scholarly journals Discrete Time vs Continuous Time Stock-price Dynamics and Implications for Option Pricing

2001 ◽  
Author(s):  
Damiano Brigo ◽  
Fabio Mercurio
Keyword(s):  
Option Pricing ◽  
Discrete Time ◽  
Continuous Time ◽  
Stock Price ◽  
Price Dynamics

scholarly journals Executive stock options with a rebate: Valuation formula

2006 ◽  
Vol 3 (4) ◽  
pp. 76-79
Author(s):  
P.W.A. Dayananda
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Stock Price ◽  
Stock Options ◽  
Stock Option ◽  
Executive Stock Options ◽  
Exercise Time ◽  
Executive Stock Option ◽  
Exercise Period ◽  
Over Time

We examine the valuation of executive stock option award where there is a rebate at exercise. The rebate depends on the performance of the stock of the corporation over time the period concerned; in particular we consider the situation where the executive can purchase the stock at exercise time at discount proportional to the minimum value of the stock price over the exercise period. Valuation formulae are provided both when assessment is done in discrete time as well as in continuous time. Some numerical illustrations are also presented

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

scholarly journals Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker
Keyword(s):  
Network Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Centrality Measures ◽  
Time Interval ◽  
Direct Effects ◽  
Network Approach ◽  
Var Models ◽  
Auto Regressive ◽  
Conceptual Shift

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

On quasi-stationary distributions in absorbing continuous-time finite Markov chains

1967 ◽  
Vol 4 (1) ◽  
pp. 192-196 ◽  
Author(s):  
J. N. Darroch ◽  
E. Seneta
Keyword(s):  
Markov Chains ◽  
Discrete Time ◽  
Continuous Time ◽  
Present Note ◽  
Time Parameter ◽  
Stationary Distributions ◽  
Finite State ◽  
Absorbing Markov Chains ◽  
Finite Markov Chains ◽  
Finite State Space

In a recent paper, the authors have discussed the concept of quasi-stationary distributions for absorbing Markov chains having a finite state space, with the further restriction of discrete time. The purpose of the present note is to summarize the analogous results when the time parameter is continuous.

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