مطالب مرتبط با کلیدواژه

Risk measurement


۱.

Risk measurement and Implied volatility under Minimal Entropy Martingale Measure for Levy process(مقاله علمی وزارت علوم)

کلیدواژه‌ها: Minimal Entropy Martingale Measure Risk measurement Implied volatility Exponential Levy Process Monte-Carlo

حوزه‌های تخصصی:
تعداد بازدید : ۳۲۱ تعداد دانلود : ۲۳۰
This paper focuses on two main issues that are based on two important concepts: exponential Levy process and minimal entropy martingale measure. First, we intend to obtain risk measurement such as value-at-risk (VaR) and conditional value-at-risk (CvaR) using Monte-Carlo methodunder minimal entropy martingale measure (MEMM) for exponential Levy process. This Martingale measure is used for the exponential type of the processes such as exponential Levy process. Also, it can be said MEMM is a kind of important sampling method where the probability measure with minimal relative entropy replaces the main probability. Then we are going to obtain VaR and CVaR by Monte-Carlo simulation. For this purpose, we have to calculate option price, implied volatility and returns under MEMM and then obtain risk measurement by proposed algorithm. Finally, this model is simulated for exponential variance gamma process. Next, we intend to develop two theorems for implied volatility under minimal entropy martingale measure by examining the conditions. These theorems consider the asymptotic implied volatility for the case that time to maturity tends to zero and infinity.
۲.

Introduction of New Risk Metric using Kernel Density Estimation Via Linear Diffusion(مقاله علمی وزارت علوم)

کلیدواژه‌ها: Risk measurement Generalized Co-Lower Partial Moment Portfolio optimization Nonparametric estimation Stock Market

حوزه‌های تخصصی:
تعداد بازدید : ۳۳۳ تعداد دانلود : ۲۰۶
Any investor in stock markets around the world has a deep concern about the shortfalls of allocation wealth to any stock without accurate estimation of related risks. As we review the literature of risk management methods, one of the main pillars for the risk management framework in defining risk measurement approach using historical data is the estimation of the probability distribution function. In this paper, we propose a new measure by using kernel density estimation via diffusion as a nonparametric approach in probability distribution estimation to enhance the accuracy of estimation and consider some distribution characteristics, investor risk aversion and target return which will make it more accurate, compre-hensive and consistent with stock historical performance and investor concerns.