scholarly journals Beyond the Pearson Correlation: Heavy-Tailed Risks, Weighted Gini Correlations, and a Gini-Type Weighted Insurance Pricing Model

2016 ◽  
Author(s):  
Edward Furman ◽  
Ricardas Zitikis
Keyword(s):  
Pearson Correlation ◽  
Pricing Model ◽  
Insurance Pricing ◽  
Heavy Tailed

scholarly journals BEYOND THE PEARSON CORRELATION: HEAVY-TAILED RISKS, WEIGHTED GINI CORRELATIONS, AND A GINI-TYPE WEIGHTED INSURANCE PRICING MODEL

2017 ◽  
Vol 47 (3) ◽  
pp. 919-942 ◽  
Author(s):  
Edward Furman ◽  
Ričardas Zitikis
Keyword(s):  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Insurance Pricing ◽  
Asset Pricing Model ◽  
Research Areas ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Theoretical Considerations

AbstractGini-type correlation coefficients have become increasingly important in a variety of research areas, including economics, insurance and finance, where modelling with heavy-tailed distributions is of pivotal importance. In such situations, naturally, the classical Pearson correlation coefficient is of little use. On the other hand, it has been observed that when light-tailed situations are of interest, and hence when both the Gini-type and Pearson correlation coefficients are well defined and finite, these coefficients are related and sometimes even coincide. In general, understanding how these correlation coefficients are related has been an illusive task. In this paper, we put forward arguments that establish such a connection via certain regression-type equations. This, in turn, allows us to introduce a Gini-type weighted insurance pricing model that works in heavy-tailed situations and thus provides a natural alternative to the classical capital asset pricing model. We illustrate our theoretical considerations using several bivariate distributions, such as elliptical and those with heavy-tailed Pareto margins.

Agricultural Insurance Pricing Model

2014 ◽  
Vol 9 (2) ◽  
pp. 1-12
Author(s):  
Chao-Hui Yeh ◽  
◽  
Ti-Ling Wang ◽  
Keyword(s):  
Pricing Model ◽  
Agricultural Insurance ◽  
Insurance Pricing

scholarly journals An Optimal Investment Strategy and Multiperiod Deposit Insurance Pricing Model for Commercial Banks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Grant E. Muller
Keyword(s):  
Optimal Control ◽  
Deposit Insurance ◽  
Optimal Investment ◽  
Investment Strategy ◽  
Pricing Model ◽  
Insurance Pricing ◽  
Optimal Investment Strategy ◽  
Asset Value ◽  
Asset Portfolio ◽  
Future Date

We employ the method of stochastic optimal control to derive the optimal investment strategy for maximizing an expected exponential utility of a commercial bank’s capital at some future date T>0. In addition, we derive a multiperiod deposit insurance (DI) pricing model that incorporates the explicit solution of the optimal control problem and an asset value reset rule comparable to the typical practice of insolvency resolution by insuring agencies. By way of numerical simulations, we study the effects of changes in the DI coverage horizon, the risk associated with the asset portfolio of the bank, and the bank’s initial leverage level (deposit-to-asset ratio) on the DI premium while the optimal investment strategy is followed.

scholarly journals A Doubling Method for the Generalized Lambda Distribution

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Todd C. Headrick ◽  
Mohan D. Pant
Keyword(s):  
Monte Carlo ◽  
Pearson Correlation ◽  
Relative Bias ◽  
Simulation Studies ◽  
Generalized Lambda Distribution ◽  
New Family ◽  
Heavy Tailed Distributions ◽  
Moment Estimates ◽  
Doubling Method ◽  
Heavy Tailed

This paper introduces a new family of generalized lambda distributions (GLDs) based on a method of doubling symmetric GLDs. The focus of the development is in the context of L-moments and L-correlation theory. As such, included is the development of a procedure for specifying double GLDs with controlled degrees of L-skew, L-kurtosis, and L-correlations. The procedure can be applied in a variety of settings such as modeling events and Monte Carlo or simulation studies. Further, it is demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are substantially superior to conventional product-moment estimates of skew, kurtosis, and Pearson correlation in terms of both relative bias and efficiency when heavy tailed distributions are of concern.

scholarly journals Evaluating the Effectiveness of Asset Pricing Model before, during and after Financial Crisis 2008: Evidence from Karachi Stock Exchange

2017 ◽  
Vol 7 (1) ◽  
pp. 177
Author(s):  
Waqar Ul Hassan ◽  
Zeeshan Hasnain ◽  
Shahbaz Hussain
Keyword(s):  
Financial Crisis ◽  
Stock Returns ◽  
Stock Exchange ◽  
Pearson Correlation ◽  
Random Effect ◽  
Random Effect Model ◽  
Poor Performance ◽  
Pricing Model ◽  
Crisis Period ◽  
Effect Model

The Study aims to explore the strength of arbitrage pricing model (APT) for determining stock returns of Karachi stock exchange (KSE) across three distinct and structured periods; before financial crisis period (2006-07), during financial crisis period (2008) and after financial crisis period (2009-10). The Study adopted descriptive statistics, Pearson correlation, linear regression, Random effect model for interpretation and execution of data. 253 financial and non-financial listed companies on KSE for the period of (2006-10) are considered as sample firms. Results of regression analysis indicated that models selected for the present study showed poor performance for measuring KSE returns. Independent variables showed significant behavior for measuring KSE returns in pre-financial crisis period; no statistical relationship for measuring KSE returns in during financial crisis period; insignificant nature for measuring KSE returns the post-financial crisis period. The Study has provided understandings about arbitrage theory applicability and financial crisis - 2008 impacts on KSE. 

The pricing of loan insurance based on the Gram-Charlier option model

2018 ◽  
Vol 8 (4) ◽  
pp. 425-440 ◽  
Author(s):  
Yaojie Zhang ◽  
Yu Wei ◽  
Benshan Shi
Keyword(s):  
Iterative Method ◽  
Empirical Evidence ◽  
Closed Form Solution ◽  
Asset Returns ◽  
Insurance Premium ◽  
Pricing Model ◽  
Practical Application ◽  
Content Type ◽  
Insurance Pricing ◽  
Skewness And Kurtosis

PurposeThe purpose of this paper is to develop a loan insurance pricing model allowing for the skewness and kurtosis existing in underlying asset returns.Design/methodology/approachUsing the theory of Gram-Charlier option, the authors first derive a closed-form solution of the Gram-Charlier pricing model. To address the difficulties in implementing the pricing model, the authors subsequently propose an iterative method to estimate skewness and kurtosis in practical application, which shows a relatively fast convergence rate in the empirical test.FindingsNot only the theoretical analysis but also the empirical evidence shows that the effects of skewness and kurtosis on loan insurance premium tend to be negative and positive, respectively. Furthermore, the actual values of skewness and kurtosis are usually negative and positive, respectively, which leads to the empirical result that the pricing model ignoring skewness and kurtosis substantially underestimates loan insurance premium.Originality/valueThis paper proposes a loan insurance pricing model considering the skewness and kurtosis of asset returns, in which the authors use the theory of Gram-Charlier option. More importantly, the authors further propose a novel iterative method to estimate skewness and kurtosis in practical application. The empirical evidence suggests that the Gram-Charlier pricing model captures the information content of skewness and kurtosis.

Systematic risk and deposit insurance pricing

2017 ◽  
Vol 7 (4) ◽  
pp. 390-406 ◽  
Author(s):  
Yaojie Zhang ◽  
Benshan Shi
Keyword(s):  
Moral Hazard ◽  
Option Pricing ◽  
Deposit Insurance ◽  
Systematic Risk ◽  
Market Risk ◽  
Market Model ◽  
Pricing Model ◽  
Content Type ◽  
Insurance Premiums ◽  
Insurance Pricing

Purpose The purpose of this paper is to alleviate the moral hazard problem created by deposit insurance and therefore develop a deposit insurance pricing model explicitly considering systematic risk. Design/methodology/approach Using the market model, the authors introduce the systematic risk component consisting of market risk and beta risk. A closed-form solution for the authors’ pricing model is derived based on the option pricing framework. Findings Compared with the authors’, the pricing model that ignores systematic risk underestimates deposit insurance premium, and cannot cover the excessive loss created by systematic risk. To examine the effect of the systematic risk component on the deposit insurance premiums estimated by the authors’ model, this paper also provides empirical evidence from China by regression analysis. The results demonstrate that, in addition to the individual failure risk, the systematic risk component is properly priced and explicitly reflected in the authors’ model. Research limitations/implications More risk factors such as liquidity risk should be introduced in the pricing of deposit insurance. Practical implications Deposit insurance premiums estimated by the authors’ model can alleviate the moral hazard problem that banks have an incentive to take on excessive systematic risk, because substantial higher insurance premiums would be charged in doing so. Originality/value Applying the option pricing theory and market model, this paper develops a deposit insurance pricing model with explicit consideration of systematic risk. The systematic risk component contains not only the market volatility but also the sensitivity of market risk.

scholarly journals A Method for Simulating Nonnormal Distributions with Specified L-Skew, L-Kurtosis, and L-Correlation

2012 ◽  
Vol 2012 ◽  
pp. 1-23 ◽  
Author(s):  
Todd C. Headrick ◽  
Mohan D. Pant
Keyword(s):  
Monte Carlo ◽  
Pearson Correlation ◽  
Theoretical Development ◽  
Nonnormal Distributions ◽  
Heavy Tailed Distributions ◽  
Moment Estimates ◽  
Monte Carlo Studies ◽  
Asymmetric Distributions ◽  
Heavy Tailed ◽  
Primary Focus

This paper introduces two families of distributions referred to as the symmetric κ and asymmetric - distributions. The families are based on transformations of standard logistic pseudo-random deviates. The primary focus of the theoretical development is in the contexts of L-moments and the L-correlation. Also included is the development of a method for specifying distributions with controlled degrees of L-skew, L-kurtosis, and L-correlation. The method can be applied in a variety of settings such as Monte Carlo studies, simulation, or modeling events. It is also demonstrated that estimates of L-skew, L-kurtosis, and L-correlation are superior to conventional product-moment estimates of skew, kurtosis, and Pearson correlation in terms of both relative bias and efficiency when moderate-to-heavy-tailed distributions are of concern.

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