Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter

Ahn, Kwang Woo; Chan, Kung‐Sik

doi:10.1016/j.csda.2013.07.038

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…The conditions under which this least squares estimator with the embedded UKF filter is consistent and asymptotically normal are provided in Ahn and Chan (2014).…”

Section: A Pricing Cds Contractsmentioning

confidence: 99%

Market perception of sovereign credit risk in the euro area during the financial crisis

Camba-Méndez

Serwa

2016

The North American Journal of Economics and Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe study market perception of sovereign credit risk in the euro area during the financial crisis. In our analysis we use a parsimonious CDS pricing model to estimate the probability of default (PD) and the loss given default (LGD) as perceived by financial markets. We find that separate identification of PD and LGD appears empirically tractable for a number of euro area countries. In our empirical results the estimated LGDs perceived by financial markets stay comfortably below 40% in most of the samples. We also find that macroeconomic and institutional developments were only weakly correlated with the market perception of sovereign credit risk, whereas financial contagion appears to have exerted a non-negligible effect.JEL-Classification: C11, C32, G01, G12, G15. Keywords: sovereign credit risk, CDS spreads, euro area, probability of default, loss given default.ECB Working Paper 1710, August 2014 1 Executive summaryThe aim of this paper is to study market perception of sovereign credit risk in the euro area during the financial crisis. Understanding the factors that influence changes in market perception of sovereign credit risk is a question of special interest for economists, investors, and financial regulators. Similarly to a number of recent studies, we also monitor the market perception of sovereign risk by using information derived from CDS spreads.In studying market perceptions of sovereign credit risk, one of the aims of this paper is to estimate measures of the probability of default (PD) as perceived by financial markets and the loss given default (LGD), also as perceived by financial markets, embedded in the CDS spreads. Earlier studies of the euro area sovereign debt crisis usually employed the CDS spreads alone and thus were not able to separately identify changes in the valuation of risk stemming from either increases of potential losses or a growing likelihood of default.Yet, in the pricing framework of 'fractional recovery of face value', the factors governing the dynamics of the PD and LGD play very distinct roles in the derivation of the price of the CDS contract, thus suggesting that PD and LGD can indeed, at least in theory, be separately identified. However, what is conceptually true may not always hold empirically, and to date the separate identification of PD and LGD remains challenging.The second aim of this paper is to shed some light on the key drivers of the market perception of ...

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“…The conditions under which this least squares estimator with the embedded UKF filter is consistent and asymptotically normal are provided in Ahn and Chan (2014).…”

Section: A Pricing Cds Contractsmentioning

confidence: 99%

Market perception of sovereign credit risk in the euro area during the financial crisis

Camba-Méndez

Serwa

2016

The North American Journal of Economics and Finance

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“…The mean j  in six-dimensions is given by respectively. Concretely, for better implementation [8], 1,000 particles were drawn for the bootstrap PF with resampling after each measurement, and 100 Monte Carlo runs were applied.…”

Section: Comparison and Analysis Resultsmentioning

confidence: 99%

“…The most nonlinear method used to fuse data from sensors, and to therefore estimate the vehicle state, is the extended Kalman filter (EKF). However, owing to its non-negligible approximation error with strong nonlinear models, the authors in [8] introduced the unscented Kalman filter (UKF) as an alternative method using a deterministic sampling scheme consisting of the "sigma points." Furthermore, to overcome the computational complexity caused by the UKF, a square-root UKF (SR-UKF) was presented in [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In practice, the UKF uses several "sigma points" to calculate the mean and covariance of random variables, and these sigma points propagate through a true nonlinear system. In the SR-UPF, the number of sigma points should be much smaller than the number of particles for a better implementation [8]. Moreover, the PF at its foundation is able to address extremely nonlinear models with any type of noise distributions.…”

Section: System Descriptionmentioning

confidence: 99%

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A Hybrid Approach-based Sparse Gaussian Kernel Model for Vehicle State Estimation during the Free and Complete GPS Outages

Havyarimana¹,

Xiao²,

Wang³

2016

ETRI J

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To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.

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“…Unscented Kalman filter [43][44][45][46] is an efficient derivative free filtering algorithm for computing approximate solutions to discrete-time nonlinear optimal filtering problems. It has been successfully applied to numerous practical problems and it has been shown to outperform the EKF in many cases.…”

Section: Introductionmentioning

confidence: 99%

New state estimator of nonlinear discrete‐time systems

Aljuwaiser

Badr

Hassan

2016

Optim Control Appl Methods

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Summary In this paper, a new technique is developed to estimate the states of both deterministic and stochastic discrete‐time nonlinear systems. The stochastic system can be uncertain. The corrupting input and/or the output noise vectors may be either Gaussian or non‐Gaussian zero‐mean sequences. The proposed filter is based on pole placement technique, in which a set of constraints are imposed of the estimated outputs. The stability of the estimator is rigorously analyzed. An extension to the developed estimator is proposed to deal with constrained estimation problems. To illustrate the effectiveness and simplicity of the developed technique, illustrative examples are presented. Simulations results show that, for the deterministic case, the developed procedure leads to better results when compared with High‐gain observer, Thau's filter, and the Iterative Regularized Least Square estimator. On the other hand, for the stochastic case, the proposed estimator is superior to the extended Kalman filter and the iterative constrained estimator (ICE) with non‐ideal situations where the statistics of the noise signals and/or the system parameters are unknown, or the noise signals are non‐Gaussian. Copyright © 2016 John Wiley & Sons, Ltd.

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Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter

Cited by 14 publications

References 19 publications

Market perception of sovereign credit risk in the euro area during the financial crisis

Market perception of sovereign credit risk in the euro area during the financial crisis

A Hybrid Approach-based Sparse Gaussian Kernel Model for Vehicle State Estimation during the Free and Complete GPS Outages

New state estimator of nonlinear discrete‐time systems

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