possibilitou que fosse feita uma avaliação sobre a condução da política monetária implementada pelo Banco Central brasileiro nos últimos anos através do conceito de hiato de juros. Em linhas gerais, a análise mostrou um Banco Central mais conservador entre o final de 2001 e 2005, e mais próximo da neutralidade desde então. Esta conclusão difere da apontada por outros estudos, especialmente para o primeiro período.

Essays on regulatory risk issues

Most studies around that try to verify the existence of regulatory risk look mainly at developed countries. Looking at regulatory risk in emerging market regulated sectors is no less important to improving and increasing investment in those markets. This thesis comprises three papers comprising regulatory risk issues. In the first Paper I check whether CAPM betas capture information on regulatory risk by using a two-step procedure. In the first step I run Kalman Filter estimates and then use these estimated betas as inputs in a Random-Effect panel data model. I find evidence of regulatory risk in electricity, telecommunications and all regulated sectors in Brazil. I find further evidence that regulatory changes in the country either do not reduce or even increase the betas of the regulated sectors, going in the opposite direction to the buffering hypothesis as proposed by Peltzman (1976). In the second Paper I check whether CAPM alphas say something about regulatory risk. I investigate a methodology similar to those used by some regulatory agencies around the world like the Brazilian Electricity Regulatory Agency (ANEEL) that incorporates a specific component of regulatory risk in setting tariffs for regulated sectors. I find using SUR estimates negative and significant alphas for all regulated sectors especially the electricity and telecommunications sectors. This runs in the face of theory that predicts alphas that are not statistically different from zero. I suspect that the significant alphas are related to misspecifications in the traditional CAPM that fail to capture true regulatory risk factors. On of the reasons is that CAPM does not consider factors that are proven to have significant effects on asset pricing, such as Fama and French size (ME) and price-to-book value (ME/BE). In the third Paper, I use two additional factors as controls in the estimation of alphas, and the results are similar. Nevertheless, I find evidence that the negative alphas may be the result of the regulated sectors premiums associated with the three Fama and French factors, particularly the market risk premium. When taken together, ME and ME/BE regulated sectors diminish the statistical significance of market factors premiums, especially for the electricity sector. This show how important is the inclusion of these factors, which unfortunately is scarce in emerging markets like Brazil.

Estimating Brazilian Monthly GDP: a State-Space Approach

This paper has several original contributions. The first is to employ a superior interpolation method that enables to estimate, nowcast and forecast monthly Brazilian GDP for 1980-2012 in an integrated way; see Bernanke, Gertler and Watson (1997, Brookings Papers on Economic Activity). Second, along the spirit of Mariano and Murasawa (2003, Journal of Applied Econometrics), we propose and test a myriad of interpolation models and interpolation auxiliary series- all coincident with GDP from a business-cycle dating point of view. Based on these results, we finally choose the most appropriate monthly indicator for Brazilian GDP. Third, this monthly GDP estimate is compared to an economic activity indicator widely used by practitioners in Brazil - the Brazilian Economic Activity Index - (IBC-Br). We found that the our monthly GDP tracks economic activity better than IBC-Br. This happens by construction, since our state-space approach imposes the restriction (discipline) that our monthly estimate must add up to the quarterly observed series in any given quarter, which may not hold regarding IBC-Br. Moreover, our method has the advantage to be easily implemented: it only requires conditioning on two observed series for estimation, while estimating IBC-Br requires the availability of hundreds of monthly series. Third, in a nowcasting and forecasting exercise, we illustrate the advantages of our integrated approach. Finally, we compare the chronology of recessions of our monthly estimate with those done elsewhere.

Estratégia de trading utilizando o modelo dinâmico de Nelson-Siegel

Esta pesquisa busca testar a eficácia de uma estratégia de arbitragem de taxas de juros no Brasil baseada na utilização do modelo de Nelson-Siegel dinâmico aplicada à curva de contratos futuros de taxa de juros de 1 dia da BM&FBovespa para o período compreendido entre 02 de janeiro de 2008 e 03 de dezembro de 2012. O trabalho adapta para o mercado brasileiro o modelo original proposto por Nelson e Siegel (1987), e algumas de suas extensões e interpretações, chegando a um dos modelos propostos por Diebold, Rudebusch e Aruoba (2006), no qual estimam os parâmetros do modelo de Nelson-Siegel em uma única etapa, colocando-o em formato de espaço de estados e utilizando o Filtro de Kalman para realizar a previsão dos fatores, assumindo que o comportamento dos mesmos é um VAR de ordem 1. Desta maneira, o modelo possui a vantagem de que todos os parâmetros são estimados simultaneamente, e os autores mostraram que este modelo possui bom poder preditivo. Os resultados da estratégia adotada foram animadores quando considerados para negociação apenas os 7 primeiros vencimentos abertos para negociação na BM&FBovespa, que possuem maturidade máxima próxima a 1 ano.

Previsão de inflação utilizando modelos de séries temporais

Este trabalho compara modelos de séries temporais para a projeção de curto prazo da inflação brasileira, medida pelo Índice de Preços ao Consumidor Amplo (IPCA). Foram considerados modelos SARIMA de Box e Jenkins e modelos estruturais em espaço de estados, estimados pelo filtro de Kalman. Para a estimação dos modelos, foi utilizada a série do IPCA na base mensal, de março de 2003 a março de 2012. Os modelos SARIMA foram estimados no EVIEWS e os modelos estruturais no STAMP. Para a validação dos modelos para fora da amostra, foram consideradas as previsões 1 passo à frente para o período de abril de 2012 a março de 2013, tomando como base os principais critérios de avaliação de capacidade preditiva propostos na literatura. A conclusão do trabalho é que, embora o modelo estrutural permita, decompor a série em componentes com interpretação direta e estudá-las separadamente, além de incorporar variáveis explicativas de forma simples, o desempenho do modelo SARIMA para prever a inflação brasileira foi superior, no período e horizonte considerados. Outro importante aspecto positivo é que a implementação de um modelo SARIMA é imediata, e previsões a partir dele são obtidas de forma simples e direta.

Estimação de prêmio de risco de startup

Por definição as empresas startups estão expostas a mais riscos e vulnerabilidades que empresas maduras e já estabelecidas no mercado. O objetivo do presente estudo é identificar, aplicar e testar uma possível metodologia para calcular prêmio de risco adicional para startups. Para tanto este trabalho desenvolve um estudo de caso no qual a conhecida metodologia para cálculo de prêmio de risco de tamanho da Morningstar é aplicada a uma startup americana. A aderência da metodologia proposta neste estudo é testada pela metodologia do filtro de Kalman, que calcula o prêmio de risco por tamanho variando ao longo do tempo. Os resultados encontrados são similares em ambas as metodologias. De forma que é possível concluir que a metodologia da Morningstar, quando aplicada para calcular prêmio por tamanho variante ao longo do tempo é robusta.