This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility.
Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models; however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria— A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account.
However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework. Introduction Oil is an important source of energy that drives modern economies. Changes in oil prices may lead to a substantial impact on such economies; therefore, the proactive knowledge of future movements of oil prices can lead to better decisions at various levels of governments, central banks, and the private sector.
In an attempt to gain knowledge on future movements of oil prices, the forecasting of both the level and the volatility of oil prices proves useful.
Fourth, the financial industry standard approach to investment risk management Alternative volatility forecasting method evaluation to model risk within a parametric approach framework by using value-at-risk VaR as a proxy to measure the risk of financial instruments e.
Although a relatively large number of models are available to forecast the volatility of crude oil prices, their relative performance evaluation has not attracted as much attention as it deserves.
To be more specific, our survey of the literature revealed that most studies tend to use several performance criteria and, for each criterion, one or several metrics to evaluate the performance of competing forecasting models; however, the assessment exercise is typically restricted to the ranking of models by measure.
Xu and Ouenniche  highlighted this issue faced by the forecasting community; namely, the fact that the current methodology for assessing the relative performance of competing forecasting models is unidimensional in nature i.
Their multi-criteria framework allows one to obtain a single ranking that takes account of several performance criteria; however, it suffers from a number of issues.
First, under the variable returns-to-scale VRS assumption, input-oriented efficiency scores can be different from output-oriented efficiency scores, which may lead to different rankings. Second, radial super-efficiency DEA models e.
Third, radial super-efficiency DEA models ignore potential slacks in inputs and outputs and thus may over-estimate the efficiency score, on one hand, and could only take account of technical efficiency i.
Finally, in many applications such as ours, the choice of an orientation in DEA is rather superfluous. In this paper, we overcome these issues by proposing an orientation-free super-efficiency DEA framework; namely, a slacks-based super-efficiency DEA framework for assessing the relative performance of competing volatility forecasting models.
The remainder of this paper is organized as follows.
In Section 3, we briefly review the basic concepts of DEA and propose an improved DEA framework to evaluate the relative performance of competing forecasting models for crude oil prices volatility.
In Section 4, we present and discuss our empirical results. Finally, Section 5 concludes the paper. Crude Oil Volatility Crude oil is one of the most important sources of energy and its price has undergone large and persistent fluctuations and seems greatly influenced by exogenous events.
In general, global macroeconomic conditions and political instabilities in both OPEC regions and non-OPEC regions are believed to have a substantial impact on oil supply and demand and subsequently on its prices e.
Given the volatile nature of the oil market, a reliable forecast of oil price volatility is an important input to many decision making processes such as macroeconomic policy making, risk management, options pricing, and portfolio management.This literature review summarizes the academic research on option-implied volatility.
It describes algorithms for calculating implied volatility and various weighting schemes used to derive a single volatility estimate from the prices of multiple options, summarizes evidence in the debate on whether to use historical data or implied volatility in forecasting, and reviews several other papers.
Home — All Essay Examples — Finance — Evaluation Of Alternative Volatility Forecasting Methods Evaluation Of Alternative Volatility Forecasting Methods Category: Finance.
|Forecasting realized volatility: a review - Munich Personal RePEc Archive||Therefore, the research of volatility forecasting has been an active area of study since the past years.|
We use intraday data on four major currency pairs to perform out-of-sample evaluation of volatility forecasts between our model and well-established alternative models.
The empirical Hence the arti cial neural network method is the key to achieving forecasting precision and. The Information Content of Implied Volatility from reduce interpretive issues that can arise from traditional forecast evaluation procedures. Results To compare the forecasting performance of implied volatility versus a time series alternative, a simple GARCH (1,1) model is estimated using the nearby futures return series presented.
Section 3 outlines the econometric methodology, including the volatility forecasting models considered, alternative volatility proxy measures and forecast appraisal criteria.
Section 4 presents the empirical findings of the volatility forecasting exercise and evaluates the forecasts in the context of VaR calculation, while summary and. Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades.