Minimum revenue guarantees valuation in PPP projects under a mean reverting process

2018 ◽  
Vol 37 (3) ◽  
pp. 121-138 ◽  
Author(s):  
Carlos Andrés Zapata Quimbayo ◽  
Carlos Armando Mejía Vega ◽  
Naielly Lopes Marques
Keyword(s):  
Mean Reverting Process

scholarly journals Volatility in International Stock Markets: An Empirical Study during COVID-19

2020 ◽  
Vol 13 (9) ◽  
pp. 208
Author(s):  
Rashmi Chaudhary ◽  
Priti Bakhshi ◽  
Hemendra Gupta
Keyword(s):  
Econometric Model ◽  
Central Tendency ◽  
International Stock Markets ◽  
Efficient Portfolios ◽  
The Mean ◽  
Garch Modeling ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Bounce Back ◽  
Daily Returns ◽  
Mean Reverting Process

Predicting volatility is a must in the finance domain. Estimations of volatility, along with the central tendency, permit us to evaluate the chances of getting a particular result. Financial analysts are frequently challenged with the assignment of diversifying assets in order to form efficient portfolios with a higher risk to reward ratio. The objective of this research is to analyze the influence of COVID-19 on the return and volatility of the stock market indices of the top 10 countries based on GDP using a widely applied econometric model—generalized autoregressive conditional heteroscedasticity (GARCH). For this purpose, the daily returns of market indices from January 2019 to June 2020 were taken into consideration. The results reveal daily negative mean returns for all market indices during the COVID period (January 2020 to June 2020). Though the second quarter of the COVID period reflects a bounce back for all market indices with altered strengths, the volatility remains higher than in normal periods, signaling a bearish tendency in the market. The COVID variable, as an exogenous variance regressor in GARCH modeling, is found to be positive and significant for all market indices. Furthermore, the results confirmed the mean-reverting process for all market indices.

A Jump Diffusion Model for Evaluating of an Oilfield Development Project and its Application

2012 ◽  
Vol 616-618 ◽  
pp. 1563-1567
Author(s):  
Yuan Qi Zhou ◽  
Liang Yan
Keyword(s):  
Optimization Problem ◽  
Investment Decisions ◽  
Jump Diffusion ◽  
Development Project ◽  
Market Uncertainty ◽  
Corporate Decisions ◽  
Jump Diffusion Model ◽  
Irreversible Investments ◽  
Oilfield Development ◽  
Mean Reverting Process

Irreversible investments with largest outlay made with incomplete information are the mainstay of the oilfield development. Real Options Analysis (ROA) is a useful tool for making investment decisions under market uncertainty. Normal information generates continuous mean-reverting process for oil prices, whereas random abnormal information generates discrete jumps of random size. We will evaluate an oilfield development project using Mean-Reversion with Jumps (MRJ). As an example, we compare MRJ and Geometric Brownian Motion (GBM )valuation for the timing of investment and the optimization problem. This article concludes MRJ in some cases can induce better corporate decisions than GBM.

Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction

2016 ◽  
Vol 52 (5) ◽  
pp. 2313-2330 ◽  
Author(s):  
Leonardo M. Millefiori ◽  
Paolo Braca ◽  
Karna Bryan ◽  
Peter Willett
Keyword(s):  
Term Prediction ◽  
Long Term Prediction ◽  
Mean Reverting Process

scholarly journals Modelando Opcões de Conversão com Movimento de Reversão à Média

2007 ◽  
Vol 5 (2) ◽  
pp. 97 ◽  
Author(s):  
Carlos L. Bastian-Pinto ◽  
Luiz E. T. Brandão
Keyword(s):  
Brownian Motion ◽  
Stochastic Model ◽  
Diffusion Process ◽  
Commodity Prices ◽  
Geometric Brownian Motion ◽  
Base Case ◽  
Option Value ◽  
Uncertain Variables ◽  
Mean Reverting Process

Commodity prices are generally better modeled by a long-term Mean Reverting Process, than by a Geometric Brownian Motion stochastic diffusion process, which is more generally used to value real options, since it is simpler to use. In this article we model two correlated uncertain variables using a mean reversing process bivariate lattice to value the switch option between outputs available to ethanol and sugar producers, using the same source: sugarcane. The model results show that the switch option adds a significant value for the producer income. The article also shows that when modeled by a geometric brownian motion, the switch option yields significantly higher values than with a mean reverting model, for the option itself as much as for the base case without flexibility. This confirms that the stochastic model chosen can influence significantly the option value.

MODELING TREND AND TIME-VARYING VARIANCE OF HEART BEAT RR INTERVALS DURING STRESS TEST

2011 ◽  
Vol 10 (02) ◽  
pp. 169-180 ◽  
Author(s):  
CAMILLO CAMMAROTA ◽  
MARIO CURIONE
Keyword(s):  
Global Minimum ◽  
Stress Test ◽  
Heart Beat ◽  
Random Fluctuation ◽  
Data Series ◽  
Time Varying ◽  
Rr Intervals ◽  
Deterministic Trend ◽  
Increasing Trend ◽  
Mean Reverting Process

The heart beat RR intervals extracted from the electrocardiogram recorded during the stress test show a non stationary profile consisting of a decreasing trend during the exercise phase, an increasing trend during the recovery and a global minimum (acme). In addition this time series exhibits a time-varying variance. We decompose the series into a deterministic trend and random fluctuation. The trend is obtained as an exponential fit of the data; the fluctuation is modeled as a mean reverting process driven by the trend, in which the random innovation has a time-varying variance. Data analysis, performed on ambulatory recorded electrocardiograms of 10 healthy subjects, shows that the model describes correctly the data series on a scale of at least 300 beats.

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