Stabilizing Log-Normal Diffusion in View of Compounding Swaps Funding Risk Credit Valuation Adjustment (FRCVA)

2015 ◽  
Author(s):  
Cyril Durand
Keyword(s):  
Normal Diffusion ◽  
Credit Valuation Adjustment ◽  
Log Normal ◽  
Funding Risk

Fitting real data by means of non-homogeneous log-normal diffusion processes

2017 ◽  
Vol 10 (4) ◽  
pp. 585-600 ◽  
Author(s):  
Patricia Román-Román ◽  
Juan José Serrano-Pérez ◽  
Francisco Torres-Ruiz
Keyword(s):  
Diffusion Processes ◽  
Real Data ◽  
Normal Diffusion ◽  
Log Normal

On the quasi-Monte Carlo method with Halton points for elliptic PDEs with log-normal diffusion

2016 ◽  
Vol 86 (304) ◽  
pp. 771-797 ◽  
Author(s):  
Helmut Harbrecht ◽  
Michael Peters ◽  
Markus Siebenmorgen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Elliptic Pdes ◽  
Quasi Monte Carlo ◽  
Normal Diffusion ◽  
Log Normal

scholarly journals Optimal consumption/portfolio choice with borrowing rate higher than deposit rate

1998 ◽  
Vol 39 (4) ◽  
pp. 449-462 ◽  
Author(s):  
Wensheng Xu ◽  
Shuping Chen
Keyword(s):  
Portfolio Choice ◽  
Optimal Strategies ◽  
Optimal Investment ◽  
Dynamic Programming Principle ◽  
Optimal Consumption ◽  
Bank Account ◽  
Normal Diffusion ◽  
Hamilton Jacobi Bellman ◽  
Log Normal ◽  
Optimal Consumption And Investment

AbstractIn this paper, optimal consumption and investment decisions are studied for an investor who has available a bank account and a stock whose price is a log normal diffusion. The bank pays at an interest rate r(t) for any deposit, and vice takes at a larger rate r′(t) for any loan. Optimal strategies are obtained via Hamilton-Jacobi-Bellman (HJB) equation which is derived from dynamic programming principle. For the specific HARA case, we get the optimal consumption and optimal investment explicitly, which coincides with the classical one under the condition r′(t) ≡ r(t)

On the log‐normal diffusion process

1989 ◽  
Vol 30 (4) ◽  
pp. 953-955 ◽  
Author(s):  
Siegfried H. Lehnigk
Keyword(s):  
Diffusion Process ◽  
Normal Diffusion ◽  
Log Normal

Algorithm AS 309: Estimation in Multivariate Log‐normal Diffusion Processes with Exogenous Factors

1997 ◽  
Vol 46 (1) ◽  
pp. 140-146 ◽  
Author(s):  
R. Gutiérrez Jáimez ◽  
A. González Carmona ◽  
F. Torres Ruiz
Keyword(s):  
Diffusion Processes ◽  
Normal Diffusion ◽  
Exogenous Factors ◽  
Log Normal

scholarly journals Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk

2020 ◽  
Vol 7 (6) ◽  
pp. 70
Author(s):  
Derek Singh ◽  
Shuzhong Zhang
Keyword(s):  
Credit Risk ◽  
Wasserstein Distance ◽  
Lagrangian Duality ◽  
Computational Experiments ◽  
Over The Counter ◽  
Duality Results ◽  
Counterparty Credit Risk ◽  
Credit Valuation Adjustment ◽  
Distributionally Robust ◽  
Funding Risk

This paper investigates calculations of robust X-Value adjustment (XVA), in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA), for over-the-counter derivatives under distributional ambiguity using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional ambiguity under a variety of portfolio and market configurations. Finally some suggestions for further work are discussed.

FLOW THROUGH ANISOTROPIC POROUS MEDIUM WITH MULTISCALE LOG-NORMAL CONDUCTIVITY

2010 ◽  
Vol 13 (2) ◽  
pp. 171-182
Author(s):  
O. N. Soboleva ◽  
E. P. Kurochkina
Keyword(s):  
Porous Medium ◽  
Anisotropic Porous Medium ◽  
Flow Through ◽  
Log Normal

Prediction of Haemoglobin Level by Some Probability Distribution

2020 ◽  
Vol 9 (1) ◽  
pp. 84-88
Author(s):  
Govinda Prasad Dhungana ◽  
Laxmi Prasad Sapkota
Keyword(s):  
Probability Distribution ◽  
Normal Distribution ◽  
Goodness Of Fit ◽  
Continuous Variable ◽  
Hemoglobin Level ◽  
Chi Square ◽  
Chi Square Test ◽  
Log Normal ◽  
Two Parameters ◽  
Best Fit

 Hemoglobin level is a continuous variable. So, it follows some theoretical probability distribution Normal, Log-normal, Gamma and Weibull distribution having two parameters. There is low variation in observed and expected frequency of Normal distribution in bar diagram. Similarly, calculated value of chi-square test (goodness of fit) is observed which is lower in Normal distribution. Furthermore, plot of PDFof Normal distribution covers larger area of histogram than all of other distribution. Hence Normal distribution is the best fit to predict the hemoglobin level in future.

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