23 research outputs found
Assessment of regional bestâfit probability density function of annual maximum rainfall using CFSR precipitation data
The upper Cross River basin (UCRB) fits a true description of a data scarce watershed in respect of
climatic data. This paper seeks to determine the bestâfit probability density function (PDF) of annual
maximum rainfall for the UCRB using the Climate Forecast System Reanalysis (CFSR) precipitation data.
Also, to evaluate the performance of the Intergovernmental Panel on Climate Change (IPCC) Coupled
Model Interâcomparison Project (CMIP3) Fourth Assessment Report (AR4) Global Circulation Models
(GCMs) in simulating the monthly precipitation in the UCRB considering 1979â2014 data. For the
determination of the bestâfit PDF, the models under review included the generalized extreme value
(GEV), normal, gamma, Weibull and logânormal (LN) distributions. Twentyâfour weather station datasets
were obtained and subjected to frequency distribution analysis on per station basis, and subsequently
fitted to the respective PDFs. Also, simulated monthly precipitation data obtained from 16 AR4 GCMs,
for weather station p6191, were subjected to frequency distribution analysis. The results showed the
percentages of bestâfit to worstâfit PDFs, considering the total number of stations, as follows: 54.17%,
45.83%, 37.50%, 45.83%, and 50%/50%. These percentages corresponded to GEV, Weibull, gamma,
gamma, and LN/normal, respectively. The comparison of the predicted and observed values using the
Chiâsquare goodnessâofâfit test revealed that the GEV PDF is the bestâfit model for the UCRB. The
correlation coefficient values further corroborated the correctness of the test. The PDF of the observed
data (weather station p6191) and the simulations of the 16 GCMs computed using monthly rainfall
datasets were compared using a mean square error (MSE) dependent skill score. The result from this
study suggested that the CGCM3.1 (T47) and MRIâCGCM2.3.2 provide the best representations of
precipitation, considering about 36 years trend for station p6191. The results have no influence on how
well the models perform in other geographical locations
Models for Heavy-tailed Asset Returns
Many of the concepts in theoretical and empirical finance developed over the past decades â including the classical portfolio theory, the Black-Scholes-Merton option pricing model or the RiskMetrics variance-covariance approach to VaR â rest upon the assumption that asset returns follow a normal distribution. But this assumption is not justified by empirical data! Rather, the empirical observations exhibit excess kurtosis, more colloquially known as fat
tails or heavy tails. This chapter is intended as a guide to heavy-tailed models. We first describe the historically oldest heavy-tailed model â the stable laws. Next, we briefly characterize their recent lighter-tailed generalizations, the so-called truncated and tempered stable distributions. Then we study the class of generalized hyperbolic laws, which â like tempered stable distributions â can be classified somewhere between infinite variance stable laws and the Gaussian distribution. Finally, we provide numerical examples
Change Point Analysis based on Empirical Characteristic Functions
62G20, 62E20, 60F17, Empirical characteristic function, Change point analysis,
Nonparametric two-sample estimation of location and scale parameters from empirical characteristic functions
A homogeneity test for bivariate random variables
Homogeneity, Bivariate populations, Empirical characteristic function, Quadratic PowellâSabin splines, Quadratic Taylor approximation, Bootstrap,
Estimating the codifference function of linear time series models with infinite variance
ARMA, Infinite variance, Codifference, Empirical characteristic function,