scholarly journals Volatility Bursts: A Discrete-time Option Model with Multiple Volatility Components

2021 ◽  
Author(s):  
Francesca Lilla
Keyword(s):  
Discrete Time ◽  
Option Model

scholarly journals A Discrete-Time American Put Option Model with Fuzziness of Stock Prices

2005 ◽  
Vol 4 (3) ◽  
pp. 191-207 ◽  
Author(s):  
Yuji Yoshida ◽  
Masami Yasuda ◽  
Jun-ichi Nakagami ◽  
Masami Kurano
Keyword(s):  
Discrete Time ◽  
Stock Prices ◽  
American Put Option ◽  
Put Option ◽  
Option Model ◽  
American Put

The pricing of real options in discrete time models

2001 ◽  
Vol 19 (1) ◽  
pp. 9-34 ◽  
Author(s):  
Yuichiro Kawaguchi ◽  
Kazuhiro Tsubokawa
Keyword(s):  
Real Options ◽  
Discrete Time ◽  
Real Option ◽  
Tokyo Metropolitan Area ◽  
Real Option Pricing ◽  
Option Model ◽  
Ito Process ◽  
Return Process ◽  
Discrete Time Models ◽  
Made In

This paper proposes a discrete time real options model with time‐dependent and serial correlated return process for a real estate development problem with waiting options. Based on a Martingale condition, the paper claims to be able to relax many unrealistic assumptions made in the typical real option pricing methodology. Our real option model is a new one without assuming the return process as “Ito Process”, specifically, without assuming a geometric Brownian motion. We apply the model to the condominium market in Tokyo metropolitan area in the period 1971‐1997 and estimate the value of waiting to invest in 1998‐2007. The results partly provide realistic estimates of the parameters and show the applicability of our model.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Sign in / Sign up

Export Citation Format

Share Document