Forecasting Value-at-Risk under Different Distributional Assumptions

Braione, Manuela; Scholtes, Nicolas K.

doi:10.3390/econometrics4010003

Cited by 37 publications

(19 citation statements)

References 41 publications

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“…Many empirical studies, e.g. see [8,36,12], and [11], showed that majority of the asset return errors are not normally distributed. Furthermore, the empirical studies reveal a fact that the financial return distributions are leptokurtic, i.e.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Nugroho

Susanto

Prasetia

et al. 2019

jak

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This study proposed two new classes of GARCH(1,1) model by applying the Tukeytransformations to the returns and to the lagged variance. The behavior of return volatility was investigated on the basis of models with normal and Student-t distributions for return error. The competing models were estimated by using the Excel Solver and Matlab tools. The empirical analysis is based on simulated data, daily exchange rates of the IDR/USD, and daily stock indices of FTSE100 and TOPIX. This study recommends the use of Excel Solver for finance academics and practitioners working on volatility using GARCH(1,1) models. Our empirical findings conclude that GARCH(1,1) models under Tukey transformations should be considered in risk management decisions since the models are more appropriate than standard for describing returns and volatility of financial time series and its stylized facts including fat tails and mean reverting. The Tukey transformed returns imply a shorter volatility half-life, and thus this study suggests that investors should invest the observed assets in a shorter time period to obtain higher returns.

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Section: Introductionmentioning

confidence: 99%

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Nugroho

Susanto

Prasetia

et al. 2019

jak

View full text Add to dashboard Cite

show abstract

“…In the research work of References 22 and 23, the ARIMA model was implemented to forecast peak load in a day to the next 14 days. But they have not included the power system planning factors like average load, peak load factor, day type (weekend or weekday), and season (summer, winter, spring, and autumn).…”

Section: Related Work With Motivationsmentioning

confidence: 99%

Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm

Pitchaimani¹,

Nadar²

2021

Int J Energy Res

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Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: • Novel pooling-based deep neural network is applied for residential energy management in a smart grid. • The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. • Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.

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“…Kuester et al (2006) use an EVT-based approach and focuses on the long tails of the return distribution. Braione and Scholtes (2016) study the performance of forecasting VaR under different parametric distributional assumptions and show the predominance and the predictive ability for the skewed and heavy-tailed distributions in the univariate case. The main drawback of using a parametric model is the high dependency of the obtained method to the hypothesized distribution model.…”

Section: Introductionmentioning

confidence: 99%

Interval Estimation of Value-at-Risk Based on Nonparametric Models

2018

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Value-at-Risk (VaR) has become the most important benchmark for measuring risk in portfolios of different types of financial instruments. However, as reported by many authors, estimating VaR is subject to a high level of uncertainty. One of the sources of uncertainty stems from the dependence of the VaR estimation on the choice of the computation method. As we show in our experiment, the lower the number of samples, the higher this dependence. In this paper, we propose a new nonparametric approach called maxitive kernel estimation of the VaR. This estimation is based on a coherent extension of the kernel-based estimation of the cumulative distribution function to convex sets of kernel. We thus obtain a convex set of VaR estimates gathering all the conventional estimates based on a kernel belonging to the above considered convex set. We illustrate this method in an empirical application to daily stock returns. We compare the approach we propose to other parametric and nonparametric approaches. In our experiment, we show that the interval-valued estimate of the VaR we obtain is likely to lead to more careful decision, i.e., decisions that cannot be biased by an arbitrary choice of the computation method. In fact, the imprecision of the obtained interval-valued estimate is likely to be representative of the uncertainty in VaR estimate.

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Forecasting Value-at-Risk under Different Distributional Assumptions

Cited by 37 publications

References 41 publications

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Modeling of Returns Volatility using GARCH(1,1) Model under Tukey Transformations

Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm

Interval Estimation of Value-at-Risk Based on Nonparametric Models

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