XJTLU Research Repository

1. Energy Dissipation During Impact of an Agglomerate Composed of Autoadhesive Elastic-Plastic Particles

Author:Liu, LF;Thornton, C;Shaw, SJ

Source:PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON DISCRETE ELEMENT METHODS,2017,Vol.188

Abstract:Discrete Element Method is used to simulate the impact of agglomerates consisting of autoadhesive, elastic-plastic primary particles. In order to explain the phenomenon that the elastic agglomerate fractures but the elastic-plastic agglomerate disintegrates adjacent to the impact site for the same impact velocity, we increase the impact velocity and lower the yield strength of the constituent particles of the agglomerate. We find that increasing the impact velocity can lead to the increased number of yielded contacts, and cause the elastic-plastic agglomerate to disintegrate faster. Mostly importantly, the energy dissipation process for the elastic-plastic agglomerate impact has been investigated together with the evolutions of the yielding contacts, and evolutions of velocity during impact.

2. On the theoretical distribution of the wind farm power when there is a correlation between wind speed and wind turbine availability

Author:Kan, C;Devrim, Y;Eryilmaz, S

Source:RELIABILITY ENGINEERING & SYSTEM SAFETY,2020,Vol.203

Abstract:It is important to elicit information about the potential power output of a wind turbine and a wind farm consisting of specified number of wind turbines before installation of the turbines. Such information can be used to estimate the potential power output of the wind farm which will be built in a specific region. The output power of a wind turbine is affected by two factors: wind speed and turbine availability. As shown in the literature, the correlation between wind speed and wind turbine availability has an impact on the output of a wind farm. Thus, the probability distribution of the power produced by the farm depending on the wind speed distribution and turbine availability can be effectively used for planning and risk management. In this paper, the theoretical distribution of the wind farm power is derived by considering the dependence between turbine availability and the wind speed. The theoretical results are illustrated for real wind turbine reliability and wind speed data.

3. A multi-objective optimization model for bike-sharing

Author:Shan, Yu ; Xie, Dejun ; Zhang, Rui

Source:ACM International Conference Proceeding Series,2019,Vol.

Abstract:The study proposes a multi-objective optimization model for bike-sharing industry by monitoring, with high accuracy, the user demand and providing the suitable number of bikes at selected stations. One of the key factors for designing an optimized bike sharing system is to balance the demand of pick-ups (drop-offs) around a given station and the number of available bikes (vacant lockers) in the station throughout the day. The model optimizes the location of bicycle stations and the number of parking slots that each station should have by taking account of the main contributing factors including the total budget of the bike sharing system, the popularity of riding in the city, and the expected proximity of the stations. A case study using the bike-sharing in New York was conducted to test theeffectiveness of themodel. © 2019 Association for Computing Machinery.

4. Valuation Bounds on Barrier Options Under Model Uncertainty

Author:Hong, Y

Source:JOURNAL OF FUTURES MARKETS,2013,Vol.33

Abstract:This article investigates valuation bounds on barrier options under model uncertainty. This investigation enriches the literature on the model-free valuation of these exotic options. It is found that with weak assumptions on underlying price processes, tight valuation bounds on barrier options can be sought from a set of European options. As a result, the numerical routine developed in this article can be reviewed as a new method for the evaluation of barrier options, which is independent of model assumptions. (c) 2012 Wiley Periodicals, Inc. Jrl Fut Mark 33:199234, 2013

5. Editorial for the special issue on modern aspects of financial engineering

Author:Goncu, A

Source:CHINA FINANCE REVIEW INTERNATIONAL,2018,Vol.8

6. Prediction of exchange rates with machine learning

Author:Goncu, Ahmet

Source:ACM International Conference Proceeding Series,2019,Vol.

Abstract:In this study a macroeconomic model is considered to predict the next month’s monthly average exchange rates via machine learning based regression methods including the Ridge, decision tree regression, support vector regression and linear regression. The model incorporates the domestic money supply, real interest rates, Federal Funds rate of the USA, and the last month’s monthly average exchange rate to predict the next month’s exchange rate. Monthly data with 148 observations from the US Dollar and Turkish Lira exchange rates are considered for the empirical testing of the model. Empirical results show that the Ridge regression offers accurate estimation for investors or policy makers with relative errors less than 60 basis points. Policy makers can obtain point estimates and confidence intervals for analyzing the effects of interest rate cuts on the exchange rates. © 2019 Association for Computing Machinery.

7. Structural signature and contact force distributions in the simulated three-dimensional sphere packs subjected to uniaxial compression

Author:Liu, LF;Zhang, L;Liao, SF

Source:SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY,2010,Vol.53

Abstract:Packing of spherical particles in a three-dimensional cylindrical container is simulated by using Discrete Element Method. The packed bed of spheres is also subjected to vertical compression which results in a dense compact. Microstructures of the packing during compaction are examined in detail in terms of the contact number, deviator fabric, and radial distribution function. Furthermore, contact force distributions are measured at different locations in the pack, i.e. the centre, the side wall, and the base (or bottom wall) of the container. The simulations show that random close packing (RCP) tends to exist in the centre of the pack, while ordered packing structures exist near the container's walls. The uniaxial compression doesn't seem to alter the packing structure in the pack centre remarkably, but to reduce the structural anisotropy of the packing close to the container's base. The simulated results have also helped to establish the correlations between packing structures and contact force distributions. Further, it is shown that small contact force distributions are sensitive to local packing structures. The simulated results are shown to be consistent with the recent experimental and simulation findings.

8. Artificial neural networks for optimization of gold-bearing slime smelting

Author:Liu, D;Yuan, YD;Liao, SF

Source:EXPERT SYSTEMS WITH APPLICATIONS,2009,Vol.36

Abstract:Pyrometallurgy is often used in the industrial process for treating gold-bearing slime. Slag compositions have remarkable influences on the recovery of gold and the gold content in slag. A method for determining optimum flux compounding with neural networks is studied in this paper, and the neural network model for estimating the gold contents with different slag compositions is presented. On the basis of the neural network model, an algorithm for searching the optimum flux compounding in the gold-slime smelting process is proposed, and the optimum flux compositions are obtained accordingly. (C) 2009 Elsevier Ltd. All rights reserved.

9. When to Refinance Mortgage Loans in a Stochastic Interest Rate Environment

Author:Gan, SW;Zheng, J;Feng, XX;Xie, DJ

Source:INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTIST, IMECS 2012, VOL II,2012,Vol.2196

Abstract:Refinancing refers to the replacement of an existing debt obligation with another debt obligation to take the advantage of a lower interest rate. This paper reflects our ongoing work to find a desirable refinancing time for mortgage borrowers to minimize the total payments in a dynamic interest rate environment. To simulate the alternative financial service that the market may offer, it is assumed that the future interest rate follows a stochastic model with mean-reverting property, which is essentially the only required market condition to implement our method. To make it more applicable to the real financial practice, two balance settlement schemes are considered and compared. Numerical simulations with varying samplings lead to several interesting characteristics pertaining to the optimal mortgage refinancing period. Our method is robust and user friendly, thus is useful for financial institutions and individual property investors.

10. Uniform point sets and the collision test

Author:Goncu, A;Okten, G

Source:JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS,2014,Vol.259

Abstract:Monte Carlo and quasi-Monte Carlo methods are popular numerical tools used in many applications. The quality of the pseudorandom sequence used in a Monte Carlo simulation is essential to the accuracy of its estimates. Likewise, the quality of the low-discrepancy sequence determines the accuracy of a quasi-Monte Carlo simulation. There is a vast literature on statistical tests that help us assess the quality of a pseudorandom sequence. However, for low-discrepancy sequences, assessing quality by estimating discrepancy is a very challenging problem, leaving us with no practical options in very high dimensions. In this paper, we will discuss how a certain interpretation of the well-known collision test for pseudorandom sequences can be used to obtain useful information about the quality of low-discrepancy sequences. Numerical examples will be used to illustrate the applications of the collision test. (C) 2013 Elsevier B.V. All rights reserved.

11. Forecasting daily residential natural gas consumption: A dynamic temperature modelling approach

Author:Göncü,Ahmet;Karahan,Mehmet Oğuz;Kuzubaş,Tolga Umut

Source:Bogazici Journal,2019,Vol.33

Abstract:In this paper, we propose a new methodology to forecast residential and commercial natural gas consumption which combines natural gas demand estimation with a stochastic temperature model. We model demand and temperature processes separately and derive the distribution of natural gas consumption conditional on temperature. Natural gas consumption and local temperature processes are estimated using daily data on natural gas consumption and temperature for Istanbul, Turkey. First, using the derived conditional distribution of the natural gas consumption we obtain confidence intervals of point forecasts. Second, we forecast natural gas consumption by using temperature and consumption paths generated by Monte Carlo simulations. We evaluate the forecast performance of different model specifications by comparing the realized consumption values with the model forecasts by backtesting method. We utilize our analytical solution to establish a relationship between the traded temperature-based weather derivatives, i.e. HDD/CDD futures, and expected natural gas consumption. This relationship allows for partial hedging of the demand risk faced by the natural gas suppliers via traded weather derivatives.

12. Discrete element modelling of impact attritions of agglomerates of autoadhesive elastoplastic particles

Author:Liu Lianfeng;Liao Shufang

Source:Chinese Journal of Applied Mechanics,2015,Vol.32

Abstract:In this paper, discrete element modelling of agglomerate impact has been conducted by adopting the theory of contact mechanics for elastoplastic auto-adhesive particles. Results show that, under the same conditions other than the predefined yield pressure, the elasto-plastic agglomerate tends to disintegrate during impact in contrast to the elastic agglomerate which has fractured in terms of breakage pattern. Due to the presence of plastic deformation and additional kinetic energy loss, the elastoplastic agglomerate during impact needs longer loading period than the elastic agglomerate, and generates larger peak wall force and greater internal damage. It was also observed that the amplitude of wall forces during loading is relatively smaller than the corresponding elastic agglomerate. However, during unloading, the remaining kinetic energy become less.

13. An ARMA Model for Natural Gas Consumption

Author:Ahmet Goncu;

Source:Proceedings of 2013 3rd International Conference on Energy and Environmental Science(ICEES 2013),2013,Vol.

Abstract:In this study we propose a new model for modeling and forecasting natural gas consumption, which is important for efficient management of energy resources. The existing literature on modeling natural gas consumption uses time series models such as autoregressive moving average (ARMA) models. We exte...

14. Reliability based modeling and analysis for a wind power system integrated by two wind farms considering wind speed dependence

Author:Eryilmaz, S;Kan, C

Source:RELIABILITY ENGINEERING & SYSTEM SAFETY,2020,Vol.203

Abstract:Integrating multiple wind farms into power systems may reduce the fluctuation in total power output of wind farms and hence it decreases the system risk resulting from the wind speed variability. In this paper, a wind power system consisting of two wind farms is modeled and analyzed considering the dependence between wind speeds at two sites. In particular, the system is modeled as a threshold system and reliability values of wind turbines are also taken into account in capacity based calculations. The results are illustrated for the available bivariate wind speed data in the literature.

15. Application of MCMC algorithm in interest rate modeling

Author:Feng, Xiaoxia ; Xie, Dejun

Source:IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011,2011,Vol.2

Abstract:Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.

16. Fitting the variance-gamma model: A goodness-of-fit check for emerging markets

Author:Göncü,Ahmet;Karahan,Mehmet Oğuz;Kuzubaş,Tolga Umut

Source:Bogazici Journal,2013,Vol.27

Abstract:Variance-Gamma model is widely used for option pricing; however there has been little research on the empirical performance of this model for emerging market economies. In this paper we evaluate the goodness-of-fit of the Variance-Gamma model using index returns data from ten different emerging markets. Based on the Chi-square, Anderson-Darling and Kolmogorov-Smirnov goodness-of-fit test statistics, we show that the Variance-Gamma model fits the dataset well and improves upon the fit of the normal distribution for emerging stock market indices. Furthermore, under the Variance–Gamma model, closed form solutions for pricing European call and put options exist and model parameters can be efficiently estimated via the maximum likelihood method.

17. Modeling the Cashflow Management of Bike Sharing Industry

Author:Shen, BR;Shan, Y;Jia, YY;Xie, DJ;Zhu, SX

Source:BUSINESS INFORMATION SYSTEMS, BIS 2019, PT II,2019,Vol.354

Abstract:The sharing economy has been widely concerned since its inception and bike sharing is one of the most representative examples. The paper attempts to investigate the cashflow management strategy of bike sharing companies to optimize the overall financial return. The framework of our model is based on the assumption that bike sharing companies may invest operation income in financial market for long-term and short-term earnings. Optimal reserve pool is modeled and estimated using double parameterized compound Poisson distributions. Empirical examples and Monte Carlo analysis are provided for model validation.

18. Calibration of Heston’s Option Pricing Model by Using Simulated Annealing Algorithm

Author:王林;张蕾;刘连峰

Source:The Journal of Quantitative & Technical Economics,2011,Vol.28

Abstract:Stochastic Volatility Model （SVM） for option pricing relaxes the assumptions of the BS model. Heston＇s option pricing model, which differs from other similar stochastic models, has a closed-form solution. However, it is usually difficult to determine the input parameters when the model is employed. In this pa- per, we use the Simulated Annealing Method together with the minimum residual sum of squares to estimate the 5 parameters in Heston model. The simulated annea- ling method can jump out of the local minimal values at certain probability, then converge with a probability 1 to the global minimal value. This method allows us to calibrate suitable parameters for Heston model. In the empirical study, we have de- termined the suitable parameters for Heston model by using Hang Kong Hangsh- eng equity index option data on October 15, 2010 and we have also used these pa- rameters to price the index options for October 18, 2010.

19. Arbitrage Bounds on Currency Basket Options

Author:Hong, Y

Source:MATHEMATICAL AND COMPUTATIONAL APPLICATIONS,2020,Vol.25

Abstract:This article exploits arbitrage valuation bounds on currency basket options. Instead of using a sophisticated model to price these options, we consider a set of pricing models that are consistent with the prices of available hedging assets. In the absence of arbitrage, we identify valuation bounds on currency basket options without model specifications. Our results extend the work in the literature by seeking tight arbitrage valuation bounds on these options. Specifically, the valuation bounds are enforced by static portfolios that consist of both cross-currency options and individual options denominated in the numeraire currency.

20. Study on model-free implied volatility of hang seng index options

Author:Liu,David;Lin,Ran;Zhang,Lei

Source:International Journal of Mathematics in Operational Research,2012,Vol.4

Abstract:This paper carries out studies on the Model-Free Implied Volatility (MF-IV) and other two competing volatility measurements including Black-Scholes Implied Volatility (BS-IV) and GARCH (1,1) with respect to forecasting error and information content. It is known that MF-IV is a volatility measurement independent of any option pricing model and it provides a direct test on market efficiency. The study introduces data of Hang Seng Index Call Options over two different forecasting horizons to compare the forecasting performance. The empirical results indicate that MF-IV contains richer information content than BS-IV over both monthly and bi-monthly forecasting horizons. However, MF-IV has better predictive accuracy in monthly forecasting horizon, while BS-IV performs better over bi-monthly forecasting horizon. Copyright © 2012 Inderscience Enterprises Ltd.

Department of Financial Mathematics

ADDRESS

WEBSITE

66 records

Filter records by:

Publication Year

Content Type

Source

Authorship