Journal of Risk and Financial Management

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24 pages, 1147 KiB

Open AccessArticle

Evidence of Economic Policy Uncertainty and COVID-19 Pandemic on Global Stock Returns

by Thomas Chinan Chiang

J. Risk Financial Manag. 2022, 15(1), 28; https://doi.org/10.3390/jrfm15010028 - 10 Jan 2022

Cited by 15 | Viewed by 4510

This paper examines the impact of changes in economic policy uncertainty (EPU) and COVID-19 shock on stock returns. Tests of 16 global stock market indices, using monthly data from January 1990 to August 2021, suggest a negative relation between the stock return and [...] Read more.

This paper examines the impact of changes in economic policy uncertainty (EPU) and COVID-19 shock on stock returns. Tests of 16 global stock market indices, using monthly data from January 1990 to August 2021, suggest a negative relation between the stock return and a country’s EPU. Evidence suggests that a rise in the U.S. EPU causes not only a decline in a country’s stock return, but also a negative spillover effect on the global market; however, we cannot find a comparable negative effect from global EPU to U.S. stocks. Evidence suggests that the COVID-19 pandemic has a negative impact that significantly affects stock return worldwide. This study also finds an indirect COVID-19 impact that runs through a change in domestic EPU and, in turn, affects stock return. Evidence shows significant COVID-19 effects that change relative stock returns between the U.S. and global markets, creating a decoupling phenomenon. Full article

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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13 pages, 1113 KiB

Open AccessArticle

External Shocks and Volatility Overflow among the Exchange Rate of the Yen, Nikkei, TOPIX and Sectoral Stock Indices

by Mirzosaid Sultonov

J. Risk Financial Manag. 2021, 14(11), 560; https://doi.org/10.3390/jrfm14110560 - 19 Nov 2021

Cited by 3 | Viewed by 2083

Abstract

In this paper, we examined the changes in volatility overflow among the exchange rate of the Japanese yen (JPY), the Nikkei Stock Average (Nikkei), the Tokyo Stock Price Index (TOPIX) and the TOPIX sectoral indices for the period of 10 February 2016 to [...] Read more.

In this paper, we examined the changes in volatility overflow among the exchange rate of the Japanese yen (JPY), the Nikkei Stock Average (Nikkei), the Tokyo Stock Price Index (TOPIX) and the TOPIX sectoral indices for the period of 10 February 2016 to 24 March 2017. We employed the exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model, the cross-correlation function, and the daily logarithmic returns of JPY, Nikkei, TOPIX and the TOPIX components with a weight of 5% and more in estimations (banks, chemicals, electric appliances, information and communication, machinery and transportation equipment indices). The findings highlighted causality in variance (volatility spillover) among the variables. We revealed that volatility could also spread indirectly among the variables (from one variable to another through a third variable). We demonstrated how the impact of news about the results of the Brexit referendum (BR) and the United States presidential election (USE) in 2016 might spread among the variables indirectly within a week. Full article

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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31 pages, 901 KiB

Open AccessArticle

Spillovers and Asset Allocation

by Lai T. Hoang and Dirk G. Baur

J. Risk Financial Manag. 2021, 14(8), 345; https://doi.org/10.3390/jrfm14080345 - 27 Jul 2021

Cited by 4 | Viewed by 2238

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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15 pages, 1421 KiB

Open AccessArticle

Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

by Najam Iqbal, Muhammad Saqib Manzoor and Muhammad Ishaq Bhatti

J. Risk Financial Manag. 2021, 14(7), 314; https://doi.org/10.3390/jrfm14070314 - 8 Jul 2021

Cited by 15 | Viewed by 4618

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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17 pages, 1588 KiB

Open AccessArticle

Forecasting Volatility and Tail Risk in Electricity Markets

by Antonio Naimoli and Giuseppe Storti

J. Risk Financial Manag. 2021, 14(7), 294; https://doi.org/10.3390/jrfm14070294 - 26 Jun 2021

Cited by 3 | Viewed by 2072

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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32 pages, 1072 KiB

Open AccessArticle

How Much Do Negative Probabilities Matter in Option Pricing?: A Case of a Lattice-Based Approach for Stochastic Volatility Models

by Chung-Li Tseng, Daniel Wei-Chung Miao, San-Lin Chung and Pai-Ta Shih

J. Risk Financial Manag. 2021, 14(6), 241; https://doi.org/10.3390/jrfm14060241 - 30 May 2021

Cited by 1 | Viewed by 2585

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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28 pages, 1293 KiB

Open AccessArticle

Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects

by Zhongxian Men, Tony S. Wirjanto and Adam W. Kolkiewicz

J. Risk Financial Manag. 2021, 14(5), 225; https://doi.org/10.3390/jrfm14050225 - 18 May 2021

Cited by 1 | Viewed by 2417

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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29 pages, 1388 KiB

Open AccessArticle

Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors

by Makoto Nakakita and Teruo Nakatsuma

J. Risk Financial Manag. 2021, 14(4), 145; https://doi.org/10.3390/jrfm14040145 - 29 Mar 2021

Cited by 6 | Viewed by 3446

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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14 pages, 245 KiB

Open AccessArticle

Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure

by Adriana Csikosova, Maria Janoskova and Katarina Culkova

J. Risk Financial Manag. 2020, 13(10), 231; https://doi.org/10.3390/jrfm13100231 - 28 Sep 2020

Cited by 7 | Viewed by 2831

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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Graphical abstract

25 pages, 1245 KiB

Open AccessArticle

Stochastic Volatility and GARCH: Do Squared End-of-Day Returns Provide Similar Information?

by David Edmund Allen

J. Risk Financial Manag. 2020, 13(9), 202; https://doi.org/10.3390/jrfm13090202 - 7 Sep 2020

Cited by 2 | Viewed by 3971

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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13 pages, 523 KiB

Open AccessArticle

Precious Metal Mutual Fund Performance Evaluation: A Series Two-Stage DEA Modeling Approach

by Ioannis E. Tsolas

J. Risk Financial Manag. 2020, 13(5), 87; https://doi.org/10.3390/jrfm13050087 - 30 Apr 2020

Cited by 15 | Viewed by 4665

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

(This article belongs to the Special Issue Volatility Modelling and Forecasting)

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