A Survival Analysis of Bank Failures in Turkey: Incorporating Unobserved Heterogeneity in Continuous Time Parametric Models

2015 ◽  
Vol 26 (95) ◽  
pp. 33
Author(s):  
Didem Pekkurnaz ◽  
Zeynep Elitas
Keyword(s):  
Survival Analysis ◽  
Continuous Time ◽  
Unobserved Heterogeneity ◽  
Parametric Models ◽  
Bank Failures

scholarly journals Bank-Specific and Macroeconomic Determinants of Profitability: A Revisit of Pakistani Banking Sector under Dynamic Panel Data Approach

2020 ◽  
Vol 8 (3) ◽  
pp. 42 ◽  
Author(s):  
Habib-ur Rahman ◽  
Muhammad Waqas Yousaf ◽  
Nageena Tabassum
Keyword(s):  
Panel Data ◽  
Interest Rates ◽  
Economies Of Scale ◽  
Banking Sector ◽  
Unobserved Heterogeneity ◽  
Dynamic Panel Data ◽  
Capital Adequacy ◽  
Bank Failures ◽  
Dynamic Panel ◽  
Macroeconomic Determinants

This study aims to examine the effect of the bank-specific and macroeconomic determinants of profitability for the banking sector of Pakistan. To incorporate the issues of endogeneity, unobserved heterogeneity, and profit persistence, we apply a generalised method of moments (GMM) technique under the Arellano–Bond framework to a panel of Pakistani banks that covers the period 2003–2017. The results of a dynamic panel data approach reveal that capital adequacy accelerates the profitability of the banking sector in Pakistan. Capital adequacy helps the financial system to absorb any negative shock by reducing the number of bank failures and losses. Conversely, our empirical investigation reveals that the liquidity ratio, business mix indicators, interest rates, and industrial production deteriorates the bank profitability. Liquidity risks enhance the probability of default risks and transmit into the unpaid loans and hence the lower return. Our empirical evidence further reveals that Pakistani banks are not getting any benefit of the economies of scale in terms of financial performance.

scholarly journals Survival Analysis on Pedigrees: A Marked Point Process Model

2010 ◽  
Vol 40 (1) ◽  
pp. 35-64
Author(s):  
Angus S. Macdonald
Keyword(s):  
Survival Analysis ◽  
Process Model ◽  
Point Processes ◽  
Marked Point ◽  
Age At Onset ◽  
Parametric Models ◽  
Marked Point Processes ◽  
Inherited Disorders ◽  
Partial Model ◽  
Special Cases

AbstractRegulation of insurers' use of genetic information means actuaries are interested in age-at-onset of genetic disorders. Arjas & Haara (1984) suggested marked point processes (MPPs) as useful models for life history data with complex covariates. Age-at-onset distributions (or equivalently, hazard rates) in respect of inherited disorders are often estimated from pedigrees, which are life histories with unusually complex covariates, as well as strong dependencies induced by shared genes. Since Elston (1973) parametric models have often been used, conditioning the likelihood on known genotypes. However, a genotype identii ed by a presymptomatic genetic test is a form of internal covariate (Kalbfleisch & Prentice, 2002). We propose a very general MPP model of a pedigree, including presymptomatic genetic testing, (‘the full model’) and show under what circumstances the partial model leading to Elston's likelihood is valid. In practice, pedigrees are often ascertained retrospectively. Many such events can be modelled by augmenting the natural filtration of the MPP. We show that, except in simple special cases, the partial model is no longer valid, and the resulting likelihoods appear to be intractable. In particular, ascertainment interacts even with independent censoring so that likelihoods no longer factorize. For one simple special case — studies of sibships — we generalise a classical result to age-at-onset data. We conclude that the study of genetic conditions with variable age at onset gains insights from the underlying principles of survival analysis in their modern form, but that great care is needed in translating epidemiological studies into actuarial models.

On some waiting time problems

2000 ◽  
Vol 37 (03) ◽  
pp. 756-764 ◽  
Author(s):  
Valeri T. Stefanov
Keyword(s):  
Survival Analysis ◽  
Finite Number ◽  
Markov Processes ◽  
Waiting Time ◽  
Generating Functions ◽  
Continuous Time ◽  
Waiting Times ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Semi Markov Processes

A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

scholarly journals Unobserved Heterogeneity in Determinants of Under-five Mortality in Nigeria: Frailty Modeling in Survival Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 565-587 ◽  
Author(s):  
Adeniyi Francis Fagbamigbe ◽  
Rotimi Felix Afolabi ◽  
Kofoworola Yussuf Alade ◽  
Ayo Stephen Adebowale ◽  
Bidemi Oyindamola Yusuf
Keyword(s):  
Survival Analysis ◽  
Unobserved Heterogeneity ◽  
Under Five

scholarly journals A Non-Parametric Approach for Survival Analysis of Component-Based Software

2020 ◽  
Vol 5 (2) ◽  
pp. 309-318
Author(s):  
Sandeep Chopra ◽  
Lata Nautiyal ◽  
Preeti Malik ◽  
Mangey Ram ◽  
Mahesh K. Sharma
Keyword(s):  
Survival Analysis ◽  
Failure Rate ◽  
Growth Models ◽  
Reliability Estimation ◽  
Parametric Models ◽  
Model Parameters ◽  
Parametric Approach ◽  
Estimation Model ◽  
Failure Data ◽  
Non Parametric

Reliability of a software or system is the probability of system to perform its functions adequately for the stated time period under specific environment conditions. In case of component-based software development reliability estimation is a crucial factor. Existing reliability estimation model falls into two broad categories parametric and non-parametric models. Parametric models approximate the model parameters based on the assumptions of fundamental distributions. Non-parametric models enable parameter estimation of the software reliability growth models without any assumptions. We have proposed a novel non-parametric approach for survival analysis of components. Failure data is collected based on which we have calculated failure rate and reliability of the software. Failure rate increases with the time whereas reliability decreases with the time.

On some waiting time problems

2000 ◽  
Vol 37 (3) ◽  
pp. 756-764 ◽  
Author(s):  
Valeri T. Stefanov
Keyword(s):  
Survival Analysis ◽  
Finite Number ◽  
Markov Processes ◽  
Waiting Time ◽  
Generating Functions ◽  
Continuous Time ◽  
Waiting Times ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Semi Markov Processes

A unifying technology is introduced for finding explicit closed form expressions for joint moment generating functions of various random quantities associated with some waiting time problems. Sooner and later waiting times are covered for general discrete- and continuous-time models. The models are either Markov chains or semi-Markov processes with a finite number of states. Waiting times associated with generalized phase-type distributions, that are of interest in survival analysis and other areas, are also covered.

Unobserved Heterogeneity in Determinants of Under-five Mortality in Nigeria: Frailty Modeling in Survival Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 565-588
Author(s):  
Adeniyi Francis Fagbamigbe ◽  
Rotimi Felix Afolabi ◽  
Alade Yussuf Kofoworola ◽  
Alade Yussuf KOFOWOROLA ◽  
Adebowale AYO STEPHEN ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Unobserved Heterogeneity ◽  
Under Five

Integrating Semiparametric and Parametric Models in Survival Analysis of Bridge Element Deterioration

2013 ◽  
Vol 19 (2) ◽  
pp. 176-185 ◽  
Author(s):  
Ying Nan Yang ◽  
Mohan M. Kumaraswamy ◽  
Hoat Joen Pam ◽  
Hong Ming Xie
Keyword(s):  
Survival Analysis ◽  
Parametric Models

A nonparametric model for spot price dynamics and pricing of futures contracts in electricity markets

2014 ◽  
Vol 18 (5) ◽  
Author(s):  
Katja Ignatieva
Keyword(s):  
Electricity Markets ◽  
Continuous Time ◽  
Regime Switching ◽  
Diffusion Processes ◽  
Market Price ◽  
Parametric Models ◽  
Futures Contracts ◽  
Spot Price ◽  
Nonparametric Model ◽  
Spot Prices

AbstractThis paper deals with the estimation of continuous time diffusion processes describing the dynamics of electricity spot prices. Different parametric models have been proposed in the literature, each attempting to capture empirical characteristics and stylized facts of the electricity market like the spiky behavior of the spot prices. Although jump-diffusion and regime-switching models perform reasonably well, there is always a trade-off between model parsimony and adequacy. The results in the literature indicate that none of the models seem to consistently outperform its counterparts. This paper avoids making parametric assumption about the drift and the diffusion coefficient functions of the underlying electricity spot prices, and estimates these functions together with the market price of risk in a nonparametric way. The latter allows us to price futures contracts written on electricity spots. Using electricity spot prices and futures data from the regional electricity markets in Australia, we show that besides offering a convenient way of estimating the continuous-time models for electricity spot prices, our nonparametric estimation procedure performs well in- and out-of-sample when dealing with pricing of future contracts.

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