nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒05‒22
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz By Georg Mainik; Georgi Mitov; Ludger R\"uschendorf
  2. The multi-layer network nature of systemic risk and its implications for the costs of financial crises By Sebastian Poledna; Jos\'e Luis Molina-Borboa; Seraf\'in Mart\'inez-Jaramillo; Marco van der Leij; Stefan Thurner
  3. What attitudes to risk underlie distortion risk measure choices? By Jaume Belles-Sampera; Montserrat Guillén; Miguel Santolino
  4. Modelling Annuity Portfolios and Longevity Risk with Extended CreditRisk+ By Jonas Hirz; Uwe Schmock; Pavel V. Shevchenko
  5. Management of projects risk with Business Intelligence By Jiri Kriz; Lenka Smolikova; Vladena Stepankova
  6. Fiscal Reform and Improved Earthquake Insurance Claims-paying Capacity: Can the Two Coexist? —Attempting to reconcile heightened earthquake risk with sound fiscal policy— By Oguro, Kazumasa; Hiraizumi, Nobuyuki; Owen, Michael; Guo, Jicang
  7. How Not to Regulate Insurance Markets: The Risks and Dangers of Solvency II By Avinash D. Persaud
  8. The Failure of supervisory stress testing: Fannie Mae, Freddie Mac, and OFHEO By Frame, W. Scott; Gerardi, Kristopher S.; Willen, Paul S.
  9. Volatility risk premia and financial connectedness By Andrea Cipollini; Iolanda Lo Cascio; Silvia Muzzioli
  10. The efficiency of Anderson-Darling test with limited sample size: an application to Backtesting Counterparty Credit Risk internal model By M. Formenti; L. Spadafora; M. Terraneo; F. Ramponi
  11. The Price of Variance Risk By Ian Dew-Becker; Stefano Giglio; Anh Le; Marius Rodriguez
  12. A Multivariate Model of Strategic Asset Allocation with Longevity Risk By Bisetti, Emilio; Favero, Carlo A.; Nocera, Giacomo; Tebaldi, Claudio
  13. Will Islamic Banking make the World less risky? An Empirical Analysis of Capital Structure, Risk Shifting and Financial Stability By Sweder van Wijnbergen; Sajjad Zaheer; Moazzam Farooq
  14. Maintaining Central-Bank Financial Stability under New-Style Central Banking By Robert E. Hall; Ricardo Reis
  15. FloGARCH : Realizing long memory and asymmetries in returns volatility By Harry Vander Elst
  16. Forecasting volatility of wind power production By Zhiwei Shen; Matthias Ritter; ;
  17. Indicator Based Forecasting of Business Cycles in Azerbaijan By Mammadov, Fuad; Shaig Adigozalov, Shaiq
  18. The Impact of Jumps and Leverage in Forecasting Co-Volatility By Manabu Asai; Michael McAleer
  19. Are We Risking Too Much? By Malcolm, Bill; Sinnett, Alex
  20. The Direct Impact of Risk Management Tools on Farm Income: The Case of Irelands Spring Barley Producers By Loughrey, Jason; Thorne, Fiona; Hennessy, Thia
  21. International Sign Predictability of Stock Returns: The Role of the United States By Henri Nyberg; Harri Pönkä
  22. Risk Perception in an Arab Country By Adel Al Khattab

  1. By: Georg Mainik; Georgi Mitov; Ludger R\"uschendorf
    Abstract: Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study seems to be the first application of extreme value techniques in portfolio management on a large scale. The primary aim of our investigation is the potential of ERI in practice. The performance of this strategy is benchmarked against the minimum variance portfolio and the equally weighted portfolio. These fundamental strategies are important benchmarks for large-scale applications. Our comparison includes annualized portfolio returns, maximal drawdowns, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator. Our results show that the ERI strategy significantly outperforms both the minimum-variance portfolio and the equally weighted portfolio on assets with heavy tails.
    Date: 2015–05
  2. By: Sebastian Poledna; Jos\'e Luis Molina-Borboa; Seraf\'in Mart\'inez-Jaramillo; Marco van der Leij; Stefan Thurner
    Abstract: The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007-2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor four higher now than before the financial crisis of 2007-2008. We find that systemic risk contributions of individual transactions can be up to a factor of thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data driven quantification of systemic risk on national scales that reveal its true levels.
    Date: 2015–05
  3. By: Jaume Belles-Sampera (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); Montserrat Guillén (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona); Miguel Santolino (Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona)
    Abstract: Understanding the attitude to risk implicit within a risk measure sheds some light on the way in which decision makers perceive losses. In this paper, a two-stage strategy is developed to characterize the underlying risk attitude involved in a risk evaluation, when executed by the family of distortion risk measures. First, we show that aggregation indicators defined for discrete Choquet integrals provide informa- tion about the implicit global risk attitude of the agent. Second, an analysis of the distortion function offers a local description of the agent's stance on risk in relation to the occurrence of accumulated losses. Here, the concepts of absolute risk attitude and local risk attitude arise naturally. An example is provided to illustrate the usefulness of this strategy for characterizing risk attitudes in an insurance company.
    Keywords: Risk management, Risk tolerance, GlueVaR, Solvency II, Basel III.
    Date: 2015–05
  4. By: Jonas Hirz; Uwe Schmock; Pavel V. Shevchenko
    Abstract: Using an extended version of the credit risk model CreditRisk$^+$, we develop a flexible framework to estimate stochastic life tables and to model credit, life insurance and annuity portfolios, including actuarial reserves. Deaths are driven by common stochastic risk factors which may be interpreted as death causes like neoplasms, circulatory diseases or idiosyncratic components. Our approach provides an efficient, numerically stable algorithm for an exact calculation of the one-period loss distribution where various sources of risk are considered. As required by many regulators, we can then derive risk measures for the one-period loss distribution such as value at risk and expected shortfall. Using publicly available data, we provide estimation procedures for model parameters including classical approaches, as well as Markov chain Monte Carlo methods. We conclude with a real world example using Australian death data. In particular, our model allows stress testing and, therefore, offers insight into how certain health scenarios influence annuity payments of an insurer. Such scenarios may include outbreaks of epidemics, improvement in health treatment, or development of better medication. Further applications of our model include modelling of stochastic life tables with corresponding forecasts of death probabilities and demographic changes.
    Date: 2015–05
  5. By: Jiri Kriz (Brno University of Technology); Lenka Smolikova (Brno University of Technology); Vladena Stepankova (Brno University of Technology)
    Abstract: Project management is characterize like the broader concept of a comprehensive set of management processes and activities that are limited in time and whose aim is to implement something specific, whether the introduction, change, etc. In project management, which aims to ensure effective management of a comprehensive package of activities to a greater or lesser extent, concerns virtually all organizations and from internal changes or activities, supply of products, the introduction of ICT technologies to large investment projects. Project management involves the application of knowledge, experience, skills, activities, tools and techniques so that the final project met its requirements and achieves its goals in a limited time interval. Between the initial and final state the project goes through several phases, including project risk. To eliminate these risks is determined by the risk management as an area focusing on analysis and risk reduction using various tools and techniques. If we seek to answer the question what is the risk, then in terms of project management it can be understood as the likelihood that an event occurs that is contrary to the assumption. The first stage is to identify risks. This is based on the areas covered by the project and cannot be generalized for different types of projects. For example, a project for the implementation of data warehouse will have different areas of risk than new product development. The next stage is risk analysis. At this stage, we try to find the level of risk and its impact on the completion of the project. We are looking for those risks which are important and have a significant influence on the project (priority risks). Following the planning and risk management, which proposes procedures to minimize risk, responsibility for the procedures and time frames in which the procedures are being implemented. The last phase is monitoring, which leads to elimination of risks, which are no longer relevant and to re-identify new risks. This entire process is appropriate to support software tool that allows us to their effective management. We can use Business Intelligence tools as one of the software tools, especially in the phase of risk identification and analysis. Identifying risks putting together a basic set of potential risks when the input use various available sources of information such as the previously identified risks files or lists the usual risks in managing similar projects. In the analysis phase, then we can make risk assessment of the potential risks, including the determination of their probabilities to create a catalogue of potential risks of the project, which must be addressed at the planning stage and management, Business Intelligence tools are with justification used for the suggestions to minimize risks. The article discusses the Business Intelligence tools and their application in the field of project’s risk management. This is an opportunity to create panels, tables, graphs and matrices, including analyses of data cubes and to a certain extent and use of prediction algorithms for determining the probability of the risk and its impact on the implementation of the project.
    Keywords: risk management, Business Intelligence, Data Cube, Prediction Algorithms
    JEL: O22 G32 D81
    Date: 2015–05
  6. By: Oguro, Kazumasa; Hiraizumi, Nobuyuki; Owen, Michael; Guo, Jicang
    Abstract: From the standpoint of reconciling heightened earthquake risk with sound fiscal policy, this paper performs a simplified simulation analysis of obtainable risk reduction in proportion to reinsurance premiums to explore the potential for improving the claims-paying capacity of Japan’s earthquake insurance program by using reinsurance, which is currently considered the least expensive method for improving risk transfer/claims-paying capacity. We divided the roughly 5 trillion yen of risk that is currently retained by Japan’s earthquake insurance program into 21 layers, starting with four successive layers in the 200 billion yen to 1 trillion yen group and ending with four successive layers in the 4.2 to 5 trillion yen group. We then compared the price of risk (reinsurance premiums necessary for reducing one unit of risk) for the different layers. Our analysis indicated that the four layers in the 1.4 to 2.2 trillion yen group could be reinsured for the lowest price per unit risk. Hence, if these successive four layers were ceded, the reinsurance premiums to be paid under the base insurance premiums to be paid under the base case would be 42.5 billion yen (a 5.31% reinsurance premium rate is applied for ceding 800 billion yen risk), thereby making possible risk reduction on the order of 698.5 billion (99% Tail VaR).
    Keywords: Government Special Account reform, earthquake insurance program, claims-paying capacity, reinsurance, price of risk, Tail VaR
    JEL: H60 H61 H63
    Date: 2015–04
  7. By: Avinash D. Persaud (Peterson Institute for International Economics)
    Abstract: Solvency II, which the European Parliament adopted in March 2014, codifies and harmonizes insurance regulations in Europe to reduce the risk of an insurer defaulting on its obligations and producing dangerous systemic side effects. The new directive tries to achieve these aims primarily by setting capital requirements for the assets of insurers and pension funds based on the annual volatility of the price of these assets. Persaud argues that these capital requirements will impose an asset allocation on life insurers and pension funds that does not serve the interests of consumers, the financial system, or the economy. The main problem with Solvency II is that the riskiness of the assets of a life insurer or pension fund with liabilities that will not materialize before 10 or sometimes 20 years is not well measured by the amount by which prices may fall during the next year. Solvency II fails to take account of the fact that institutions with different liabilities have different capacities for absorbing different risks and that it is the exploitation of these differences that creates systemic resilience. To correct this problem, Persaud offers an alternative approach that is more attuned to the risk that a pension fund or life insurer would fail to meet its obligations when they come due and less focused on the short-term volatility of asset prices.
    Date: 2015–04
  8. By: Frame, W. Scott (Federal Reserve Bank of Atlanta); Gerardi, Kristopher S. (Federal Reserve Bank of Atlanta); Willen, Paul S. (Federal Reserve Bank of Boston)
    Abstract: In the aftermath of the global financial crisis, policymakers in the United States and elsewhere have adopted stress testing as a central tool for supervising large, complex, financial institutions and promoting financial stability. Although supervisory stress testing may confer substantial benefits, such tests are vulnerable to model risk. This paper studies the risk-based capital stress test conducted by the Office of Federal Housing Enterprise Oversight (OFHEO) for Fannie Mae and Freddie Mac, the two government-sponsored enterprises (GSEs) that are central to the U.S. housing finance system. This research aims to identify the sources of the stress test's spectacular failure to detect the growing risk and ultimate financial distress at these GSEs as mortgage market conditions deteriorated in 2007 and 2008. The analysis focuses on a key element of OFHEO's stress test, the models used to predict default and prepayment of 30-year fixed-rate mortgages.
    Keywords: bank supervision; stress test; model risk; residential mortgages; government-sponsored enterprises
    JEL: G21 G23 G28
    Date: 2015–03–01
  9. By: Andrea Cipollini; Iolanda Lo Cascio; Silvia Muzzioli
    Abstract: In this paper we use the Diebold Yilmaz (2009 and 2012) methodology to construct an index of connectedness among five European stock markets: France, Germany, UK, Switzerland and the Netherlands, by using volatility risk premia. The volatility risk premium, which is a proxy of risk aversion, is measured by the difference between the implied volatility and expected realized volatility of the stock market for next month. While Diebold and Yilmaz focus is on the forecast error variance decomposition of stock returns or range based volatilities employing a stationary VAR in levels, we account for the (locally) long memory stationary properties of the levels of volatility risk premia series. Therefore, we estimate and invert a Fractionally Integrated VAR model to compute the cross forecast error variance shares necessary to obtain the index of total connectedness and the net contribution of each series to total connectedness. The results show that, over January 2000-August 2013, the index of total connectedness among volatility risk premia has been relatively stable with an increasing role played by France and with a positive (but decreasing) role played by Germany and the Netherlands. Non EMU countries such as the UK and Switzerland are negative net contributors to the index.
    Keywords: volatility risk premium, long memory, FIVAR, financial connectedness
    JEL: C32 C38 C58 G13
    Date: 2014–12
  10. By: M. Formenti; L. Spadafora; M. Terraneo; F. Ramponi
    Abstract: This work presents a theoretical and empirical evaluation of Anderson-Darling test when the sample size is limited. The test can be applied in order to backtest the risk factors dynamics in the context of Counterparty Credit Risk modelling. We show the limits of such test when backtesting the distributions of an interest rate model over long time horizons and we propose a modified version of the test that is able to detect more efficiently an underestimation of the model's volatility. Finally we provide an empirical application.
    Date: 2015–05
  11. By: Ian Dew-Becker; Stefano Giglio; Anh Le; Marius Rodriguez
    Abstract: In the period 1996-2014, the average investor in the variance swap market was indifferent to news about future variance at horizons ranging from 1 month to 14 years. It is only purely transitory and unexpected realized variance that were priced. These results present a challenge to most structural models of the variance risk premium, such as the intertemporal CAPM, recent models with Epstein-Zin preferences and long-run risks, and models where institutional investors have value-at-risk constraints. The results also have strong implications for macro models where volatility affects investment decisions, suggesting that investors are not willing to pay to hedge shocks in expected economic uncertainty.
    JEL: E44 G12
    Date: 2015–05
  12. By: Bisetti, Emilio; Favero, Carlo A.; Nocera, Giacomo; Tebaldi, Claudio
    Abstract: Generalized unexpected raise in life expectancy is a source of aggregate risk. Longevity-linked securities are a natural instrument to reallocate these risks by making them tradable in the financial market. This paper extends the Campbell and Viceira (2005) strategic asset allocation model including a longevity-linked investment possibility in addition to equity and fixed income securities. Estimation of the model, based on prices for standardized annuities publicly offered by US insurance companies, shows that aggregate shocks to survival probabilities are predictors for long term returns of the longevity linked securities, and reveals an unexpected predictability pattern. The empirical valuation of the market price of longevity risk confirms that longevity linked securities offer cheap funding opportunities to asset managers willing to leverage their investment portfolio.
    Keywords: longevity risk; strategic asset allocation
    JEL: G11 G12 G22
    Date: 2015–05
  13. By: Sweder van Wijnbergen (Faculty of Economics and Business, University of Amsterdam); Sajjad Zaheer (State Bank of Pakistan, Pakistan); Moazzam Farooq (Central Bank of Oman, Oman)
    Abstract: We use a classic Merton credit risk framework to argue that Islamic Banking Institutions (IBIs) face less incentive to take on risks than Conventional Banking Institutions (CBI). IBIs have less incentive for risk shifting both in and outside of distress situations. We test and confirm this prediction in an empirical analysis based on a dataset covering all CBIs, IBIs, and Islamic and conventional subsidiaries of mixed banking institutions in Pakistan. We find that full-fledged Islamic banks (IBs) are indeed more stable than conventional banking institutions (CBIs), and are better capitalized than their conventional counterparts. IBIs also have less volatile asset returns, less non-performing loans (NPLs) and lower loan loss provisioning. Similar results obtain for Islamic windows of mixed banks compared with conventional windows. The analysis suggests that the loss absorption capacity of Islamic banks leads to less risk taking and a more stable banking system.
    Keywords: Islamic Banking; risk shifting; asset quality; financial stability
    JEL: G2 G21 Z12
    Date: 2015–05–04
  14. By: Robert E. Hall; Ricardo Reis
    Abstract: Since 2008, the central banks of advanced countries have borrowed trillions of dollars from their commercial banks in the form of interest-paying reserves and invested the proceeds in portfolios of risky assets. We investigate how this new style of central banking affects central banks' solvency. A central bank is insolvent if its requirement to pay dividends to its government exceeds its income by enough to cause an unending upward drift in its debts to commercial banks. We consider three sources of risk to central banks: interest-rate risk (the Federal Reserve), default risk (the European Central Bank), and exchange-rate risk (central banks of small open economies). We find that a central bank that pays dividends equal to a standard concept of net income will always be solvent---its reserve obligations will not explode. In some circumstances, the dividend will be negative, meaning that the government is making a payment to the bank. If the charter does not provide for payments in that direction, then reserves will tend to grow more in crises than they shrink in normal times. To prevent this buildup, the charter needs to provide for makeup reductions in payments from the bank to the government. We compute measures of the financial strength of central banks at the end of 2013, and discuss how different institutions interact with quantitative easing policies to put these banks in less or more danger of instability. We conclude that the risks to financial stability are real in theory, but remote in practice today.
    JEL: E42 E58
    Date: 2015–05
  15. By: Harry Vander Elst (Université libre de Bruxelles)
    Abstract: We introduce the class of FloGARCH models in this paper. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models.
    Keywords: Realized GARCH models, high-frequency data, long memory, realized measures.
    JEL: C22 C53 C58 G17
    Date: 2015–04
  16. By: Zhiwei Shen; Matthias Ritter; ;
    Abstract: The increasing share of wind energy in the portfolio of energy sources highlights its uncertainties due to changing weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility forecasting abilities of different GARCH-type models for wind power production. Moreover, due to characteristic features of the wind power process, such as heteroscedasticity and nonlinearity, we also investigate the use of a Markov regime-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. The realized volatility, which is derived from lower-scale data, serves as a benchmark for the latent volatility. We find that the MRS-GARCH model significantly outperforms traditional GARCH models in predicting the volatility of wind power, while the exponential GARCH model is superior among traditional GARCH models.
    Keywords: Wind energy, volatility forecasting, GARCH models, Markov regime-switching, realized volatility
    JEL: C22 Q42 Q47
    Date: 2015–05
  17. By: Mammadov, Fuad; Shaig Adigozalov, Shaiq
    Abstract: This paper has attempted to construct leading indicator systems and based on that to predict future contraction period of the Azerbaijan non-oil economy using more than 100 publicly available economic and financial data. Our results show plausible and significant performance of composite leading indicator system with average leading time of 7.2 months. We found that between January of 2000 and May of 2014, there were 6 turning points in Azerbaijan non-oil economy, consisting of three peaks and three troughs corresponding three expansion and four contraction periods. It turns out that the average duration of expansion and contraction phases is 43 and 10 month, respectively. Based on selected leading indicators we constructed composite indicator is found to be able to predict all the six turning points. Using dynamic probit model we estimated contraction probability of non-oil output gap for the future period. Out-of-sample as well as in-sample forecast performance suggest that the leading indicator systems have significant predictive power and could be used as a useful tool for economic forecasting.
    Keywords: Business cycles, Dating, Turning points, Forecasting, Probit Model
    JEL: C25 C53 E32
    Date: 2014–10–10
  18. By: Manabu Asai (Faculty of Economics, Soka University); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.)
    Abstract: The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013) such that the estimated matrix is positive definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for forecasting weekly and monthly horizons.
    Keywords: Co-Volatility; Forecasting; Jump; Leverage Effects; Realized Covariance; Threshold Estimation.
    JEL: C32 C53 C58 G17
    Date: 2015–02
  19. By: Malcolm, Bill; Sinnett, Alex
    Keywords: Consumer/Household Economics, Risk and Uncertainty,
    Date: 2015–02
  20. By: Loughrey, Jason; Thorne, Fiona; Hennessy, Thia
    Abstract: Tillage farmers must manage numerous economic risks including uncertain yields and prices. Despite the presence of government subsidies, these factors can generate a relatively high variability in farm income. The improved management of farm income variability can contribute towards stability in household consumption, support for farm investments and further investment in child education. Forward contracting is the main available risk management tool for Irish tillage farmers. This paper uses a stochastic farm-level model to simulate the potential direct profit impact of this tool under alternative scenarios where 20 per cent of expected output is forward sold. Our results suggest that risk averse farmers may be willing in these scenarios, to forego approximately one to two per cent of their overall farm income to receive the protection of forward contracts. The proportion of market based income tends to be much greater as many tillage farms rely on government subsidies for a majority of their income. The overall direct profit impact also depends on the costs of production and the share of production committed to the contract.
    Keywords: Spring Barley, Forward Contract, Risk Management, Stochastic Model, Crop Production/Industries, Farm Management, International Development, C15, D81, Q12,
    Date: 2015–04
  21. By: Henri Nyberg (University of Helsinki); Harri Pönkä (University of Helsinki and CREATES)
    Abstract: We study the directional predictability of monthly excess stock market returns in the U.S. and ten other markets using univariate and bivariate binary response models. Our main interest is on the potential benefits of predicting the signs of the returns jointly, focusing on the predictive power from the U.S. to foreign markets. We introduce a new bivariate probit model that allows for such a contemporaneous predictive linkage from one market to the other. Our in-sample and out-of-sample forecasting results indicate superior predictive performance of the new model over the competing models by statistical measures and market timing performance, suggesting gradual diffusion of predictive information from the U.S. to the other markets.
    Keywords: Excess stock return, Directional predictability, Bivariate probit model, Market timing
    JEL: C22 G12 G17
    Date: 2015–05–05
  22. By: Adel Al Khattab (Al Hussein Bin Talal University)
    Abstract: The main aim of this paper is to examine risk perception in transport among a representative sample of the Jordanian public.The results are based on a questionnaire surveys carried out among a representative sample of the Jordanian public in 2013. The results showed that transport risks fell into two main categories: public and private mode of transport. Respondents assessed the probability of experiencing risk as lower for themselves than others, and they were also more worried about others experiencing a transport threat.Overall, worry was found to be the most important predictor of risk perception. Female subjects were found to emphasize worry in regard to both public and private transportation. Worry was found to be most important in regard to public transportation whereas probability assessments (i.e. cognitive evaluations) were found to be most important in regard to private mode of transport. This difference may guide how risk is communicated to the public.
    Keywords: Risk perception, Transport, Jordan.
    JEL: M16

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