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Financial Markets, Financial Volatility and Beyond (Volume II)
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Financial Markets, Financial Volatility and Beyond (Volume II)

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Financial Markets".

Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 44465

Special Issue Editor

Dr. Ruipeng Liu

E-Mail Website
Guest Editor
Department of Finance, Deakin Business School, Deakin University, 221 Burwood Highway, Melbourne, VIC 3125, Australia
Interests: financial markets; long memory volatility modelling; multifractal processes; risk measurements and management; climate finance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my pleasure to invite you to submit papers for the upcoming Special Issue on “Financial Markets, Financial Volatility and Beyond”. Topics include but are not limited to empirical and theoretical asset pricing, financial markets, climate finance, financial modelling, volatility forecasting, fund management, risk measurements and instruments. Novel research on computational aspects in finance is also encouraged—for instance, heuristic techniques for financial market modelling, higher dimensional computation, big data and high frequency trading, etc.

Contributions focusing on interdisciplinary research are also welcome, for instance, approaches and methods explaining key elements of stylised facts of financial markets, market microstructure, financial contagion, behavioural finance, etc. Submissions from practitioners and regulators are also welcome.

Dr. Ruipeng Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • asset pricing
  • financial market modelling
  • fund management
  • climate finance
  • volatility
  • long memory
  • estimation and forecasting
  • risk measurement and management
  • derivatives
  • energy markets
  • interdisciplinary applications in finance

Published Papers (16 papers)

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Research

16 pages, 538 KiB  
Article
Agency Problem and Stock Returns: Combining Measures of Asset Growth and Gross Profit
by Jun Chen, Joseph Mohr and Ronald Rutherford
J. Risk Financial Manag. 2023, 16(7), 336; https://doi.org/10.3390/jrfm16070336 - 15 Jul 2023
Viewed by 928
Abstract
In this paper, we propose a new factor in predicting stock returns, after taking agency problems into account. Although intensive studies have focused on asset growth and profitability as factors in predicting future returns, very limited attention has been given to their interaction. [...] Read more.
In this paper, we propose a new factor in predicting stock returns, after taking agency problems into account. Although intensive studies have focused on asset growth and profitability as factors in predicting future returns, very limited attention has been given to their interaction. We construct a measure that combines both asset growth and scaled gross profit in a single measure (defined as AGGP, hereafter), by excluding the change in capital expenditures from gross profit. We demonstrate that our measure of profitability controls for the agency problem from managerial decisions in investment. Our results are also robust to the scaling issues raised by recent studies. Further, consistent with prior literature, our measure produces superior results in the full universe of CRSP stocks, but inferior results when applied to a subset of the 500 largest nonfinancial firms. This is consistent with the fact that those largest firms are less affected by the agency problem, leading to the failure of our new measure in predicting future returns among this subsample. In sum, our new measure sheds new lights on how to price agency issues, by providing a “cleaner” profitability measure free of agency costs and also lending supportive evidence to the mispricing explanation of the asset-growth effect. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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14 pages, 659 KiB  
Article
The Efficiency of Weekly Option Prices around Earnings Announcements
by Jonathan A. Milian
J. Risk Financial Manag. 2023, 16(5), 270; https://doi.org/10.3390/jrfm16050270 - 12 May 2023
Viewed by 1354
Abstract
This study examines the efficiency of weekly option prices around firms’ earnings announcements. With most of the largest firms now having options that expire on a weekly basis, option traders can hedge or speculate on earnings news using options that expire very close [...] Read more.
This study examines the efficiency of weekly option prices around firms’ earnings announcements. With most of the largest firms now having options that expire on a weekly basis, option traders can hedge or speculate on earnings news using options that expire very close to a firm’s earnings announcement date. For earnings announcements near an options expiration date, one can estimate a firm’s expected stock price move in response to its earnings news (i.e., its option implied earnings announcement move) as the price of its at-the-money straddle as a proportion of its stock price. This study tests whether differences between historical earnings announcement moves and option implied earnings announcement moves predict straddle returns. Through the analysis of portfolio returns and Fama–MacBeth regressions, this study finds that straddle returns are significantly higher (lower) when the historical earnings announcement move is high (low) relative to the option implied earnings announcement move. In contrast to prior research, this study does not find an association between straddle returns and historical volatility, historical earnings announcement volatility, implied volatility, or the difference between historical volatility and implied volatility. Overall, this study suggests that weekly straddle prices around earnings announcements are not optimally efficient. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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15 pages, 961 KiB  
Article
Impact of Financial Inclusion on India’s Economic Development under the Moderating Effect of Internet Subscribers
by Aman Pushp, Rahul Singh Gautam, Vikas Tripathi, Jagjeevan Kanoujiya, Shailesh Rastogi, Venkata Mrudula Bhimavarapu and Neha Parashar
J. Risk Financial Manag. 2023, 16(5), 262; https://doi.org/10.3390/jrfm16050262 - 03 May 2023
Cited by 1 | Viewed by 7756
Abstract
Financial inclusion is an emerging economic growth paradigm, especially in developing economies like India. It is an essential barometer for the all-encompassing growth of a country and its economy. However, there is still a debate regarding the effect of Financial Inclusion (FI) on [...] Read more.
Financial inclusion is an emerging economic growth paradigm, especially in developing economies like India. It is an essential barometer for the all-encompassing growth of a country and its economy. However, there is still a debate regarding the effect of Financial Inclusion (FI) on achieving sustainable development. This study aims to determine if FI helps achieve Sustainable Development Growth (SDG) in India and if internet subscribers significantly influence the connection between FI and SDG. Secondary data from 16 states and one UT in India have been collected for 2017–2019. Therefore, the sample data is recent and covers a large country span. The data source is NITI Aayog and PMFBY (“Pradhan Mantri Fasal Bhima Yojana”) reports. The findings of this research are that FI has a positively significant relationship with sustainable development goals (SDG) in India. However, when the internet subscribers are high, the FI’s positive association with SDG gets reduced. PMFBY and SDG have been used for the first time, along with internet subscribers as moderators. The outcome has direct policy implications for improving the nation’s financial inclusion and economic growth. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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16 pages, 610 KiB  
Article
Scalar Measures of Volatility and Dependence for the Multivariate Models with Applications to Asian Financial Markets
by Sangwhan Kim and Anil K. Bera
J. Risk Financial Manag. 2023, 16(4), 212; https://doi.org/10.3390/jrfm16040212 - 27 Mar 2023
Viewed by 1398
Abstract
The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and the pairwise linear dependence of a set of variables. However, the matrix itself is difficult to represent in a concise way, particularly in the context of multivariate [...] Read more.
The variance–covariance matrix is a multi-dimensional array of numbers, containing information about the individual variabilities and the pairwise linear dependence of a set of variables. However, the matrix itself is difficult to represent in a concise way, particularly in the context of multivariate autoregressive conditional heteroskedastic models. The common practice is to report the plots of k(k−1)/2 time-varying pairwise conditional covariances, where k is the number of markets (or assets) considered; thus, when k=10, there will be 45 graphs. We suggest a scalar measure of overall variabilities (and dependences) by summarizing all the elements in a variance–covariance matrix into a single quantity. The determinant of the covariance matrix Σ, called the generalized variance, can be used as a measure of overall spread of the multivariate distribution. Similarly, the positive square root of the determinant |R| of the correlation matrix, called the scatter coefficient, will be a measure of linear independence among the random variables, while collective correlation+(1−|R|)1/2 will be an overall measure of linear dependence. In an empirical application to the six Asian market returns, these statistics perform the intended roles successfully. In addition, these are shown to be able to reveal and explain the empirical facts that cannot be uncovered by the traditional methods. In particular, we show that both the contagion and interdependence (among the national equity markets) are present and could be quantitatively measured in contrast to previous studies, which revealed only market interdependence. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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48 pages, 2198 KiB  
Article
Assessing the Use of Gold as a Zero-Beta Asset in Empirical Asset Pricing: Application to the US Equity Market
by Muhammad Abdullah, Hussein A. Abdou, Christopher Godfrey, Ahmed A. Elamer and Yousry Ahmed
J. Risk Financial Manag. 2023, 16(3), 204; https://doi.org/10.3390/jrfm16030204 - 15 Mar 2023
Cited by 1 | Viewed by 4051
Abstract
This paper examines the use of the return on gold instead of treasury bills in empirical asset pricing models for the US equity market. It builds upon previous research on the safe-haven, hedging, and zero-beta characteristics of gold in developed markets and the [...] Read more.
This paper examines the use of the return on gold instead of treasury bills in empirical asset pricing models for the US equity market. It builds upon previous research on the safe-haven, hedging, and zero-beta characteristics of gold in developed markets and the close relationship between interest rates, stock, and gold returns. In particular, we extend this research by showing that using gold as a zero-beta asset helps to improve the time-series performance of asset pricing models when pricing US equities and industries between 1981 and 2015. The performance of gold zero-beta models is also compared with traditional empirical factor models using the 1-month Treasury bill rate as the risk-free rate. Our results indicate that using gold as a zero-beta asset leads to higher R-squared values, lower Sharpe ratios of alphas, and fewer significant pricing errors in the time-series analysis. Similarly, the pricing of small stock and industry portfolios is improved. In cross-section, we also find improved results, with fewer cross-sectional pricing errors and more economically meaningful pricing of risk factors. We also find that a zero-beta gold factor constructed to be orthogonal to the Carhart four factors is significant in cross-section and helps to improve factor model performance on momentum portfolios. Furthermore, the Fama–French three- and five-factor asset pricing models and the Carhart model are all improved by these means, particularly on test assets which have been poorly priced by the traditional versions. Our results have salient implications for policymakers, governments, central bank rate-setting decisions, and investors. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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15 pages, 593 KiB  
Article
The Impact of Uncertainty in Macroeconomic Variables on Stock Returns in the USA
by Leonardo Iania, Robbe Collage and Michiel Vereycken
J. Risk Financial Manag. 2023, 16(3), 189; https://doi.org/10.3390/jrfm16030189 - 10 Mar 2023
Cited by 1 | Viewed by 3164
Abstract
In this research paper, the impact of macroeconomic uncertainty on stock returns in the United States of America is examined. To measure this macroeconomic uncertainty, a survey of Consensus Economics with data ranging from 1989 until 2019 was employed. The survey consists of [...] Read more.
In this research paper, the impact of macroeconomic uncertainty on stock returns in the United States of America is examined. To measure this macroeconomic uncertainty, a survey of Consensus Economics with data ranging from 1989 until 2019 was employed. The survey consists of monthly forecasts for several macroeconomic variables for multiple countries. Four uncertainty measures were developed, based on the standard deviation, interquartile range, high-minus-low and an AR- and GARCH model. By performing linear regressions, a positive relationship between macroeconomic uncertainty and stock returns was identified for, on average, 13 out of 49 sectors, which is consistent with economic theory. Furthermore, the standard deviation of stock returns was regressed on macroeconomic uncertainty. A positive relationship was found for, on average, 41.7 out of 49 sectors. The results are discussed at a general level, at the level of the macroeconomic variables and at the sector level. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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16 pages, 360 KiB  
Article
Geopolitical Risks and Yield Dynamics in the Australian Sovereign Bond Market
by Milan Christian De Wet
J. Risk Financial Manag. 2023, 16(3), 144; https://doi.org/10.3390/jrfm16030144 - 22 Feb 2023
Cited by 3 | Viewed by 2460
Abstract
Geopolitical risks and shocks such as military conflicts, terrorist attacks, and war tensions are known to cause significant economic downturns. The main purpose of this paper is to determine the dynamics between Australian sovereign bond yields and geopolitical risk. This is achieved by [...] Read more.
Geopolitical risks and shocks such as military conflicts, terrorist attacks, and war tensions are known to cause significant economic downturns. The main purpose of this paper is to determine the dynamics between Australian sovereign bond yields and geopolitical risk. This is achieved by employing a quantile regression analysis. The findings of this study indicate that the impact of geopolitical risk on Australian sovereign yield dynamics is asymmetrical. Furthermore, an increase in geopolitical risk only impacts short-term yields at extreme regimes. However, the impact is, by and large, insignificant. On the other hand, an increase in geopolitical risk does have a statistically significant positive impact on medium- and long-term yields across most quantiles. Lastly, an increase in geopolitical risk tends to result in a steeper yield curve at the belly of the curve but causes the yield curve to flatten at the long end. This study is the first study that holistically examines the dynamics between geopolitical risk and Australian sovereign bond yields. The study thereby contributes to the body of knowledge on Australian bond yields, specifically, and adds to the sparse body of knowledge on the dynamics between geopolitical risk and sovereign bond yields. The findings of this study have implications for monetary policy makers, given that shifts in sovereign bond yields could impact all three core mandates of the Australian Reserve Bank. Furthermore, changes in the slope of the yieldcurve could be used by monetary policy makers to pre-empt changes in future economic growth. The results of this study also relate to fiscal policy formulation, given that yields directly impact the cost of government borrowing. Lastly, portfolio managers could benefit from the results of this study, as these results provide information on the ability of Australian sovereign bonds to hedge against geopolitical risk. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
18 pages, 5195 KiB  
Article
The Moderating Effect of Financial Knowledge on Financial Risk Tolerance
by John E. Grable and Abed Rabbani
J. Risk Financial Manag. 2023, 16(2), 137; https://doi.org/10.3390/jrfm16020137 - 17 Feb 2023
Cited by 2 | Viewed by 3494
Abstract
The purpose of this paper is to describe a study that was designed to determine to what extent subjective and objective measures of financial knowledge moderate the relationship between an investor’s financial risk tolerance and demographic factors thought to be important descriptors of [...] Read more.
The purpose of this paper is to describe a study that was designed to determine to what extent subjective and objective measures of financial knowledge moderate the relationship between an investor’s financial risk tolerance and demographic factors thought to be important descriptors of an investor’s willingness to take a financial risk. It was determined that those who identified as male, and those with more attained education and income, exhibited higher investment risk tolerance (IRT). Subjective financial knowledge (SFK) was positively associated with IRT. The relationship between gender and IRT was moderated by SFK, whereas the relationship between IRT and age was moderated by objective financial knowledge (OFK). A positive relationship between education and IRT was noted, but the relationship was moderated by OFK, whereas the association between IRT and household income was moderated by SFK. Findings from this study indicate that while SFK and OFK are positively correlated, they are not measuring the same underlying construct, and as such, each moderates IRT relationships differently. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
13 pages, 414 KiB  
Article
Impact of Financial Distress on the Dividend Policy of Banks in India
by Anureet Virk Sidhu, Pooja Jain, Satyendra Pratap Singh, Jagjeevan Kanoujiya, Aashi Rawal, Shailesh Rastogi and Venkata Mrudula Bhimavarapu
J. Risk Financial Manag. 2023, 16(2), 107; https://doi.org/10.3390/jrfm16020107 - 09 Feb 2023
Cited by 3 | Viewed by 2449
Abstract
The present study primarily examines the impact of financial distress (FD) on the dividend policy of 33 banks working in the Indian economy from 2010 to 2019. In addition, we further explore the association between financial distress and dividend policy under the influence [...] Read more.
The present study primarily examines the impact of financial distress (FD) on the dividend policy of 33 banks working in the Indian economy from 2010 to 2019. In addition, we further explore the association between financial distress and dividend policy under the influence of shareholder activism (SHA). Using the static panel data regression technique, it is revealed that financial distress is non-linearly associated with the dividend policy of banks in an inverted U-shape. In the initial phase of a distressing situation, banks tend to have a liberal dividend policy. However, after reaching the pressure point, the banks start to squeeze dividend distribution to the stakeholders. Furthermore, the significant impact of shareholder activism has been found in the association between financial distress and the dividend payout policy of banks. From the policy perspective, the study will provide the policymakers with a clear all-round perspective of distressing situations, as the current research involves exploring the impact of distress on the dividend policy that will help the experts in basically understanding the adverse effect of financial distress and the repercussions, respectively, on the earning of the shareholders. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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13 pages, 1072 KiB  
Article
Three Major Crises and Asian Emerging Market Informational Efficiency: A Case of Pakistan Stock Exchange-100 Index
by Bahrawar Said, Shafiq Ur Rehman and Muhammad Wajid Raza
J. Risk Financial Manag. 2022, 15(12), 619; https://doi.org/10.3390/jrfm15120619 - 19 Dec 2022
Cited by 1 | Viewed by 2039
Abstract
Periods of economic turmoil distort the ability of stock prices to reflect the available information. In the last three decades, emerging markets experienced numerous crises. The major three of them are the Asian Financial Crisis (1997–1998), Global Financial Crisis (2007–2009) and Global Pandemic [...] Read more.
Periods of economic turmoil distort the ability of stock prices to reflect the available information. In the last three decades, emerging markets experienced numerous crises. The major three of them are the Asian Financial Crisis (1997–1998), Global Financial Crisis (2007–2009) and Global Pandemic Crisis (2020–2022). The nature, intensity and duration of these crises differ significantly. This study investigates the impact of these varying natures of crises on the level of informational efficiency. The empirical evidence is based on the emerging stock market of Pakistan. Index-level data are collected from Pakistan Stock Exchange-100 Index for the period 1995–2022. The rebalancing is done each year to ensure that the final sample is composed of only 100 stocks with the highest market capitalization. The results based on the Variance Ratio (VR) test show that informational efficiency is time-varying. Among all the three crises, informational efficiency deters more in the COVID-19 pandemic, albeit the market efficiency recovers soon. This implies that the arbitrage opportunity is marginal in crisis periods, while investors prefer to invest in post-crisis periods. Finally, our results reveal that among all the crises, investors were more informed in the Global Financial Crisis. Investors must keep a close eye on market regimes for designing investment solutions. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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12 pages, 531 KiB  
Article
Forecasting Detrended Volatility Risk and Financial Price Series Using LSTM Neural Networks and XGBoost Regressor
by Aistis Raudys and Edvinas Goldstein
J. Risk Financial Manag. 2022, 15(12), 602; https://doi.org/10.3390/jrfm15120602 - 13 Dec 2022
Cited by 2 | Viewed by 1716
Abstract
It is common practice to employ returns, price differences or log returns for financial risk estimation and time series forecasting. In De Prado’s 2018 book, it was argued that by using returns we lose memory of time series. In order to verify this [...] Read more.
It is common practice to employ returns, price differences or log returns for financial risk estimation and time series forecasting. In De Prado’s 2018 book, it was argued that by using returns we lose memory of time series. In order to verify this statement, we examined the differences between fractional differencing and logarithmic transformations and their impact on data memory. We employed LSTM (long short-term memory) recurrent neural networks and an XGBoost regressor on the data using those transformations. We forecasted risk (volatility) and price value and compared the results of all models using original, unmodified prices. From the results, models showed that, on average, a logarithmic transformation achieved better volatility predictions in terms of mean squared error and accuracy. Logarithmic transformation was the most promising transformation in terms of profitability. Our results were controversial to Marco Lopez de Prado’s suggestion, as we managed to achieve the most accurate volatility predictions in terms of mean squared error and accuracy using logarithmic transformation instead of fractional differencing. This transformation was also most promising in terms of profitability. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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18 pages, 305 KiB  
Article
Financial Literacy Levels among Saudi Citizens across Budgeting, Saving, Investment, Debt, and Insurance Dimensions
by Naseem Al Rahahleh
J. Risk Financial Manag. 2022, 15(12), 582; https://doi.org/10.3390/jrfm15120582 - 06 Dec 2022
Cited by 5 | Viewed by 4435
Abstract
This paper provides a comprehensive account of financial literacy among Saudi citizens. Responses to items about key aspects of financial literacy—i.e., budgeting, debt, saving, investment, and insurance—were elicited from a large sample of Saudi citizens through an online survey. The data from 887 [...] Read more.
This paper provides a comprehensive account of financial literacy among Saudi citizens. Responses to items about key aspects of financial literacy—i.e., budgeting, debt, saving, investment, and insurance—were elicited from a large sample of Saudi citizens through an online survey. The data from 887 completed surveys were subjected to a descriptive analysis, a T-test, and ANOVA. A high level of financial literacy was found among the respondents in relation to budgeting, debt, and saving, but only a moderate level in relation to investment and insurance. The study findings indicate that current efforts on the part of government agencies, schools, universities, and financial institutions focused on budgeting, debt, and saving should continue and that more attention should be paid to educating Saudi citizens in relation to investment and insurance. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
18 pages, 916 KiB  
Article
Regional Economic and Financial Interconnectedness and the Impact of Sanctions: The Case of the Commonwealth of Independent States
by Mirzosaid Sultonov
J. Risk Financial Manag. 2022, 15(12), 565; https://doi.org/10.3390/jrfm15120565 - 29 Nov 2022
Cited by 4 | Viewed by 1638
Abstract
The war in Ukraine and the direct and indirect political, economic and financial involvement of many countries worldwide in this conflict demonstrates the difficult process of developing the new world order. Over 10,000 sanctions have already been imposed on Russia by the United [...] Read more.
The war in Ukraine and the direct and indirect political, economic and financial involvement of many countries worldwide in this conflict demonstrates the difficult process of developing the new world order. Over 10,000 sanctions have already been imposed on Russia by the United States, the European Union and their allies. Many countries are significantly affected by sanctions regardless of whether they are imposing them, being targeted by them, or have economic and trade partnerships with either—or both—of the sides. Commonwealth of Independent States (CIS) countries have been significantly affected by sanctions related to the Russian–Ukrainian war. Seasonally adjusted real quarterly time series, including gross domestic product and external trade, monthly nominal exchange rate time series, exogenous dummy variables for sanctions, and a combination of the vector autoregressive model and the Granger causality test were used in the estimations. We demonstrate how sanctions have affected the Russian economy and foreign exchange market and how their impact may spill over to the economies and foreign exchange markets of other CIS countries. Based on the research findings and contemporary political and economic conditions in the region and the world, we make suggestions helpful for improving the international economic and trade policies of the CIS countries. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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21 pages, 6421 KiB  
Article
Predicting Volatility Based on Interval Regression Models
by Hui Qu and Mengying He
J. Risk Financial Manag. 2022, 15(12), 564; https://doi.org/10.3390/jrfm15120564 - 29 Nov 2022
Viewed by 2326
Abstract
Considering the inferior volatility tracking capability of the point-data-based models, we propose using the more informative price interval data and building interval regression models for volatility forecasting. To characterize the heterogeneity of the market and the nonlinearity of volatility, we incorporated the heterogeneous [...] Read more.
Considering the inferior volatility tracking capability of the point-data-based models, we propose using the more informative price interval data and building interval regression models for volatility forecasting. To characterize the heterogeneity of the market and the nonlinearity of volatility, we incorporated the heterogeneous autoregressive structure and the Markov regime switching structure in the benchmark interval regression model, respectively, and thus propose three extended models. Our empirical examination on S&P 500 index shows that: (1) the proposed interval regression models significantly improve the volatility prediction accuracy compared to the point-data-based GARCH model. (2) Incorporating the heterogeneous structure significantly improves the volatility prediction accuracy, and the corresponding models significantly outperform the range-based ECARR model. (3) Incorporating the Markov regime switching structure improves the prediction performance, and the improvement is significant when the heterogeneous structure is characterized. The above results are robust under different market conditions, including the extremely volatile periods. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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20 pages, 512 KiB  
Article
The Asymmetric Overnight Return Anomaly in the Chinese Stock Market
by Yahui An, Lin Huang and Youwei Li
J. Risk Financial Manag. 2022, 15(11), 534; https://doi.org/10.3390/jrfm15110534 - 16 Nov 2022
Viewed by 1908
Abstract
Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that [...] Read more.
Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that overnight return is significantly negative, contradicts the risk–return trade-off. We find that this anomaly is asymmetrical, as the overnight return is significantly negative after a negative daytime return, whereas the anomaly does not occur following a positive daytime return. We explain this anomaly from the perspective of investor attention. We show that the attention of individual investors behaves asymmetrically such that they draw more attention on negative daytime returns, and play an essential role in explaining the overnight return puzzle. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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12 pages, 382 KiB  
Article
Efficient Pricing of Spread Options with Stochastic Rates and Stochastic Volatility
by Alexis Levendis and Eben Maré
J. Risk Financial Manag. 2022, 15(11), 504; https://doi.org/10.3390/jrfm15110504 - 31 Oct 2022
Cited by 2 | Viewed by 1970
Abstract
Spread options are notoriously difficult to price without the use of Monte Carlo simulation. Some strides have been made in recent years through the application of Fourier transform methods; however, to date, these methods have only been applied to specific underlying processes including [...] Read more.
Spread options are notoriously difficult to price without the use of Monte Carlo simulation. Some strides have been made in recent years through the application of Fourier transform methods; however, to date, these methods have only been applied to specific underlying processes including two-factor geometric Brownian motion (gBm) and three-factor stochastic volatility models. In this paper, we derive the characteristic function for the two-asset Heston–Hull–White model with a full correlation matrix and apply the two-dimensional fast Fourier transform (FFT) method to price equity spread options. Our findings suggest that the FFT is up to 50 times faster than Monte Carlo and yields similar accuracy. Furthermore, stochastic interest rates can have a material impact on long-dated out-of-the-money spread options. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond (Volume II))
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