Showing papers by "Pritam Ranjan published in 2017"
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TL;DR: In this paper, a modified history matching approach was proposed for calibrating the time-series rainfall-runoff models with respect to real data collected from the state of Georgia, USA.
Abstract: Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively.
3 citations
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TL;DR: This paper proposes to apply a history matching approach for calibrating these hydrological models, which also gives better insights for improved management of these systems.
Abstract: In this paper, we consider two rainfall-runoff computer models. The first model is Matlab-Simulink model which simulates runoff from windrow compost pad (located at the Bioconversion Center in Athens, GA) over a period of time based on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which estimates surface runoff in the Middle Oconee River in Athens, GA. The input parameter spaces of both models are sensitive and high dimensional, the model output for every input combination is a time-series of runoff, and these two computer models generate a wide spectrum of outputs including some that are far from reality. In order to improve the prediction accuracy, in this paper we propose to apply a history matching approach for calibrating these hydrological models, which also gives better insights for improved management of these systems.
2 citations
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TL;DR: In this paper, the authors propose mean-corrections for several generalizations of Taylor SVM that capture the complex market behavior as well as satisfy the efficient market hypothesis (EMH), a necessary mean zero condition for the return distribution that prevents arbitrage possibilities.
Abstract: In an efficient stock market, the returns and their time-dependent volatility are often jointly modeled by stochastic volatility models (SVMs). Over the last few decades several SVMs have been proposed to adequately capture the defining features of the relationship between the return and its volatility. Among one of the earliest SVM, Taylor (1982) proposed a hierarchical model, where the current return is a function of the current latent volatility, which is further modeled as an auto-regressive process. In an attempt to make the SVMs more appropriate for complex realistic market behavior, a leverage parameter was introduced in the Taylor SVM, which however led to the violation of the efficient market hypothesis (EMH, a necessary mean-zero condition for the return distribution that prevents arbitrage possibilities). Subsequently, a host of alternative SVMs had been developed and are currently in use. In this paper, we propose mean-corrections for several generalizations of Taylor SVM that capture the complex market behavior as well as satisfy EMH. We also establish a few theoretical results to characterize the key desirable features of these models, and present comparison with other popular competitors. Furthermore, four real-life examples (Oil price, CITI bank stock price, Euro-USD rate, and S&P 500 index returns) have been used to demonstrate the performance of this new class of SVMs.
2 citations
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TL;DR: In this article, the authors propose mean-corrections for several generalizations of Taylor's SVM that capture the complex market behavior as well as satisfy the efficient market hypothesis (EMH), which is a necessary condition for the return distribution that prevents arbitrage possibilities.
Abstract: In an efficient stock market, the returns and their time-dependent volatility are often jointly modeled by stochastic volatility models (SVMs). Over the last few decades several SVMs have been proposed to adequately capture the defining features of the relationship between the return and its volatility. Among one of the earliest SVM, Taylor (1982) proposed a hierarchical model, where the current return is a function of the current latent volatility, which is further modeled as an auto-regressive process. In an attempt to make the SVMs more appropriate for complex realistic market behavior, a leverage parameter was introduced in the Taylor's SVM, which however led to the violation of the efficient market hypothesis (EMH, a necessary mean-zero condition for the return distribution that prevents arbitrage possibilities). Subsequently, a host of alternative SVMs had been developed and are currently in use. In this paper, we propose mean-corrections for several generalizations of Taylor's SVM that capture the complex market behavior as well as satisfy EMH. We also establish a few theoretical results to characterize the key desirable features of these models, and present comparison with other popular competitors. Furthermore, four real-life examples (Oil price, CITI bank stock price, Euro-USD rate, and S&P 500 index returns) have been used to demonstrate the performance of this new class of SVMs.