Topical Collection "Volatility Modelling and Forecasting"

Editor

Prof. Dr. Robert Brooks
E-Mail Website
Guest Editor
Department of Econometrics and Business Statistics, Monash Business School, Monash University, Melbourne, VIC 3145, Australia
Interests: financial econometrics; volatility modelling; credit ratings; asset pricing

Topical Collection Information

Dear Colleagues,

Volatility modelling is a major topic in empirical finance and financial econometrics research. The key dimensions of volatility modelling include risk management; volatility modelling, including models from the GARCH, realised volatility, and stochastic volatility families; the role of big data and data at different frequencies (daily, intra-day); volatility spillovers; and the behaviour of volatility in crisis periods.

The topics covered in this Special Issue will include but are not limited to:

  • Volatility and its role in risk management;
  • Estimation of GARCH, realised volatility, and stochastic volatility models;
  • The role of big data in volatility estimation;
  • Volatility spillovers;
  • Volatility and its role in crises and contagion.

Prof. Dr. Robert Brooks
Guest Editor

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Keywords

  • Volatility
  • GARCH
  • Realised volatility
  • Stochastic volatility
  • Spillovers
  • Contagion

Published Papers (10 papers)

2021

Jump to: 2020, 2018

Article
Spillovers and Asset Allocation
J. Risk Financial Manag. 2021, 14(8), 345; https://doi.org/10.3390/jrfm14080345 - 27 Jul 2021
Viewed by 326
Abstract
There is a large and growing literature on spillovers but no study that systematically evaluates the importance of spillovers for portfolio management. This paper provides such an analysis and demonstrates that spillovers are fully embedded in estimates of expected returns, variances, and correlations [...] Read more.
There is a large and growing literature on spillovers but no study that systematically evaluates the importance of spillovers for portfolio management. This paper provides such an analysis and demonstrates that spillovers are fully embedded in estimates of expected returns, variances, and correlations and that estimation of spillovers is not necessary for asset allocation. Simulations of typical empirical spillover settings further show that same-frequency spillovers are often negligible and spurious. Full article
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Article
Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19
J. Risk Financial Manag. 2021, 14(7), 314; https://doi.org/10.3390/jrfm14070314 - 08 Jul 2021
Viewed by 717
Abstract
This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity [...] Read more.
This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news. Full article
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Article
Forecasting Volatility and Tail Risk in Electricity Markets
J. Risk Financial Manag. 2021, 14(7), 294; https://doi.org/10.3390/jrfm14070294 - 26 Jun 2021
Viewed by 334
Abstract
This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility, Value-at-Risk, and Expected Shortfall in the Australian electricity markets of New South Wales, Queensland, and Victoria. We propose using Realized GARCH-type models with multiple measurement equations based [...] Read more.
This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility, Value-at-Risk, and Expected Shortfall in the Australian electricity markets of New South Wales, Queensland, and Victoria. We propose using Realized GARCH-type models with multiple measurement equations based on robust estimators to account for market microstructure noise and jumps in electricity price series. The model specifications that combine information from multiple realized measures improve the in-sample fit of the data. The out-of-sample analysis shows that use of the jump-robust medRV estimator significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different risk levels, the standard GARCH(1,1) also performs remarkably well. Full article
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Article
How Much Do Negative Probabilities Matter in Option Pricing?: A Case of a Lattice-Based Approach for Stochastic Volatility Models
J. Risk Financial Manag. 2021, 14(6), 241; https://doi.org/10.3390/jrfm14060241 - 30 May 2021
Viewed by 684
Abstract
In this paper, we focus on two-factor lattices for general diffusion processes with state-dependent volatilities. Although it is common knowledge that branching probabilities must be between zero and one in a lattice, few methods can guarantee lattice feasibility, referring to the property [...] Read more.
In this paper, we focus on two-factor lattices for general diffusion processes with state-dependent volatilities. Although it is common knowledge that branching probabilities must be between zero and one in a lattice, few methods can guarantee lattice feasibility, referring to the property that all branching probabilities at all nodes in all stages of a lattice are legitimate. Some practitioners have argued that negative probabilities are not necessarily ‘bad’ and may be further exploited. A theoretical framework of lattice feasibility is developed in this paper, which is used to investigate how negative probabilities may impact option pricing in a lattice approach. It is shown in this paper that lattice feasibility can be achieved by adjusting a lattice’s configuration (e.g., grid sizes and jump patterns). Using this framework as a benchmark, we find that the values of out-of-the-money options are most affected by negative probabilities, followed by in-the-money options and at-the-money options. Since legitimate branching probabilities may not be unique, we use an optimization approach to find branching probabilities that are not only legitimate but also can best fit the probability distribution of the underlying variables. Extensive numerical tests show that this optimized lattice model is robust for financial option valuations. Full article
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Article
Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects
J. Risk Financial Manag. 2021, 14(5), 225; https://doi.org/10.3390/jrfm14050225 - 18 May 2021
Viewed by 504
Abstract
This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown [...] Read more.
This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms. Full article
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Article
Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors
J. Risk Financial Manag. 2021, 14(4), 145; https://doi.org/10.3390/jrfm14040145 - 29 Mar 2021
Viewed by 555
Abstract
Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for [...] Read more.
Intraday high-frequency data of stock returns exhibit not only typical characteristics (e.g., volatility clustering and the leverage effect) but also a cyclical pattern of return volatility that is known as intraday seasonality. In this paper, we extend the stochastic volatility (SV) model for application with such intraday high-frequency data and develop an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for Bayesian inference of the proposed model. Our modeling strategy is two-fold. First, we model the intraday seasonality of return volatility as a Bernstein polynomial and estimate it along with the stochastic volatility simultaneously. Second, we incorporate skewness and excess kurtosis of stock returns into the SV model by assuming that the error term follows a family of generalized hyperbolic distributions, including variance-gamma and Student’s t distributions. To improve efficiency of MCMC implementation, we apply an ancillarity-sufficiency interweaving strategy (ASIS) and generalized Gibbs sampling. As a demonstration of our new method, we estimate intraday SV models with 1 min return data of a stock price index (TOPIX) and conduct model selection among various specifications with the widely applicable information criterion (WAIC). The result shows that the SV model with the skew variance-gamma error is the best among the candidates. Full article
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2020

Jump to: 2021, 2018

Article
Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure
J. Risk Financial Manag. 2020, 13(10), 231; https://doi.org/10.3390/jrfm13100231 - 28 Sep 2020
Viewed by 586
Abstract
Activity in the mining industry is based on the profitability principle similar to other business sectors. In the case of stone pits, gravel and sand quarries, it presents a very complex task, mainly due to the fact that the economy of localities is [...] Read more.
Activity in the mining industry is based on the profitability principle similar to other business sectors. In the case of stone pits, gravel and sand quarries, it presents a very complex task, mainly due to the fact that the economy of localities is influenced greatly by natural conditions, which cannot be changed. The presented contribution deals with the problem of how mining companies, realizing the surface extraction of construction materials, could be profitable in the future. The main research method of this contribution presents regression and correlation analyses with the goal of determining parameters with a decisive influence on the future economic development of the locality. A complex system of stone pit, gravel and sand quarries demanded discriminant analysis to evaluate individual localities with the goal of dividing them into profitable and not profitable localities. The results of the contribution divide localities of quarry mining among profitable or not profitable, serving for predicting the future development of the company, based on discriminant analysis. The results of maximally possible measures respect assumptions, enabling the correct application of such multivariate statistical methods. A further orientation of the research in an area of model creation for predicting the future development of the company is possible in the application of logistic regression and neuron nets. Full article
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Article
Stochastic Volatility and GARCH: Do Squared End-of-Day Returns Provide Similar Information?
J. Risk Financial Manag. 2020, 13(9), 202; https://doi.org/10.3390/jrfm13090202 - 07 Sep 2020
Viewed by 692
Abstract
The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50 indices, sampled [...] Read more.
The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and STOXX50 indices, sampled at 5-minute intervals, taken from the Oxford Man Realised Library. Both models demonstrate comparable performance and are correlated to a similar extent with the RV estimates, when measured by OLS. However, a crude variant of Corsi’s (2009) Heterogenous Auto-Regressive (HAR) model, applied to squared demeaned daily returns on the indices, appears to predict the daily RV of the series, better than either of the two base models. The base SV model was then enhanced by adding a regression matrix including the first and second moments of the demeaned return series. Similarly, the GARCH(1,1) model was augmented by adding a vector of demeaned squared returns to the mean equation. The augmented SV model showed a marginal improvement in explanatory power. This leads to the question of whether we need either of the two standard volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the indices in the sample. The paper thus explores whether simple rules of thumb match the volatility forecasting capabilities of more sophisticated models. Full article
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Article
Precious Metal Mutual Fund Performance Evaluation: A Series Two-Stage DEA Modeling Approach
J. Risk Financial Manag. 2020, 13(5), 87; https://doi.org/10.3390/jrfm13050087 - 30 Apr 2020
Cited by 4 | Viewed by 978
Abstract
This paper documents a new series two-stage data envelopment analysis (DEA) modeling framework for mutual fund performance evaluation in terms of operational and portfolio management efficiency that is implemented to a sample of precious metal mutual funds (PMMFs). In the first and second [...] Read more.
This paper documents a new series two-stage data envelopment analysis (DEA) modeling framework for mutual fund performance evaluation in terms of operational and portfolio management efficiency that is implemented to a sample of precious metal mutual funds (PMMFs). In the first and second stage, one-input/one-output and multi-input/one-output settings are used, respectively. In the light of the results, the funds assessed are inefficient in both operational and portfolio management process and in particular, they seem to be more inefficiently operated. The operational management efficiency is correlated with portfolio management efficiency and, therefore, sample funds should give more emphasis on their operational policies to ensure their success in the industry. The research framework may not only benefit PMMFs, but also funds of other classes to quantify their performance and improve their competitive advantages. Full article
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2018

Jump to: 2021, 2020

Article
Best Fitting Fat Tail Distribution for the Volatilities of Energy Futures: Gev, Gat and Stable Distributions in GARCH and APARCH Models
J. Risk Financial Manag. 2018, 11(2), 30; https://doi.org/10.3390/jrfm11020030 - 09 Jun 2018
Cited by 4 | Viewed by 1805
Abstract
Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an [...] Read more.
Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot markets for risk managers. Capturing conditional distribution, fat tails and price spikes properly is crucial to the correct measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH models under gev, gat and alpha-stable distributions. We also applied various VaR analyses, Gaussian, Historical and Modified (Cornish-Fisher) VaR, for each variable. Results suggest that the APARCH model largely outperforms the GARCH model, and gat distribution performs better in modeling fat tails in returns. Our results also indicate that the correct volatility level, in gat distribution, is higher than those suggested under normal distribution with rates of 56%, 45% and 67% for Natural Gas Futures, Brent Oil Futures and Heating Oil Futures, respectively. Implemented VaR analyses also support this conclusion. Additionally, VaR test results demonstrate that energy futures display riskier behavior than S&P 500 returns. Our findings suggest that for optimum risk management and trading strategies, risk managers should consider alternative distributions in their models. According to our results, prices in energy markets are wilder than the perception of normal distribution. In this regard, regulators and policy makers should enhance transparency and competitiveness in the energy markets to protect consumers. Full article
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