scholarly journals What Drives Risk Perception? A Global Survey with Financial Professionals and Laypeople

2020 ◽  
Vol 66 (9) ◽  
pp. 3977-4002 ◽  
Author(s):  
Felix Holzmeister ◽  
Jürgen Huber ◽  
Michael Kirchler ◽  
Florian Lindner ◽  
Utz Weitzel ◽  
...  
Keyword(s):  
Risk Perception ◽  
Financial Risk ◽  
Large Scale ◽  
Risk Measures ◽  
Risk Measure ◽  
Cultural Backgrounds ◽  
Expected Return ◽  
Economic Decisions ◽  
Return Distributions ◽  
Large Scale Survey

Risk is an integral part of many economic decisions and is vitally important in finance. Despite extensive research on decision making under risk, little is known about how risks are actually perceived by financial professionals, the key players in global financial markets. In a large-scale survey experiment with 2,213 finance professionals and 4,559 laypeople in nine countries representing ~50% of the world’s population and more than 60% of the world’s gross domestic product, we expose participants to return distributions with equal expected return, and we systematically vary the distributions’ next three higher moments. Of these, skewness is the only moment that systematically affects financial professionals’ perception of financial risk. Strikingly, variance does not influence risk perception, even though return volatility is the most common risk measure in finance in both academia and the industry. When testing other, compound risk measures, the probability to experience losses is the strongest predictor of what is perceived as being risky. Analyzing professionals’ propensity to invest, skewness and loss probability also have strong predictive power, while volatility and kurtosis have some additional effect. Our results are very similar for laypeople, and they are robust across and within countries with different cultural backgrounds, as well as for different job fields of professionals. This paper was accepted by Yuval Rottenstreich, decision analysis.

scholarly journals What Drives Risk Perception? A Global Survey with Financial Professionals and Lay People

2019 ◽  
Author(s):  
Felix Holzmeister ◽  
Juergen Huber ◽  
Michael Kirchler ◽  
Florian Lindner ◽  
Utz Weitzel ◽  
...  
Keyword(s):  
Risk Perception ◽  
Financial Markets ◽  
Financial Risk ◽  
Large Scale ◽  
Risk Measures ◽  
Cultural Backgrounds ◽  
Economic Decisions ◽  
Lay People ◽  
Return Distributions ◽  
Large Scale Survey

Risk is an integral part of many economic decisions, and is vitally important in finance. Despite extensiveresearch on decision-making under risk, little is known about how risks are actually perceived by financialprofessionals, the key players in global financial markets. In a large-scale survey experiment with 2,213 financeprofessionals and 4,559 lay people in nine countries representing approx. 50% of the world’s population and morethan 60% of the world’s gross domestic product, we expose participants to return distributions with equalexpected return and we systematically vary the distributions’ next three higher moments. Of these, skewnessis the only moment that systematically affects financial professionals’ perception of financial risk. Strikingly,variance does not influence risk perception, even though return volatility is the most common risk measurein finance in both academia and the industry. When testing other, compound risk measures, the probabilityto experience losses is the strongest predictor of what is perceived as being risky. Analyzing professionals’propensity to invest, skewness and loss probability have strong predictive power too. However, volatility andkurtosis also have some additional effect on participants’ willingness to invest. Our results are very similarfor lay people, and they are robust across and within countries with different cultural backgrounds as wellas for different job fields of professionals.

scholarly journals Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

2021 ◽  
Vol 14 (11) ◽  
pp. 540
Author(s):  
Eyden Samunderu ◽  
Yvonne T. Murahwa
Keyword(s):  
At Risk ◽  
Normal Distribution ◽  
Hedge Funds ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Extensive Literature ◽  
Data Set ◽  
Normally Distributed

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

The role of risk measures in making seismic upgrading decisions

2020 ◽  
Vol 36 (4) ◽  
pp. 1802-1822
Author(s):  
Lukas Bodenmann ◽  
Panagiotis Galanis ◽  
Marco Broccardo ◽  
Božidar Stojadinović
Keyword(s):  
Seismic Risk ◽  
Financial Risk ◽  
Risk Mitigation ◽  
Risk Measures ◽  
Risk Measure ◽  
Full Range ◽  
Financial Benefit ◽  
Financial Benefits ◽  
Risk Appetite

Risk measures are tools that enable consistent measurement of financial risk and quantify the risk exposure to an associated hazard. In finance, there is a broad spectrum of risk measures which reflect different asset performance goals and the risk appetite of the decision-maker. In this study, the authors leverage advancements in financial risk management to examine the role of risk measures to quantify the seismically induced financial risk, measure the benefit of seismic upgrading, and relate the benefit of seismic risk reduction to a degree of the implemented seismic upgrade. The findings demonstrate that the relation between the financial benefits of a seismic upgrade, quantified using risk measures that consider the full range of earthquake events, and the degree of the seismic upgrade are concave, that is, the incremental financial benefit reduces gradually with increasing degree of seismic upgrading. The opposite holds if the risk measures consider only the high-severity low-likelihood events. Therefore, the study shows that the selection of the risk measure plays a crucial role in determining the target degree of seismic upgrading. Equivalently, quantifying the financial benefits of seismic risk mitigation using different risk measures might lead to different seismic upgrading decisions for the same structure.

scholarly journals Revisiting the relationship between different financial risk measures and the market return on ordinary shares in South Africa

2015 ◽  
Vol 18 (2) ◽  
pp. 218-231
Author(s):  
Elda Du Toit
Keyword(s):  
South African ◽  
Financial Risk ◽  
Positive Relationship ◽  
Risk Measures ◽  
The United States ◽  
Expected Return ◽  
The Difference ◽  
The One ◽  
The Relationship ◽  
A Share

The main aim of this study was to test whether there is a positive relationship between different financial risk measures and the expected return of a share. This study was performed in 1995 by Brümmer and Wolmarans, who obtained results contrary to those of a similar study in the United States of America in 1988. The reasons for the difference were not established. This study follows up the one by Brümmer and Wolmarans to determine whether the passing of 19 years could have brought about any difference in the results. This process was initiated by testing a set of variables from a sample size of 107 JSE-listed companies from 2002 to 2012 for linearity. As there was no such linear relationship between any of the variables, no assumptions can be made about any relationship between share return and the risk measures tested here. If investors were risk averse, one would expect a positive relationship between different financial risk measures and the expected return of a share. This is not the case in the South African market.

scholarly journals Aspects of a Phase Transition in High-Dimensional Random Geometry

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 805
Author(s):  
Axel Prüser ◽  
Imre Kondor ◽  
Andreas Engel
Keyword(s):  
Phase Transition ◽  
Financial Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Linear Equations ◽  
Minimax Problem ◽  
Random Coefficients ◽  
High Dimensional ◽  
Random Geometry ◽  
Competition For Resources

A phase transition in high-dimensional random geometry is analyzed as it arises in a variety of problems. A prominent example is the feasibility of a minimax problem that represents the extremal case of a class of financial risk measures, among them the current regulatory market risk measure Expected Shortfall. Others include portfolio optimization with a ban on short-selling, the storage capacity of the perceptron, the solvability of a set of linear equations with random coefficients, and competition for resources in an ecological system. These examples shed light on various aspects of the underlying geometric phase transition, create links between problems belonging to seemingly distant fields, and offer the possibility for further ramifications.

scholarly journals Multi-stage stochastic model in portfolio selection problem

Filomat ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 991-1001
Author(s):  
Shokoofeh Banihashemi ◽  
Ali Azarpour ◽  
Marziye Kaveh
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Problem ◽  
Conditional Value At Risk ◽  
Expected Return ◽  
Portfolio Selection Problem ◽  
Multi Stage

This paper is a novel work of portfolio-selection problem solving using multi objective model considering four parameters, Expected return, downside beta coefficient, semivariance and conditional value at risk at a specified confidence level. Multi-period models can be defined as stochastic models. Early studies on portfolio selection developed using variance as a risk measure; although, theories and practices revealed that variance, considering its downsides, is not a desirable risk measure. To increase accuracy and overcoming negative aspects of variance, downside risk measures like semivarinace, downside beta covariance, value at risk and conditional value at risk was other risk measures that replaced in models. These risk measures all have advantages over variance and previous works using these parameters have shown improvements in the best portfolio selection. Purposed models are solved using genetic algorithm and for the topic completion, numerical example and plots to measure the performance of model in four dimensions are provided.

scholarly journals Occurrence and Molecular Variability of Kiwifruit Viruses in Actinidia deliciosa ‘Xuxiang’ in the Shaanxi Province of China

Plant Disease ◽  
2019 ◽  
Vol 103 (6) ◽  
pp. 1309-1318 ◽  
Author(s):  
Lei Zhao ◽  
Wen Yang ◽  
Yuanle Zhang ◽  
Zhanmin Wu ◽  
Qiao-Chun Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Primary Source ◽  
Potato Virus X ◽  
Shaanxi Province ◽  
Multiple Infection ◽  
First Report ◽  
Molecular Variability ◽  
Large Scale Survey ◽  
Actinidia Spp ◽  
Main Producer

Kiwifruit (Actinidia spp.) is an economically substantial fruit crop with China the main producer. China is the primary source of wild kiwifruit and the largest producer of kiwifruit in terms of both production and planting area, and Shaanxi province is the largest kiwifruit producer in China. Previous studies reported presence of kiwifruit viruses in Actinidia chinensis. In this study, six viruses were identified in kiwifruit ‘Xuxiang’ (A. deliciosa) in Shaanxi, China. The incidence, distribution, and genetic diversity of these viruses were studied. The results showed that Actinidia virus A (AcVA), Actinidia virus B (AcVB), Actinidia chlorotic ringspot-associated virus (AcCRaV), cucumber mosaic virus (CMV), apple stem grooving virus (ASGV), and potato virus X (PVX) were the main viruses infecting Xuxiang kiwifruit in Shaanxi, China. Incidence of the various viruses with both single and multiple infection varied with different kiwifruit-growing counties. For single virus infection, the highest and the lowest numbers of samples infected were about 22 for AcCRaV and 0 for AcVB in Meixian out of 170 samples, 12 for AcVA and 0 for CMV in Zhouzhi out of 120 samples, 10 for AcVA and 0 for AcVB, AcCRaV, ASGV, PVX, and CMV in Yangling out of 70 samples, and 8 for AcCRaV and CMV and 0 for AcVA, AcVB, ASGV, and PVX in Hanzhong out of 80 samples, respectively. Samples which were multiply infected with two or more viruses were also detected. Analysis of the phylogenetic tree of these viruses showed some genetic variability in the AcVA, AcVB, and AcCRaV isolates of Shaanxi kiwifruit. There was no obvious molecular variation in the coat protein genes of ASGV, CMV, and PVX virus isolates from Shaanxi kiwifruit. The present study is the first large-scale survey of kiwifruit viruses in Shaanxi, China. To our knowledge, this is the first report of PVX infecting kiwifruit and the first report of molecular variability of AcVA, AcVB, and AcCRaV. These results provide important data for studying the genetic evolution of AcVA, AcVB, AcCRaV, ASGV, CMV, and PVX.

scholarly journals Grading Investment Diversification Options in Presence of Non-Historical Financial Information

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 692
Author(s):  
Clara Calvo ◽  
Carlos Ivorra ◽  
Vicente Liern ◽  
Blanca Pérez-Gladish
Keyword(s):  
Risk Measure ◽  
Statistical Information ◽  
Expected Returns ◽  
Financial Assets ◽  
Expected Return ◽  
Additional Information ◽  
Subjective Risk ◽  
The Impact ◽  
Statistical Risk ◽  
Reduction Index

Modern portfolio theory deals with the problem of selecting a portfolio of financial assets such that the expected return is maximized for a given level of risk. The forecast of the expected individual assets’ returns and risk is usually based on their historical returns. In this work, we consider a situation in which the investor has non-historical additional information that is used for the forecast of the expected returns. This implies that there is no obvious statistical risk measure any more, and it poses the problem of selecting an adequate set of diversification constraints to mitigate the risk of the selected portfolio without losing the value of the non-statistical information owned by the investor. To address this problem, we introduce an indicator, the historical reduction index, measuring the expected reduction of the expected return due to a given set of diversification constraints. We show that it can be used to grade the impact of each possible set of diversification constraints. Hence, the investor can choose from this gradation, the set better fitting his subjective risk-aversion level.

scholarly journals Modified Mean-Variance Risk Measures for Long-Term Portfolios

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 111
Author(s):  
Hyungbin Park
Keyword(s):  
Risk Measures ◽  
Risk Measure ◽  
Investment Strategy ◽  
Stochastic Volatility Model ◽  
Heston Model ◽  
Volatility Model ◽  
Variance Risk ◽  
Investment Portfolios ◽  
Mean Variance

This paper proposes modified mean-variance risk measures for long-term investment portfolios. Two types of portfolios are considered: constant proportion portfolios and increasing amount portfolios. They are widely used in finance for investing assets and developing derivative securities. We compare the long-term behavior of a conventional mean-variance risk measure and a modified one of the two types of portfolios, and we discuss the benefits of the modified measure. Subsequently, an optimal long-term investment strategy is derived. We show that the modified risk measure reflects the investor’s risk aversion on the optimal long-term investment strategy; however, the conventional one does not. Several factor models are discussed as concrete examples: the Black–Scholes model, Kim–Omberg model, Heston model, and 3/2 stochastic volatility model.

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