Effects of operator learning on production output: a Markov chain approach

2014 ◽  
Vol 65 (12) ◽  
pp. 1814-1823 ◽  
Author(s):  
Corey Kiassat ◽  
Nima Safaei ◽  
Dragan Banjevic
Keyword(s):  
Markov Chain ◽  
Markov Chain Approach ◽  
Production Output

Estimation of Interregional Trade Flows: A Markov Chain Approach

1986 ◽  
Vol 18 (1) ◽  
pp. 123-132 ◽  
Author(s):  
I Weksler ◽  
D Freeman ◽  
G Alperovich
Keyword(s):  
Markov Chain ◽  
Trade Flows ◽  
Interregional Trade ◽  
Markov Chain Approach

Change point dynamics for financial data: an indexed Markov chain approach

2018 ◽  
Vol 15 (2) ◽  
pp. 247-266 ◽  
Author(s):  
Guglielmo D’Amico ◽  
Ada Lika ◽  
Filippo Petroni
Keyword(s):  
Markov Chain ◽  
Change Point ◽  
Financial Data ◽  
Markov Chain Approach

scholarly journals Construction and Control of Genetic Regulatory Networks:A Multivariate Markov Chain Approach

2008 ◽  
Vol 01 (01) ◽  
pp. 15-21 ◽  
Author(s):  
Shu-Qin Zhang ◽  
Ling-Yun Wu ◽  
Wai-Ki Ching ◽  
Yue Jiao ◽  
Raymond, H. Chan
Keyword(s):  
Markov Chain ◽  
Markov Chain Approach ◽  
And Control

Analysis of Two Phases Queue With Vacations and Breakdowns Under T-Policy

2019 ◽  
pp. 13-31
Author(s):  
Khalid Alnowibet ◽  
Lotfi Tadj
Keyword(s):  
Markov Chain ◽  
Steady State ◽  
Performance Measures ◽  
Threshold Level ◽  
System Size ◽  
State System ◽  
Markov Chain Approach ◽  
Optimal Value ◽  
Two Phases ◽  
Random Breakdowns

The service system considered in this chapter is characterized by an unreliable server. Random breakdowns occur on the server and the repair may not be immediate. The authors assume the possibility that the server may take a vacation at the end of a given service completion. The server resumes operation according to T-policy to check if enough customers have arrived while he was away. The actual service of any arrival takes place in two consecutive phases. Both service phases are independent of each other. A Markov chain approach is used to obtain the steady state system size probabilities and different performance measures. The optimal value of the threshold level is obtained analytically.

FORWARD-RATE VOLATILITIES AND THE SWAPTION MATRIX: WHY NEITHER TIME-HOMOGENEITY NOR TIME-DEPENDENCE ARE ENOUGH

2006 ◽  
Vol 09 (05) ◽  
pp. 705-746 ◽  
Author(s):  
RICCARDO REBONATO
Keyword(s):  
Markov Chain ◽  
Market Volatility ◽  
Time Dependent ◽  
Forward Rate ◽  
Systematic Analysis ◽  
Option Markets ◽  
Markov Chain Approach ◽  
Major Market ◽  
Volatility Model

This work presents the first systematic analysis of the whole swaption matrix by fitting a parsimonious, nonlinear, financially-inspired volatility model to market data. The study uses several years of data spanning period of major market volatility. We find that the quality of the fits is good (on average of the same magnitude as the bid-offer spread), and better when a displaced-diffusion approach is chosen, but some systematic shortcomings are observed and discussed. The analysis suggests that a two-regime Markov chain approach may be more successful and better financially motivated. More generally, the present study highlights the shortcomings of purely time-dependent or time-homogenous approaches. These findings should be applicable to other option markets as well. Finally, we find that the present (nonlinear) model vastly outperforms PCA-based approaches when in comes to predicting moves in implied volatilities.

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