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Editorial Board Members’ Collection Series: Journal of Risk and Financial Management

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (1 September 2024) | Viewed by 43408

Special Issue Editor

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue titled ‘Editorial Board Members' Collection Series: Journal of Risk and Financial Management’. It will be a collection of papers from researchers invited exclusively by the Editorial Board Members. The aim is to provide an avenue for networking and communication between JRFM and scholars in the field of financial and economic risk and management. All papers will be published with fully open access after a through peer-review process.

Areas of interest for the collection

  • Banking
  • Financial markets
  • International finance
  • Financial economics
  • Mathematical methods in economics and finance
  • Risk management and analysis
  • Financial technology and innovation
  • Corporate finance
  • Entrepreneurial finance
  • Financial accounting and reporting
  • Sustainable and environmental finance
  • Energy economics and finance
  • Tourism: economics, finance, and management

Prof. Dr. Thanasis Stengos
Guest Editor

Manuscript Submission Information

The Journal of Risk and Financial Management is an international peer-reviewed open access semimonthly journal published by MDPI.

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 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 a short abstract (about 100 words) can be sent to the Editorial Office for announcement on the 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; please visit this site before submitting a manuscript.

Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and written articulately in English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (21 papers)

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Research

11 pages, 289 KiB  
Article
Economic Freedom, Budget Deficits, and Perceived Risk from Larger National Debt-to-GDP Ratios: An Exploratory Analysis of Their Real Interest Rate Effects
by Richard J. Cebula
J. Risk Financial Manag. 2024, 17(10), 469; https://doi.org/10.3390/jrfm17100469 - 17 Oct 2024
Viewed by 855
Abstract
Since the early 1980s, there have been a number of principally empirical studies of the impact of government budget deficits on interest rates that have typically tested the hypothesis that larger deficits raise interest rates. However, in more recent years, this topic has [...] Read more.
Since the early 1980s, there have been a number of principally empirical studies of the impact of government budget deficits on interest rates that have typically tested the hypothesis that larger deficits raise interest rates. However, in more recent years, this topic has received far less attention. Accordingly, this study seeks to “update” the findings of such studies and to do so for the dominant North American economies of Canada and the U.S. Furthermore, in the pursuit of further insights into interest rates, the present study also investigates an effectively heretofore overlooked variable that arguably also might influence interest rates, namely, economic freedom. Finally, given the increased upward trend of government debt (relative to GDP) in recent years in Canada and the U.S., this study investigates the interest rate impact of rising national debt-to-GDP ratios. For the 1995–2024 period (and also in one estimate for the 1985–2001 period), this exploratory study finds compelling evidence (1) that the real interest rate yield on 10-year Treasuries in Canada and the real interest rate yield on 10-year U.S. Treasury notes are increasing functions of the central government budget deficits in both Canada and the U.S., respectively, and (2) the real interest rate yields on 10-year Treasuries in Canada and 10-year U.S. Treasury notes are both decreasing functions of economic freedom in Canada and the U.S., respectively. On the other hand, regarding the impact of a higher national debt-to-GDP ratio on the real ten-year Treasury yield, there is only very mixed support for an impact, with support for its impact coming from the Canadian estimates but no support whatsoever coming from the U.S. estimates. Full article
34 pages, 4342 KiB  
Article
Constructing Divisia Monetary Aggregates for the Asian Tigers
by William A. Barnett, JongSoo Lee and Naowar Mohiuddin
J. Risk Financial Manag. 2024, 17(10), 435; https://doi.org/10.3390/jrfm17100435 - 29 Sep 2024
Viewed by 1102
Abstract
This study constructs Divisia monetary aggregates for the “Asian Tigers”—Hong Kong (1999–2024), South Korea (2009–2024), Singapore (1991–2021), and Taiwan (2005–2024)—and assesses whether Divisia monetary aggregates explain nominal GDP better than simple-sum money. Our findings demonstrate that Divisia indices respond more sensitively to economic [...] Read more.
This study constructs Divisia monetary aggregates for the “Asian Tigers”—Hong Kong (1999–2024), South Korea (2009–2024), Singapore (1991–2021), and Taiwan (2005–2024)—and assesses whether Divisia monetary aggregates explain nominal GDP better than simple-sum money. Our findings demonstrate that Divisia indices respond more sensitively to economic shocks. For Hong Kong and Taiwan, narrow Divisia money provides the best explanations for fluctuations in nominal GDP. Our results suggest that Divisia monetary aggregates can be beneficial for monetary policy analysis in these territories and underscore the importance of further research into the empirical performance of Divisia monetary aggregates in macroeconomic prediction. Full article
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<p>Nominal GDP per capita of the Asian Tigers (1950–2022). Source: Bolt and Van Zanden-Maddison Project Database 2023. Note: These data are adjusted for inflation and differences in the cost of living and are expressed in international dollars at 2011 prices.</p>
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<p>Divisia versus simple-sum monetary aggregates for the four Asian tigers.</p>
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<p>Divisia versus simple-sum growth rates for the four Asian tigers.</p>
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<p>FEVD Plot for GDP and M1.</p>
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<p>FEVD Plot for GDP and DM1.</p>
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<p>FEVD Plot for GDP and DM1A.</p>
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<p>Forecast Performance Metrics for Hong Kong.</p>
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<p>Forecast Performance Metrics for Taiwan.</p>
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26 pages, 517 KiB  
Article
The NGDOs Efficiency: A PROMETHEE Approach
by Susana Álvarez-Otero and Emma Álvarez-Valle
J. Risk Financial Manag. 2024, 17(9), 382; https://doi.org/10.3390/jrfm17090382 - 26 Aug 2024
Viewed by 714
Abstract
The current economic and political crisis has brought about a change in the environment in which non-governmental development organisations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds they receive and an increase in the population [...] Read more.
The current economic and political crisis has brought about a change in the environment in which non-governmental development organisations (NGDOs) have traditionally operated. This change can be summed up as a reduction in the funds they receive and an increase in the population they must serve. The need then arises to have mechanisms that allow an analysis of the good work performed by the NGDOs. Knowing the efficiency of the NGDOs in the management of their previous projects can contribute towards improving their future achievements. The aim of this research is to establish some objective indicators that allow an evaluation of the efficiency of these organisations. Firstly, a detailed analysis of the regulation of the three agencies is conducted (Spanish-AECID, European-EuropeAid, and American-USAID). This allows us to synthesise the indicators of good performance of the NGDO based on the study of the eligibility criteria of public donors. The research concludes with the study of the efficiency following the Promethee Approach. Our results reveal that 44.6% of the NGDOs (33 out of the 74 studied) operate inefficiently, compared to 29.7%, which are efficient. Full article
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Figure A1
<p>Classification of the NGDO according to their efficiency.</p>
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21 pages, 2456 KiB  
Article
One Man’s Bubble Is Another Man’s Rational Behavior: Comparing Alternative Macroeconomic Hypotheses for the US Housing Market
by Anastasios G. Malliaris, Mary Malliaris and Mark S. Rzepczynski
J. Risk Financial Manag. 2024, 17(8), 349; https://doi.org/10.3390/jrfm17080349 - 12 Aug 2024
Cited by 1 | Viewed by 702
Abstract
Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a [...] Read more.
Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues. Full article
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<p>H1. Case-Shiller National Home Price Index NSA and macro variables sensitivity. Period: Jan 1987-June 2022. Source FRED database: Consumer price index all (CPI) annualized change, Consumer price index rent annualized change (CPIR), Non-Farm payroll number (NFP), Industrial Production annualized change (IP), 30-year mortgage rate (MORT), macro news (MACRO), and Economic policy uncertainty Index (EPU). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 7 input nodes, 2 hidden layer nodes, 1 output node.</p>
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<p>H2. Case-Shiller National Home Price Index SA and other macro variable sensitivity. Period: Jan 1992-June 2022. Source FRED database: Durable goods (DGOOD), Disposable income (DINC), Trade balance (TBAL), Employment/population ratio (EMRATIO), Unemployment level (UNEMPLOY), Industrial production (IP). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 6 input nodes, 3 hidden layer nodes, 1 output node.</p>
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<p>H3. Case-Shiller National Home Price Index NSA monetary policy sensitivity. Period: Jan 1987-June 2022. Source FRED database: 30-year mortgage (MORT), Treasury 10-year/2-year spread (TSPREAD), Fed Funds (FFUND), EPU monetary index (EPUM). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 4 input nodes, 6 hidden layer nodes, 1 output node.</p>
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<p>H4. Case-Shiller National Home Price Index NSA monetary policy sensitivity. Period: Jan 1992-June 2022. Source FRED database: 30-year mortgage (MORT), Treasury 10-year/2-year spread (TSPREAD), Fed Funds (FFUND), EPU monetary index (EPUM), Fed assets (FEDA). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 5 input nodes, 2 hidden layer nodes, 1 output node.</p>
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<p>H5. Case-Shiller National Home Price Index NSA monetary policy sensitivity. Period: Jan 2003-June 2022. Source FRED database: 30-year mortgage (MORT), Trade Balance (TBAL), Fed Funds (FFUND), EPU monetary index (EPUM), Fed assets (FEDA). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 4 input nodes, 4 hidden layer nodes, 1 output node.</p>
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<p>H6. Case-Shiller National Home Price Index SA monetary policy sensitivity. Period: Jan 2003-June 2022. Source FRED database: 30-year mortgage (MORT), Trade Balance (TBAL), Fed Funds (FFUND), Fed assets (FEDA). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 4 input nodes, 3 hidden layer nodes, 1 output node.</p>
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<p>H7. Housing prices (NSA), micro supply and macro demand factors. Period: Jan 1987- June 2022. Source FRED database: Monthly supply of new houses (HSUPPLY), new single-family houses sold (HSOLD), single-family housing units completed (HCOMPL), industrial production (IP), and non-farm payroll (NFP). Variables are NSA. Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 5 input nodes, 4 hidden layer nodes, 1 output node.</p>
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<p>H8. Housing Prices (SA) and micro supply and demand factors. Period: Jan 1992-Jun 2002. Source FRED database: Monthly new houses started (HSTART), new single-family houses sold (HSOLD), single-family housing units completed (HCOMPL), trade balance (TBAL), disposable income (DINC), and durable goods (DGOOD). Variables are NSA. Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 6 input nodes, 3 hidden layer nodes, 1 output node.</p>
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<p>H9. Housing Prices micro supply and demand factors with expectations. Period: Feb 1990-June 2022. Source FRED database: Monthly non-farm payroll (NFP), housing units authorized but not started (HAUTH), single-family units started divided by the population level (HPOP), single-family houses sold (HSOLD), Kansas City financial stress index (STRESS), single-family housing units completed (HCOMPL), Michigan consumer sentiment (MCSENT), housing supply (HSUPPLY) and industrial production (IP). Variables are NSA. Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 9 input nodes, 5 hidden layer nodes, 1 output node.</p>
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<p>H10. Housing prices are driven by lagged prices. Period Jan 1992-June 2022. Source FRED database: Case-Shiller prices lagged 1 period (CSLAG1), Case–Shiller prices lagged 6 periods (CSLAG6), Michigan inflation expectations (MINFEX), housing units authorized but not started (HAUTH), single-family housing units completed (HCOMPL), Chicago Fed Financial conditions (CFINCON), Fed funds (FFUND), Michigan consumer sentiment (MCSENT), and single-family housing units completed (HCOMPL). Data scaled between 0 and 1 and sensitivities sum to 1. Network Architecture: 8 input nodes, 6 hidden layer nodes, 1 output node.</p>
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<p>CUSUM chart examples for H1 and H9 with upper and lower bounds for critical 0.05 level.</p>
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<p>CUSUM for with upper and lower bounds for the 0.05 critical level with matching start date of February 2003.</p>
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13 pages, 302 KiB  
Article
May 2024 Buy-Sell Guide for Dow Jones 30 Stocks and Modified Omega Criterion
by H. D. Vinod
J. Risk Financial Manag. 2024, 17(8), 343; https://doi.org/10.3390/jrfm17080343 - 8 Aug 2024
Viewed by 663
Abstract
We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that [...] Read more.
We study recent monthly data to help long-term investors buy or sell from the 30 Dow Jones Industrial Average (DJIA) Index components. The recommendations are based on six stock-picking algorithms and their average ranks. We explain the reasons for ignoring the claim that the Sharpe ratio algorithm lacks monotonicity. Since the version of “omega” in the literature uses weights that distort the actual gain–pain ratio faced by investors, we propose new weights. We use data from 30 stocks using the past 474 months (39+ years) of monthly closing prices, ending in May 2024. Our buy-sell recommendations also use newer “pandemic-proof” out-of-sample portfolio performance comparisons from the R package ‘generalCorr’. We report twelve sets of ranks for both out-of- and in-sample versions of the six algorithms. Averaging the twelve sets yields the top and bottom k stocks. For example, k=2 suggests buying Visa Inc. and Johnson & Johnson while selling Coca-Cola and Procter & Gamble. Full article
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<p>Mean–standard deviation efficiency frontier for Dow Jones 30 stocks.</p>
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<p>Empirical Cumulative Distribution Function for a Toy Example.</p>
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11 pages, 1092 KiB  
Communication
What’s Wrong with Enterprise Risk Management?
by John Fraser, Rob Quail and Betty Simkins
J. Risk Financial Manag. 2024, 17(7), 274; https://doi.org/10.3390/jrfm17070274 - 29 Jun 2024
Cited by 1 | Viewed by 1949
Abstract
Enterprise risk management (ERM) was introduced in the 1990s and has since become expected by boards of directors and regulators as a sign of good management and good corporate governance. However, many organizations struggle to implement ERM, and still seek practical advice on [...] Read more.
Enterprise risk management (ERM) was introduced in the 1990s and has since become expected by boards of directors and regulators as a sign of good management and good corporate governance. However, many organizations struggle to implement ERM, and still seek practical advice on ERM implementation. This article explains many of the reasons why organizations are unsuccessful in their efforts at implementation and provides practical solutions provided by an experienced risk manager and consultant, an ex-Chief Risk Officer, and an academic, all of whom have written extensively on the subject. This article should be of interest to practitioners involved in implementing ERM, to consultants in ERM, and to academics teaching courses on ERM, risk management, and related topics. This article also provides a base against which further future research can be performed as ERM best practices continue to evolve. Full article
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<p>Graphical representation of risks’ interconnectedness.</p>
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<p>Misuse of models.</p>
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27 pages, 1793 KiB  
Article
Action-Based Fiscal Consolidations and Economic Growth
by Markus Brueckner
J. Risk Financial Manag. 2024, 17(5), 194; https://doi.org/10.3390/jrfm17050194 - 8 May 2024
Viewed by 1391
Abstract
This paper tests the hypothesis that action-based fiscal consolidations have a negative effect on GDP growth. Using the IMF’s dataset on action-based fiscal consolidations, instrumental variables’ regressions show that action-based fiscal consolidations have a significant positive effect on GDP growth. The instrumental variables’ [...] Read more.
This paper tests the hypothesis that action-based fiscal consolidations have a negative effect on GDP growth. Using the IMF’s dataset on action-based fiscal consolidations, instrumental variables’ regressions show that action-based fiscal consolidations have a significant positive effect on GDP growth. The instrumental variables’ regressions also show that action-based fiscal consolidations significantly increase investment and productivity. The findings presented in this paper thus strongly reject the hypothesis that action-based fiscal consolidations reduce growth. The paper argues that least squares estimates presented in previous literature suffer from negative reverse causality bias: GDP growth has a significant positive effect on both the likelihood and the magnitude of action-based fiscal consolidations. To uncover causal effects of action-based fiscal consolidations, researchers need to use an instrumental variables approach. Full article
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<p>Estimated effect of a 1% of GDP fiscal consolidation on real GDP. Note: The figure shows estimates of the coefficient <span class="html-italic">β<sup>h</sup></span> in Equation (2). The letter <span class="html-italic">h</span> in the superscript refers to the horizon, in years. The solid lines in the above figure are the <span class="html-italic">β<sup>h</sup></span> coefficients obtained from instrumental variables’ regressions. The thick, long-dash-dotted lines are the <span class="html-italic">β<sup>h</sup></span> coefficients obtained from ordinary least squares regressions. The thin, tight-dotted lines are the 95% confidence bands.</p>
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17 pages, 870 KiB  
Article
Downside Risk in Australian and Japanese Stock Markets: Evidence Based on the Expectile Regression
by Kohei Marumo and Steven Li
J. Risk Financial Manag. 2024, 17(5), 189; https://doi.org/10.3390/jrfm17050189 - 2 May 2024
Viewed by 1271
Abstract
The expectile-based Value at Risk (EVaR) has gained popularity as it is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). This paper applies the expectile regression approach to evaluate the EVaR of stock market indices of Australia [...] Read more.
The expectile-based Value at Risk (EVaR) has gained popularity as it is more sensitive to the magnitude of extreme losses than the conventional quantile-based VaR (QVaR). This paper applies the expectile regression approach to evaluate the EVaR of stock market indices of Australia and Japan. We use an expectile regression model that considers lagged returns and common risk factors to calculate the EVaR for each stock market and to evaluate the interdependence of downside risk between the two markets. Our findings suggest that both Australian and Japanese stock markets are affected by their past development and the international stock markets. Additionally, ASX 200 index has significant impact on Nikkei 225 in terms of downside tail risk, while the impact of Nikkei 225 on ASX is not significant. Full article
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<p>The evolution of the stock indices during the sample period.</p>
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<p>In-sample <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>(</mo> <mi>θ</mi> <mo stretchy="false">|</mo> <msub> <mi mathvariant="script">F</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> </semantics></math> of ASX 200 index for <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0.0067</mn> <mspace width="3.33333pt"/> <mo>(</mo> <mi>α</mi> <mo>=</mo> <mn>0.01</mn> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.0254</mn> <mspace width="3.33333pt"/> <mo>(</mo> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> <mo>)</mo> </mrow> </semantics></math>. In-sample period: 1 October 2012 to 30 September 2023.</p>
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<p>In-sample <math display="inline"><semantics> <mrow> <mi>ν</mi> <mo>(</mo> <mi>θ</mi> <mo stretchy="false">|</mo> <msub> <mi mathvariant="script">F</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> </semantics></math> of Nikkei 225 index for <math display="inline"><semantics> <mrow> <mi>θ</mi> <mo>=</mo> <mn>0.0030</mn> <mspace width="3.33333pt"/> <mo>(</mo> <mi>α</mi> <mo>=</mo> <mn>0.01</mn> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.0213</mn> <mspace width="3.33333pt"/> <mo>(</mo> <mi>α</mi> <mo>=</mo> <mn>0.05</mn> <mo>)</mo> </mrow> </semantics></math>. In-sample period: 1 October 2012 to 30 September 2023.</p>
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<p>In-sample period: 1 October 2013 to 30 September 2020, the (<b>left</b>) side of the bar, and out-of-sample period: 1 October 2020 to 30 September 2023, the (<b>right</b>) side of the bar.</p>
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<p>In-sample period: 1 October 2013 to 30 September 2020, the (<b>left</b>) side of the bar, and out-of-sample period: 1 October 2020 to 30 September 2023, the (<b>right</b>) side of the bar.</p>
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<p>In-sample ES of ASX 200 under 0.01 and 0.05 quantiles.</p>
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<p>In-sample ES of Nikkei 225 under 0.01 and 0.05 quantiles.</p>
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25 pages, 441 KiB  
Article
Testing and Ranking of Asset Pricing Models Using the GRS Statistic
by Mark J. Kamstra and Ruoyao Shi
J. Risk Financial Manag. 2024, 17(4), 168; https://doi.org/10.3390/jrfm17040168 - 19 Apr 2024
Viewed by 1947
Abstract
We clear up an ambiguity in the statement of the GRS statistic by providing the correct formula of the GRS statistic and the first proof of its F-distribution in the general multiple-factor case. Casual generalization of the Sharpe-ratio-based interpretation of the single-factor GRS [...] Read more.
We clear up an ambiguity in the statement of the GRS statistic by providing the correct formula of the GRS statistic and the first proof of its F-distribution in the general multiple-factor case. Casual generalization of the Sharpe-ratio-based interpretation of the single-factor GRS statistic to the multiple-portfolio case makes experts in asset pricing studies susceptible to an incorrect formula. We illustrate the consequences of using the incorrect formulas that the ambiguity in GRS leads to—over-rejecting and misranking asset pricing models. In addition, we suggest a new approach to ranking models using the GRS statistic p-value. Full article
17 pages, 336 KiB  
Article
Knowledge Sharing and Cumulative Innovation in Business Networks
by Gilles Saint-Paul
J. Risk Financial Manag. 2024, 17(4), 137; https://doi.org/10.3390/jrfm17040137 - 26 Mar 2024
Viewed by 1256
Abstract
How can we explain the success of cooperative networks of firms which share innovations, such as Silicon Valley or the Open Source community? This paper shows that if innovations are cumulative, making an invention publicly available to a network of firms may be [...] Read more.
How can we explain the success of cooperative networks of firms which share innovations, such as Silicon Valley or the Open Source community? This paper shows that if innovations are cumulative, making an invention publicly available to a network of firms may be valuable if the firm expects to benefit from future improvements made by other firms. A cooperative equilibrium where all innovations are made public is shown to exist under certain conditions. Furthermore, such an equilibrium does not rest on punishment strategies being followed after a deviation: it is optimal not to deviate regardless of another firm’s actions following a deviation. A cooperative equilibrium is more likely to arise the greater the number of firms in the network. When R&D effort is endogenous, cooperative equilibria are associated with strategic complementarities between firms’ research effort, which may lead to multiple equilibria. Full article
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<p>Multiple equilibrium R&amp;D effort.</p>
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17 pages, 786 KiB  
Article
Segmenting Bitcoin Transactions for Price Movement Prediction
by Yuxin Zhang, Rajiv Garg, Linda L. Golden, Patrick L. Brockett and Ajit Sharma
J. Risk Financial Manag. 2024, 17(3), 128; https://doi.org/10.3390/jrfm17030128 - 21 Mar 2024
Cited by 1 | Viewed by 2315
Abstract
Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin [...] Read more.
Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, senders’ addresses, receivers’ addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies. Full article
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<p>Bitcoin price volatility and daily transaction volume.</p>
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<p>Number of transactions by block.</p>
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<p>Bitcoin market participants and transaction patterns.</p>
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<p>ROI comparison.</p>
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14 pages, 891 KiB  
Article
Analyzing Portfolio Optimization in Cryptocurrency Markets: A Comparative Study of Short-Term Investment Strategies Using Hourly Data Approach
by Sonal Sahu, José Hugo Ochoa Vázquez, Alejandro Fonseca Ramírez and Jong-Min Kim
J. Risk Financial Manag. 2024, 17(3), 125; https://doi.org/10.3390/jrfm17030125 - 20 Mar 2024
Cited by 2 | Viewed by 5028
Abstract
This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to [...] Read more.
This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, especially in shorter-term investment horizons, delivering optimal returns to investors. Additionally, our findings emphasize the importance of dynamic portfolio management, stressing the necessity of regular rebalancing in the volatile cryptocurrency market. Overall, this study offers valuable insights into optimizing cryptocurrency portfolios, providing practical guidance for investors and portfolio managers navigating this rapidly evolving market landscape. Full article
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<p>Bar charts representing the distribution of weights during holding periods with various rebalancing frequencies using the Sharpe ratio minimization strategy. Source: elaborated by the author.</p>
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<p>Bar charts representing the distribution of weights during holding and rebalancing periods using the kurtosis minimization strategy. Source: elaborated by the author.</p>
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<p>Comparison of portfolio returns behavior under different methodologies and strategies. Source: elaborated by the author.</p>
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14 pages, 1179 KiB  
Article
On the Realized Risk of Foreign Exchange Rates: A Fractal Perspective
by Masoumeh Fathi, Klaus Grobys and James W. Kolari
J. Risk Financial Manag. 2024, 17(2), 79; https://doi.org/10.3390/jrfm17020079 - 18 Feb 2024
Viewed by 2144
Abstract
While well-established literature argues that realized variances are close to a lognormal distribution, this study follows Benoit Mandelbrot by taking a fractal perspective. Using power laws to model realized foreign exchange rate variances, our findings indicate that power laws offer an alternative to [...] Read more.
While well-established literature argues that realized variances are close to a lognormal distribution, this study follows Benoit Mandelbrot by taking a fractal perspective. Using power laws to model realized foreign exchange rate variances, our findings indicate that power laws offer an alternative to the lognormal in terms of goodness-of-fit tests. Further, our analysis shows that estimated power law exponents for seven out of nine realized FX variances are α^<3, which indicates that the variance of realized variance is statistically undefined. We conclude that the foreign exchange rate market is far riskier than earlier believed. By implication, documented research in an enormous body of literature that draws conclusions from variance analyses stands on shaky grounds. Full article
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<p>Time series plot.</p>
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<p>Histogram plot.</p>
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<p>Lognormal QQ plot.</p>
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<p>Mean excess plot.</p>
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<p>Distribution comparison.</p>
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22 pages, 1437 KiB  
Article
Volatility and Herding Bias on ESG Leaders’ Portfolios Performance
by Nektarios Gavrilakis and Christos Floros
J. Risk Financial Manag. 2024, 17(2), 77; https://doi.org/10.3390/jrfm17020077 - 16 Feb 2024
Cited by 4 | Viewed by 4244
Abstract
We here analyze the factor loadings given by the CAPM, the Fama–French three (FF3), and the five-factor model (FF5), and test the performance and the validity of adding two more factors (volatility and dispersion of returns) to the FF5 factor model of European [...] Read more.
We here analyze the factor loadings given by the CAPM, the Fama–French three (FF3), and the five-factor model (FF5), and test the performance and the validity of adding two more factors (volatility and dispersion of returns) to the FF5 factor model of European index-based ESG leaders’ portfolios. Our ESG leaders’ portfolios generated significant negative alphas during 2012–2022, corroborating the literature’s negative argument. The negative abnormal returns of ESG leaders’ portfolios are homogeneous across the three ESG pillars. We conclude that European ESG leaders’ portfolios are biased toward large cap and value stocks with robust operating profitability and against aggressive investments. As robustness tests, we examine Global ESG leaders’ index-based portfolios, producing the same results but with reduced importance in some loading factors like profitability and investment strategy. Furthermore, we deduced that European and Global ESG leaders’ portfolios tilt towards volatility and herding bias. Full article
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<p>Cumulative returns of Europe ESG pillars leaders related to the benchmark (STOXX Europe 600).</p>
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<p>Cumulative returns of Global ESG pillars leaders related to the benchmark (STOXX Global 1800) index.</p>
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<p>Herding in STOXX Europe ESG leaders.</p>
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<p>Herding in STOXX Global ESG leaders.</p>
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17 pages, 1676 KiB  
Article
Option Pricing with the Logistic Return Distribution
by Haim Levy and Moshe Levy
J. Risk Financial Manag. 2024, 17(2), 67; https://doi.org/10.3390/jrfm17020067 - 10 Feb 2024
Viewed by 1790
Abstract
The Black–Scholes model and many of its extensions imply a log-normal distribution of stock total returns over any finite holding period. However, for a holding period of up to one year, empirical stock return distributions (both conditional and unconditional) are not log-normal, but [...] Read more.
The Black–Scholes model and many of its extensions imply a log-normal distribution of stock total returns over any finite holding period. However, for a holding period of up to one year, empirical stock return distributions (both conditional and unconditional) are not log-normal, but rather much closer to the logistic distribution. This paper derives analytic option pricing formulas for an underlying asset with a logistic return distribution. These formulas are simple and elegant and employ exactly the same parameters as B&S. The logistic option pricing formula fits empirical option prices much better than B&S, providing explanatory power comparable to much more complex models with a larger number of parameters. Full article
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<p>The unconditional daily return distribution for the S&amp;P 500 index. The dashed line is the best log-normal fit. The bold line is the best logistic fit. The logistic distribution provides the best fit of all 10 theoretical distributions examined. The description of the data and more details are provided in <a href="#sec2-jrfm-17-00067" class="html-sec">Section 2</a>.</p>
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<p>The distributions of daily total returns conditioned on the pervious-day’s VIX. Quintile 1 is the one with the lowest VIX. For all five quintiles, the logistic distribution fits better than the log-normal (see also <a href="#jrfm-17-00067-t001" class="html-table">Table 1</a> and Table 3).</p>
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<p>The distributions of daily total returns conditioned on the pervious-day’s VIX. Quintile 1 is the one with the lowest VIX. For all five quintiles, the logistic distribution fits better than the log-normal (see also <a href="#jrfm-17-00067-t001" class="html-table">Table 1</a> and Table 3).</p>
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<p>The implied volatility as a function of moneyness for B&amp;S (light) and the logistic option pricing formula (bold). The volatility smile still exists with logistic option pricing, but it is much less pronounced than for B&amp;S. Note that the moneyness range is much wider than ranges typically reported in the literature.</p>
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<p>The logistic distribution and the truncated symmetric stable Paretian distributions are very close. The histogram is created by drawing 200,000 random observations from a truncated stable Paretian distribution with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.4</mn> <mo>,</mo> <mo> </mo> <mo> </mo> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (asymmetry parameter), <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>=</mo> <mn>0.01</mn> </mrow> </semantics></math> (scale parameter), and <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> (location parameter). The solid line is the best logistic fit. The fit depends on the stable Paretian distribution being symmetric (<math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>), but it is not sensitive to the values of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> and <math display="inline"><semantics> <mi>δ</mi> </semantics></math>. The excellent fit holds for a wide range of the exponent <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
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<p>The sample is the set of S&amp;P 500 monthly returns over 1926–2013. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is the empirical one-month standard deviation of returns. <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>T</mi> </msub> </mrow> </semantics></math> is the empirical standard deviation of returns for the <span class="html-italic">T</span>-month holding period. While the theoretical relation <math display="inline"><semantics> <mrow> <msub> <mi>σ</mi> <mi>T</mi> </msub> <mo>=</mo> <msub> <mi>σ</mi> <mn>1</mn> </msub> <mo>⋅</mo> <msqrt> <mi>T</mi> </msqrt> </mrow> </semantics></math> holds mathematically only for i.i.d log-returns, empirically it provides a very good approximation for returns as well, at least for horizons exceeding one month.</p>
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17 pages, 677 KiB  
Article
The Impact of ESG Rating on Hedging Downside Risks: Evidence from a Weight-Tilted Hang Seng Index
by Joseph K. W. Fung, F. Y. Eric Lam and Yiuman Tse
J. Risk Financial Manag. 2024, 17(2), 57; https://doi.org/10.3390/jrfm17020057 - 31 Jan 2024
Viewed by 2298
Abstract
The study examines the return performance and resilience to market volatility of the recently introduced environment, social/sustainable, and governance (ESG) weight-tilted Hang Seng index compared to its parent, the Hang Seng index. The ESG-infused index has a higher mean return and lower return [...] Read more.
The study examines the return performance and resilience to market volatility of the recently introduced environment, social/sustainable, and governance (ESG) weight-tilted Hang Seng index compared to its parent, the Hang Seng index. The ESG-infused index has a higher mean return and lower return volatility than the parent index, although the differences are statistically and economically insignificant, a result consistent with the high correlation between the two index returns. Most importantly, the ESG weight-tilted index is more resilient to volatility spikes than the parent index and, therefore, has lower downside risks. The overall results show that stocks with high ESG ratings are less susceptible to trading pressures triggered by volatility-induced turnovers. The paper contributes to the literature by providing significant incremental information on the emerging market for ESG-related equity products in Hong Kong. Full article
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<p>Time-series plot of the daily observations of the levels of Hang Seng option-implied volatility index (VHSI) and the two stock indexes (HSIESG and HSI) for the period 8 September 2014–October 2021. The diagram shows the large variations in the perceived market volatility embedded in the Hang Seng Index options prices.</p>
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48 pages, 585 KiB  
Article
A Survey of Literature on the Interlinkage between Petroleum Prices and Equity Markets
by Miramir Bagirov and Cesario Mateus
J. Risk Financial Manag. 2024, 17(1), 40; https://doi.org/10.3390/jrfm17010040 - 22 Jan 2024
Cited by 1 | Viewed by 2241
Abstract
The multifaceted interrelationship between petroleum prices and equity markets has been a subject of immense interest. The current paper offers an extensive review of a plethora of empirical studies in this strand of literature. By scrutinising over 190 papers published from 1983 to [...] Read more.
The multifaceted interrelationship between petroleum prices and equity markets has been a subject of immense interest. The current paper offers an extensive review of a plethora of empirical studies in this strand of literature. By scrutinising over 190 papers published from 1983 to 2023, our survey reveals various research themes and points to diverse findings that are sector- and country-specific and contingent on employed methodologies, data frequencies, and time horizons. More precisely, petroleum price changes and shocks exert direct or indirect effects dictated by the level of petroleum dependency across sectors and the country’s position as a net petroleum exporter or importer. The interlinkages tend to display a time-varying nature and sensitivity to major market events. In addition, volatility is not solely spilled from petroleum to equity markets; it is also observed to transmit in the reverse direction. The importance of incorporating asymmetries is documented. Lastly, the summarised findings can serve as the basis for further research and reveal valuable insights to market participants. Full article
19 pages, 5686 KiB  
Article
The Financial Market of Indices of Socioeconomic Well-Being
by Thilini V. Mahanama, Abootaleb Shirvani, Svetlozar Rachev and Frank J. Fabozzi
J. Risk Financial Manag. 2024, 17(1), 35; https://doi.org/10.3390/jrfm17010035 - 16 Jan 2024
Viewed by 1952
Abstract
This study discusses how financial economic theory and its quantitative tools can be applied to create socioeconomic indices and develop a financial market for the so-called “socioeconomic well-being indices”. In this study, we quantify socioeconomic well-being by assigning a dollar value to the [...] Read more.
This study discusses how financial economic theory and its quantitative tools can be applied to create socioeconomic indices and develop a financial market for the so-called “socioeconomic well-being indices”. In this study, we quantify socioeconomic well-being by assigning a dollar value to the well-being factors of selected countries; this is analogous to how the Dow 30 encapsulates the financial health of the US market. While environmental, social, and governance (ESG) financial markets address socioeconomic issues, our focus is broader, encompassing national citizens’ well-being. The dollar-denominated socioeconomic indices for each country can be viewed as financial assets that can serve as risky assets for constructing a global index, which, in turn, serves as a “market of well-being socioeconomic index”. This novel global index of well-being, paralleling the Dow Jones Industrial Average (DJIA), provides a comprehensive representation of the world’s socioeconomic status. Through advanced financial econometrics and dynamic asset pricing methodologies, we evaluate the potential for significant downturns in both the socioeconomic well-being indices of individual countries and the aggregate global index. This innovative approach allows us to engineer financial instruments akin to portfolio insurance, such as index puts, designed to hedge against these downturn risks. Our findings propose a financial market model for well-being indices, encouraging the financial industry to adopt and trade these indices as mechanisms to manage and hedge against downturn risks in well-being. Full article
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<p>(<b>a</b>) US development indicators from World Bank reports (<a href="#B20-jrfm-17-00035" class="html-bibr">IBRD 2022</a>) for 1990−2020 and (<b>b</b>) the US dollar socioeconomic well-being index (DWI) constructed using Equation (3) and the log returns of the exponentially transformed DWI with constraints from Equation (5).</p>
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<p>(<b>a</b>) Dollar socioeconomic well-being indices constructed using Equation (3) and (<b>b</b>) the global DWI proposed in Equation (4).</p>
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<p>Robust regression for historical and dynamic log returns in the US. Regression lines for both historical and dynamic data result in upward forecasts.</p>
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<p>Markowitz efficient frontier.</p>
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<p>Markowitz efficient frontier: (<b>a</b>) historical indices and (<b>b</b>) dynamic indices.</p>
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<p>Conditional value-at-risk portfolio optimization: (<b>a</b>) CVaR<span class="html-italic"><sub>p</sub></span><sub>,0.05</sub> and (<b>b</b>) CVaR<span class="html-italic"><sub>p</sub></span><sub>,0.01</sub> EFs.</p>
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<p>Variation of the weight composition of the Markowitz optimal portfolios along each efficient frontier (as a function of standard deviation): (<b>a</b>) historical portfolio and (<b>b</b>) dynamic portfolio.</p>
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<p>Variation of the weight composition of the CVaR<span class="html-italic"><sub>p</sub></span><sub>,<span class="html-italic">α</span></sub> optimal portfolios along each efficient frontier (as a function of <span class="html-italic">α</span>): (<b>a</b>) historical portfolio and (<b>b</b>) dynamic portfolio.</p>
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<p>Markowitz efficient frontier for all countries and countries with high: (<b>a</b>) historical indices and (<b>b</b>) dynamic indices.</p>
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<p>Comparing conditional value-at-risk portfolio optimization for dynamic indices: (<b>a</b>) CVaR<span class="html-italic"><sub>p</sub></span><sub>,0.05</sub> and (<b>b</b>) CVaR<span class="html-italic"><sub>p</sub></span><sub>,0.01</sub> efficient frontiers.</p>
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<p>Option prices for the US DWI at time t with varying strike prices <span class="html-italic">K</span> using a GARCH(1,1) model with NIG innovations: (<b>a</b>) call prices and (<b>b</b>) put prices.</p>
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<p>Implied volatilities of the US DWI based on the time to maturity (<span class="html-italic">T</span>) and moneyness (<span class="html-italic">M</span> = <span class="html-italic">S</span>/<span class="html-italic">K</span>) using a GARCH(1,1) model with NIG innovations.</p>
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12 pages, 282 KiB  
Article
Can Investment Views Explain Why People Insure Their Cell Phones But Not Their Homes?—A New Perspective on the Catastrophe Insurance Puzzle
by Annette Hofmann and Peter Zweifel
J. Risk Financial Manag. 2024, 17(1), 30; https://doi.org/10.3390/jrfm17010030 - 12 Jan 2024
Viewed by 1771
Abstract
The consistently missing demand for catastrophe insurance and for coverage of other low-probability–high-consequence risks is often referred to as the catastrophe insurance puzzle. People show reluctance to insure low-probability–high-consequence events, even with some disastrous consequences, yet insure against small high-probability–low-consequence events. There has [...] Read more.
The consistently missing demand for catastrophe insurance and for coverage of other low-probability–high-consequence risks is often referred to as the catastrophe insurance puzzle. People show reluctance to insure low-probability–high-consequence events, even with some disastrous consequences, yet insure against small high-probability–low-consequence events. There has been no convincing explanation of this puzzle to this date. This article points out that the underlying rationale may be that individuals interpret insurance contracts with low payout probability as an investment with negative expected net present value. While premium payments start with the conclusion of the contract, usually there is only one loss payment in the near or far future. Using a simple annuity model with fixed annual premiums and expected indemnity payouts, it is found that even an individual characterized by the degree of risk aversion found in the literature is unlikely to purchase insurance with these characteristics. To alleviate this unfavorable insurance purchase syndrome, combining a low-probability with a high-probability loss insurance contract may be a way to incentivize individuals to purchase catastrophe risk coverage. Full article
24 pages, 2106 KiB  
Article
Monte Carlo Sensitivities Using the Absolute Measure-Valued Derivative Method
by Mark Joshi, Oh Kang Kwon and Stephen Satchell
J. Risk Financial Manag. 2023, 16(12), 509; https://doi.org/10.3390/jrfm16120509 - 8 Dec 2023
Viewed by 1554
Abstract
Measure-valued differentiation (MVD) is a relatively new method for computing Monte Carlo sensitivities, relying on a decomposition of the derivative of transition densities of the underlying process into a linear combination of probability measures. In computing the sensitivities, additional paths are generated for [...] Read more.
Measure-valued differentiation (MVD) is a relatively new method for computing Monte Carlo sensitivities, relying on a decomposition of the derivative of transition densities of the underlying process into a linear combination of probability measures. In computing the sensitivities, additional paths are generated for each constituent distribution and the payoffs from these paths are combined to produce sample estimates. The method generally produces sensitivity estimates with lower variance than the finite difference and likelihood ratio methods, and can be applied to discontinuous payoffs in contrast to the pathwise differentiation method. However, these benefits come at the expense of an additional computational burden. In this paper, we propose an alternative approach, called the absolute measure-valued differentiation (AMVD) method, which expresses the derivative of the transition density at each simulation step as a single density rather than a linear combination. It is computationally more efficient than the MVD method and can result in sensitivity estimates with lower variance. Analytic and numerical examples are provided to compare the variance in the sensitivity estimates of the AMVD method against alternative methods. Full article
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<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>. On the right, there are variance ratios at <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. The solid curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, the dashed curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, and the dotted curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>. On the right, there are variance ratios at <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. The solid curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, the dashed curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>, and the dotted curve is <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>PW</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>c</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <msub> <mi>S</mi> <mn>0</mn> </msub> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>On the left, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>LR</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math> and, on the right, there is the ratio <math display="inline"><semantics> <mrow> <mfenced separators="" open="" close="/"> <mi mathvariant="double-struck">V</mi> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>MVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> <mspace width="0.166667em"/> </mfenced> <mi mathvariant="double-struck">V</mi> <mrow> <mo>[</mo> <msubsup> <mo>Δ</mo> <mi>σ</mi> <mi>AMVD</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>]</mo> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>Comparison of double-barrier option vega standard deviations for the LR, MVD, and AMVD methods.</p>
Full article ">
23 pages, 742 KiB  
Article
The Effect of Short-Sale Restrictions on Corporate Managers
by Baixiao Liu, John J. McConnell and Andrew Schrowang
J. Risk Financial Manag. 2023, 16(11), 486; https://doi.org/10.3390/jrfm16110486 - 17 Nov 2023
Viewed by 2139
Abstract
This paper studies the effect of short selling on corporate managers from 2002 through 2010. We examine how the exemption of short-sale uptick tests due to the Regulation SHO pilot program affects managers’ decisions to abandon value-reducing acquisition attempts. We find that when [...] Read more.
This paper studies the effect of short selling on corporate managers from 2002 through 2010. We examine how the exemption of short-sale uptick tests due to the Regulation SHO pilot program affects managers’ decisions to abandon value-reducing acquisition attempts. We find that when deciding whether to abandon value-reducing acquisition attempts during the program, managers of pilot firms, whose stocks are less subject to short-selling impediments, are more sensitive to stock price changes than managers of nonpilot firms. We find no difference in managers’ sensitivity prior to nor post SHO. These results indicate that, despite their dislike of short sellers, managers believe that the level of informativeness from capital markets is superior when short sellers are less impeded. Full article
Show Figures

Figure 1

Figure 1
<p><b>Differences in abandonment rates of value-reducing acquisition attempts between pilot and nonpilot firms during the pre-SHO, SHO and post-SHO Periods.</b> This figure depicts the differences in abandonment rates between Regulation SHO pilot and nonpilot firms of value-reducing acquisition attempts during the pre-SHO, SHO and post-SHO periods of the Regulation SHO pilot program. The pre-SHO period is 1 January 2002 through 1 May 2005. The SHO period is 2 May 2005 through 6 August 2007. The post-SHO period is 7 August 2007 through 31 December 2010. The sample is from the 2004 Russell 3000 Index as of June 2002. The difference in abandonment rates is calculated as the difference between the abandonment rate for pilot acquirers and the abandonment rate for nonpilot acquirers of proposed value-reducing acquisitions during the time period.</p>
Full article ">Figure 2
<p><b>Media attention given to short selling activities during the pre-SHO and the SHO periods.</b> This figure depicts the monthly number of news articles including “short sellers” or “short selling” in the <span class="html-italic">Wall Street Journal</span>, <span class="html-italic">New York Times</span>, <span class="html-italic">Washington Post</span>, and <span class="html-italic">USA Today</span> during the pre-SHO and SHO periods of the Regulation SHO pilot program. The pre-SHO period is 1 January 2002 through 1 May 2005. The SHO period is 2 May 2005 through 6 August 2007.</p>
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