nep-rmg New Economics Papers
on Risk Management
Issue of 2016‒05‒14
23 papers chosen by

  1. Determining the Effectiveness of Exchange Traded Funds as a Risk Management Tool for Southeastern Producers By Maples, William; Harri, Ardian; Riley, John Michael; Tack, Jesse; Williams, Brian
  2. Disaster and fortune risk in asset returns By Lerby M. Ergun
  3. Nonparametric Tail Risk, Stock Returns and the Macroeconomy By René Garcia; Caio Almeida; Kym Ardison; Jose Vicente
  4. Bayesian Dynamic Modeling of High-Frequency Integer Price Changes By Istvan Barra; Siem Jan Koopman
  5. Volatility Spillover Effects and Cross Hedging in the U.S. Oil Market and the Energy Pipeline Sector Index By Jiang, Jingze; Marsh, Thomas L.
  6. Entangling credit and funding shocks in interbank markets By Giulio Cimini; Matteo Serri
  7. Joint Prediction Bands for Macroeconomic Risk Management By Farooq Akram; Andrew Binning; Junior Maih
  8. Indicators for Setting the Countercyclical Capital Buffer By Creedon, Conn; O'Brien, Eoin
  9. Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization By Bai, Z.; Li, H.; McAleer, M.J.; Wong, W-K.
  10. Could competition always raise the risk of bank failure? By Rodolphe Dos Santos Ferreira; Teresa Lloyd-Braga; Leonor Modesto
  11. Joint prediction bands for macroeconomic risk management By Farooq Akram; Andrew Binning; Junior Maih
  12. Capital Adequacy Regulations in Hungary: Did It Really Matter? By Dóra Siklós
  13. Volatility Managed Portfolios By Alan Moreira; Tyler Muir
  14. Leaving the market or reducing the coverage? By Anne Corcos; François Pannequin; Claude Montmarquette
  15. Latent Risk Estimation in Commercial Bank Delinquency Rates By Hubbs, Todd; Kuethe, Todd
  16. Stochastic Portfolio Theory: A Machine Learning Perspective By Yves-Laurent Kom Samo; Alexander Vervuurt
  17. Reversing Momentum: The Optimal Dynamic Momentum Strategy By Kai Li; Jun Liu
  18. Hedging effectiveness of European wheat futures markets: An application of multivariate GARCH models By Revoredo-Giha, Cesar; Zuppiroli, Marco
  19. Do Terror Attacks Predict Gold Returns? Evidence from a Quantile-Predictive-Regression Approach By Rangan Gupta; Anandamayee Majumdar; Christian Pierdzioch; Mark Wohar
  20. How the Black Swan damages the harvest: statistical modelling of extreme events in weather and crop production in Africa, Asia, and Latin America By Marmai, Nadin; Franco Villoria, Maria; Guerzoni, Marco
  21. Job Displacement Risk and Severance Pay By Marco Cozzi; Giulio Fella
  22. Improving Markov switching models using realized variance By Liu, Jia; Maheu, John M
  23. Extreme weather and risk preference: Panel evidence from Germany By Kahsay, Goytom Abraha; Osberghaus, Daniel

  1. By: Maples, William; Harri, Ardian; Riley, John Michael; Tack, Jesse; Williams, Brian
    Abstract: This research investigates the use of commodity exchange traded funds (ETFs) as a price risk management tool for agriculture producers. The effectiveness of using ETFs to hedge price risk will bfffe determined by calculating optimal hedge ratios. This paper will investigate the southeastern producer’s ability to hedge their price risk for not only outputs, like corn and feeder cattle, but also for inputs, like diesel fuel and fertilizer. These ratios will be calculated using ordinary least squares (OLS), error correction model (ECM), and generalized autoregressive conditional heteroskedasticity (GARCH) regression models. Being able to use ETFs to hedge price risk would provide a significant tool to small and mid-sized producers who are unable to take advantage of current price risk management practices, such as the use of futures, because of the large size of the futures contracts. ETFs also present a potential tool to manage a producer’s input price risk. A majority of producers are unable to protect themselves from the rising costs of inputs due to producers’ small production size and unavailability of protection methods.
    Keywords: ETFs, input price, output price, risk management, Risk and Uncertainty,
    Date: 2016
  2. By: Lerby M. Ergun
    Abstract: Do Disaster risk and Fortune risk fetch a premium or discount in the pricing of individual assets? Disaster risk and Fortune risk are measures for the co-movement of individual stocks with the market, given that the state of the world is extremely bad and extremely good, respectively. To address this question measures of Disaster risk and Fortune risk, derived from statistical Extreme Value Theory, are constructed. The measures are non-parametric and the number of order statistics to be used in the analysis is based on the Kolmogorov-Smirnov distance. This alleviates the problem of an arbitrarily chosen extreme region. The extreme dependence measures are used in Fama-MacBeth cross-sectional asset pricing regressions including Market, Fama-French, Liquidity and Momentum factors. I find that Disaster risk fetches a significant premium of 0.43% for the average stock.
    JEL: N0 F3 G3
    Date: 2016–03–10
  3. By: René Garcia; Caio Almeida; Kym Ardison; Jose Vicente
    Abstract: This paper introduces a new tail risk measure based on the risk-neutral excess expected shortfall. We propose a novel way to compute risky measures that incorporate risk neutral probabilities, without relying on option price information, from a cross section of assets returns. Empirically, we illustrate our methodology by estimating tail risk from the cross-section of the 25 Fama-French size and book-to-market portfolios. Our main results are twofold: from the assets cross-section perspective we find a premium related to downside risk, even when controlling for typical factors. Our tail risk index also provides meaningful information about future market returns and aggregate U.S. macroeconomic conditions and is straight-forwardly related to other tail downside risk measures. These results are robust to the choice of cross-sectional information retained to compute the tail risk measure. Moreover, our methodology is applicable to a broad set of assets and markets and can be used readily by regulators and risk managers.
    Keywords: Tail Risk, Risk Factor, Risk-Neutral Probability,
    JEL: G12 G13 G17
    Date: 2016–04–25
  4. By: Istvan Barra (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam, the Netherlands)
    Abstract: We investigate high-frequency volatility models for analyzing intra-day tick by tick stock price changes using Bayesian estimation procedures. Our key interest is the extraction of intra-day volatility patterns from high-frequency integer price changes. We account for the discrete nature of the data via two different approaches: ordered probit models and discrete distributions. We allow for stochastic volatility by modeling the variance as a stochastic function of time, with intra-day periodic patterns. We consider distributions with heavy tails to address occurrences of jumps in tick by tick discrete prices changes. In particular, we introduce a dynamic version of the negative binomial difference model with stochastic volatility. For each model we develop a Markov chain Monte Carlo estimation method that takes advantage of auxiliary mixture representations to facilitate the numerical implementation. This new modeling framework is illustrated by means of tick by tick data for several stocks from the NYSE and for different periods. Different models are compared with each other based on predictive likelihoods. We find evidence in favor of our preferred dynamic negative binomial difference model.
    Keywords: Bayesian inference; discrete distributions; high-frequency dynamics; Markov chain Monte Carlo; stochastic volatility
    JEL: C22 C58
    Date: 2016–04–22
  5. By: Jiang, Jingze; Marsh, Thomas L.
    Abstract: This study fills an important gap in the research literature to examine the mean and volatility spillover effects between the U.S. oil, overall U.S. stocks markets and the U.S. energy pipeline market by studying the linkages between the West Texas Intermediate (WTI), Dow Jones Industrial Average Index (DJIA), S&P 500 stock index (SP500) and the Dow Jones U.S. Pipeline Indices (DJUSPL). We are particularly interested in the impact of the liquidity crisis in the financial market on the volatility spillovers. Results indicate that both WTI and DJIA/SP500 have statistically significant volatility spillover effect on DJUSPL. In addition, the volatility transmission from the U.S. oil and overall stock markets to the U.S. energy pipeline market increased since the 2007-2008 financial crisis started. Furthermore, the raising illiquidity in the U.S. financial market is associated with the statistically significant increase in the volatility transmission between the markets. Furthermore, we examine the new cross-hedging strategy involving DJUSPL to manage the risk on the oil market and the both in-sample and out-of-sample performances of the hedging strategy are more effective than the oil-stock hedging strategy proposed by previous scholars (Basher & Sadorsky, 2016; Salisu & Oloko, 2015). Our results will assist policy makers and investors on planning energy, diversifying portfolio and managing energy risk.
    Keywords: cross hedges, crude oil, DJUSPL, energy pipeline, liquidity crisis, volatility spillover, Financial Economics, Resource /Energy Economics and Policy, Risk and Uncertainty, Q40, Q43, G11, C3,
    Date: 2016–07
  6. By: Giulio Cimini; Matteo Serri
    Abstract: Credit and liquidity risks represent main channels of financial contagion for interbank lending markets. On one hand, banks face potential losses whenever their counterparties are under distress and thus unable to fulfill their obligations. On the other hand, solvency constraints may force banks to recover lost fundings by selling their illiquid assets, resulting in effective losses in the presence of fire sales - that is, when funding shortcomings are widespread over the market. Because of the complex structure of the network of interbank exposures, these losses reverberate among banks and eventually get amplified, with potentially catastrophic consequences for the whole financial system. Building on Debt Rank [Battiston et al., 2012], in this work we define a systemic risk metric that estimates the potential amplification of losses in interbank markets accounting for both credit and liquidity contagion channels: the Debt-Solvency Rank. We implement this framework on a dataset of 183 European banks that were publicly traded between 2004 and 2013, showing indeed that liquidity spillovers substantially increase systemic risk, and thus cannot be neglected in stress-test scenarios. We also provide additional evidence that the interbank market was extremely fragile up to the 2008 financial crisis, becoming slightly more robust only afterwards.
    Date: 2016–04
  7. By: Farooq Akram; Andrew Binning; Junior Maih
    Abstract: In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty present in forecasts from multivariate time series models. Second, we suggest a new measure for assessing the plausibility of non-central point forecasts. And third, we describe how to use the density forecasts from a multivariate time series model to assess the probability of a set of future events occurring. An additional novelty of this paper is our use of a regime-switching DSGE model with an occasionally binding zero lower bound constraint, estimated on US data, to produce the density forecasts. The tools we offer will allow practitioners to better assess and communicate joint forecast probabilities, a criticism that has been leveled at central bank communications.
    Keywords: Monetary Policy, Fan charts, DSGE, Zero Lower Bound, Regime-switching, Bayesian Estimation
    Date: 2016–05
  8. By: Creedon, Conn (Central Bank of Ireland); O'Brien, Eoin (Central Bank of Ireland)
    Abstract: Since January 1 2016, the Countercyclical Capital Buffer (CCB), a new macro-prudential policy instrument, has been operational in Ireland. The CCB, which is a time-varying countercyclical capital requirement, aims to limit the potential systemic risks associated with excessive credit growth. This Letter provides an overview of the CCB and summarises European Systemic Risk Board (ESRB) recommendations on appropriate economic and financial indicator variables to guide the setting of the CCB. A number of indicators are applied to historical Irish data for illustrative purposes. The analysis also highlights challenges that arise in the estimation and interpretation of indicators and, therefore, the importance that policymaker judgement will play in setting the CCB rate.
    Date: 2016–04
  9. By: Bai, Z.; Li, H.; McAleer, M.J.; Wong, W-K.
    Abstract: This paper considers the portfolio problem for high dimensional data when the dimension and size are both large. We analyze the traditional Markowitz mean-variance (MV) portfolio by large dimension matrix theory, and find the spectral distribution of the sample covariance is the main factor to make the expected return of the traditional MV portfolio overestimate the theoretical MV portfolio. A correction is suggested to the spectral construction of the sample covariances to be the sample spectrally- corrected covariance, and to improve the traditional MV portfolio to be spectrally corrected. In the expressions of the expected return and risk on the MV portfolio, the population covariance matrix is always a quadratic form, which will direct MV portfolio estimation. We provide the limiting behavior of the quadratic form with the sample spectrally-corrected covariance matrix, and explain the superior performance to the sample covariance as the dimension increases to infinity proportionally with the sample size. Moreover, this paper deduces the limiting behavior of the expected return and risk on the spectrally-corrected MV portfolio, and illustrates the superior properties of the spectrally-corrected MV portfolio. In simulations, we compare the spectrally-corrected estimates with the traditional and bootstrap-corrected estimates, and show the performance of the spectrally-corrected estimates are the best in portfolio returns and portfolio risk. We also compare the performance of the new proposed estimation with different optimal portfolio estimates for real data from S&P 500. The empirical findings are consistent with the theory developed in the paper.
    Keywords: Markowitz Mean-Variance Optimization, Optimal Return, Optimal Portfolio Allocation, Large Random Matrix, Bootstrap Method, Spectrally-corrected Covariance Matrix
    JEL: G11 C13 C61
    Date: 2016–04–01
  10. By: Rodolphe Dos Santos Ferreira; Teresa Lloyd-Braga; Leonor Modesto
    Abstract: The debate between the 'competition-fragility' and 'competition-stability' views has been centered upon the risk of banks' loan portfolios. In this paper, we shift the focus of the debate from the riskiness of loan portfolios to the riskiness of operational costs net of the income of non-traditional banking activities, banks' default resulting from negative aggregate profits. We consider a simple model in which, due to purely idiosyncratic risks, portfolio diversification would eliminate the risk of banks' default if those net operational costs were negligible or were known with certainty. We show that more competition always raises the risk of bank default, non-monotonicity being excluded as an equilibrium outcome under free oligopolistic competition between profit maximizing banks. However, the same result obtains in fact under systemic risk, even under non-stochastic net operation costs, a situation which we explore in a slightly different model. We show further that, under liquidity shortness, a higher intensity of competition in the loan market can result in an increase of deposit rates, rather than a decrease of loan rates.
    Keywords: Bank failure, oligopolistic competition in the loan market.
    JEL: G21 D43 L13
    Date: 2016
  11. By: Farooq Akram (Norges Bank (Central Bank of Norway)); Andrew Binning (Norges Bank (Central Bank of Norway)); Junior Maih (BI Norwegian Business School)
    Abstract: In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty present in forecasts from multivariate time series models. Second, we suggest a new measure for assessing the plausibility of non-central point forecasts. And third, we describe how to use the density forecasts from a multivariate time series model to assess the probability of a set of future events occurring. An additional novelty of this paper is our use of a regime-switching DSGE model with an occasionally binding zero lower bound constraint, estimated on US data, to produce the density forecasts. The tools we off er will allow practitioners to better assess and communicate joint forecast probabilities, a criticism that has been leveled at central bank communications.
    Keywords: Monetary Policy, Fancharts, DSGE, Zero Lower Bound, Regime-switching, Bayesian Estimation
    JEL: C6 C11 C53 E1 E5 E37
    Date: 2016–04–28
  12. By: Dóra Siklós (European Stability Mechanism)
    Abstract: The main purpose of this paper is twofold. First, it aims to estimate the effect of the tightening of regulatory capital requirements on the real economy during a credit upswing. Second, it intends to show whether applying a countercyclical capital buffer measure, as per the Basel III rules, could have helped decelerate FX lending growth in Hungary, mitigating the build-up of vulnerabilities in the run-up to the global financial crisis. To answer these questions, we use a Vector Autoregression-based approach to understand how shocks affected to capital adequacy in the pre-crisis period. Our results suggest that regulatory authorities could have slowed the increase in lending temporarily. They would not, however, have been able to avoid the upswing in FX lending by requiring countercyclical capital buffers even if such a tool had been available and they had reacted quickly to accelerating credit growth. Our results also suggest that a more pronounced tightening might have reduced FX lending substantially, but at the expense of real GDP growth. The reason is that an unsustainable fiscal policy led to a trade-off between economic growth and the build-up of new vulnerabilities in the form of FX lending
    Keywords: FX lending, capital adequacy, bank regulation, counterfactual analysis
    JEL: E58 G01 G21 G28
    Date: 2016–04
  13. By: Alan Moreira; Tyler Muir
    Abstract: Managed portfolios that take less risk when volatility is high produce large, positive alphas and increase factor Sharpe ratios by substantial amounts. We document this fact for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Our portfolio timing strategies are simple to implement in real time and are contrary to conventional wisdom because volatility tends to be high after the onset of recessions and crises when selling is typically viewed as a mistake. Instead, our strategy earns high average returns while taking less risk in recessions. We study the portfolio choice implications of these results. We find volatility timing provides large utility gains to a mean variance investor, with increases in lifetime utility ranging from 50-90%. Contrary to conventional wisdom, we show that long horizon investors can benefit from volatility timing even when time-variation in volatility is completely driven by discount rate volatility.
    JEL: G0 G12
    Date: 2016–04
  14. By: Anne Corcos; François Pannequin; Claude Montmarquette
    Abstract: This study develops an experimental analysis addressing the premium sensitivity of the demand for insurance accounting for risk attitudes, including risk-loving. Our contribution disentangles the conditional demand (the non-null demand for insurance) from the propensity to buy insurance. Our research shows that the contraction of the global demand for insurance induced by the raise in unit prices and fixed cost is primarily due to policyholders exiting the insurance market rather than reducing their levels of coverage. However, contrary to the theoretical predictions, an increase in the fixed cost has effects only on the risk lovers’ behavior. The stability of the conditional demand is robust to changes in insurance contracts and individuals’risk attitude. These results suggest that the decision about insurance may boil down to an “all or nothing” choice. In line with the theory, risk lovers express a lower global demand for insurance than risk-averse subjects and are the first to leave the insurance market when the premium (unit price or fixed cost) is prohibitive. Implications regarding public and economic policies are discussed. As a by-product, our experimental design enables to test and reject the assumption of inferiority of the risk averters’ demand for insurance.
    Keywords: demand for insurance, conditional demand, propensity to buy insurance, risk attitude, two-part tariff, experimental study,
    JEL: C91 D81
    Date: 2016–05–06
  15. By: Hubbs, Todd; Kuethe, Todd
    Keywords: Agricultural and Food Policy,
    Date: 2016–03
  16. By: Yves-Laurent Kom Samo; Alexander Vervuurt
    Abstract: In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation. Our approach is deeply rooted in Stochastic Portfolio Theory (SPT), a stochastic analysis framework introduced by Robert Fernholz that aims at flexibly analysing the performance of certain investment strategies in stock markets relative to benchmark indices. In particular, SPT has exhibited some investment strategies based on company sizes that, under realistic assumptions, outperform benchmark indices with probability 1 over certain time horizons. Galvanised by this result, we consider the inverse problem that consists of learning (from historical data) an optimal investment strategy based on any given set of trading characteristics, and using a user-specified optimality criterion that may go beyond outperforming a benchmark index. Although this inverse problem is of the utmost interest to investment management practitioners, it can hardly be tackled using the SPT framework. We show that our machine learning approach learns investment strategies that considerably outperform existing SPT strategies in the US stock market.
    Date: 2016–05
  17. By: Kai Li (Finance Discipline Group, UTS Business School, University of Technology, Sydney); Jun Liu (Rady School of Management, University of California San Diego)
    Abstract: We study the optimal dynamic trading strategy between a riskless asset and a risky asset with momentum (momentum asset). The most salient characteristic of momentum is that positive price shocks predict positive future returns. This characteristic leads to big swings in returns over multiple periods. Investors with relative risk aversion greater than one dislike such big swings. We show that it is optimal for such investors to reverse momentum by holding less or even shorting the momentum asset. We find that the optimal portfolio weight also depends on the historical price path, in addition to momentum. Different historical price paths, even if they have the same momentum, lead to different optimal portfolio weights. In particular, with rebound path (a historical price path that decreases at the beginning and then rebounds later to have a positive momentum), it is optimal for investors to hold less or may short the momentum asset and hence suffer less or even benefit from momentum crashes.
    Keywords: portfolio selection; momentum crashes; dynamic optimal momentum strategy
    JEL: C32 G11
    Date: 2016–03–01
  18. By: Revoredo-Giha, Cesar; Zuppiroli, Marco
    Abstract: The instability of commodity prices and the hypothesis that speculative behaviour was one of its causes has brought renewed interest in futures markets. In this paper, the hedging effectiveness of European and US wheat futures markets were studied to test whether they were affected by the high price instability after 2007. In particular, the focus of the paper is to test of whether the increasing presence of financialization of commodity trading in futures markets mentioned in the literature have made them divorced from the physical markets. A multivariate GARCH model was applied to compute optimal hedging ratios. Important evidence was found of an improvement, after 2007, in the effectiveness of hedging with the European futures.
    Keywords: Futures prices, commodity prices, volatility, wheat, Europe., Demand and Price Analysis, Marketing, Q11, Q13,
    Date: 2015
  19. By: Rangan Gupta (Department of Economics, University of Pretoria); Anandamayee Majumdar (Center for Advanced Statistics and Econometrics, Soochow University, China); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Germany); Mark Wohar (Department of Economics, University of Nebraska-Omaha, USA and Loughborough University, UK)
    Abstract: Much significant research has been done to study how terror attacks affect financial markets. We contribute to this research by studying whether terror attacks, in addition to standard predictors considered in earlier research, help to predict gold returns. To this end, we use a Quantile-Predictive-Regression (QPR) approach that accounts for model uncertainty and model instability. We find that terror attacks have predictive value for the lower and especially for the upper quantiles of the conditional distribution of gold returns.
    Keywords: Gold returns, Terror attacks, Forecasting model, Quantile regression
    JEL: C22 C53 Q02
    Date: 2016–03
  20. By: Marmai, Nadin; Franco Villoria, Maria; Guerzoni, Marco (University of Turin)
    Abstract: Climate change constitutes a rising challenge to the agricultural base of developing countries. Most of the literature has focused on the impact of changes in the means of weather variables on mean changes in production and has found very little impact of weather upon agricultural production. Instead, a more recent stream of literature showed that we can assess the impact of weather on production by looking at extreme weather events. Based on this evidence, we surmise that there is a missing link in the literature consisting of relating the extreme events in weather with extreme losses in crop production. Indeed, extreme events are of the greatest interest for scholars and policy makers only when they carry extraordinary negative effects. We build on this idea and for the first time, we adapt a conditional dependence model for multivariate extreme values to understand the impact of extreme weather on agricultural production. Specifically, we look at the probability that an extreme event drastically reduces the harvest of any of the major crops. This analysis, which is run on data for six different crops and four different weather variables in a vast array of countries in Africa, Asia and Latin America, shows that extremes in weather and yield losses of major staples are associated events.
    Date: 2016–05
  21. By: Marco Cozzi (University of Victoria); Giulio Fella (Queen Mary University of London)
    Abstract: This paper is a quantitative, equilibrium study of the insurance role of severance pay when workers face displacement risk and markets are incomplete. A key feature of our model is that, in line with an established empirical literature, job displacement entails a persistent fall in earnings upon re-employment due to the loss of tenure. The model is solved numerically and calibrated to the US economy. In contrast to previous studies that have analyzed severance payments in the absence of persistent earning losses, we find that the welfare gains from the insurance against job displacement afforded by severance pay are sizable.
    Keywords: Severance payments, Incomplete markets, Welfare
    JEL: E24 D52 D58 J65
    Date: 2016–05
  22. By: Liu, Jia; Maheu, John M
    Abstract: This paper proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Bayesian estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to U.S. equity returns and foreign exchange rates compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. The joint Markov switching models can increase the precision of parameter estimates and sharpen the inference of the latent state variable.
    Keywords: infinite hidden Markov model, realized covariance, density forecast, MCMC
    JEL: C11 C32 C51 C58 G1
    Date: 2015–09–01
  23. By: Kahsay, Goytom Abraha; Osberghaus, Daniel
    Abstract: Individual risk preference may change after experiencing external socio-economic or natural shocks. Theoretical predictions and empirical studies suggest that risk taking may increase or decrease after experiencing shocks. So far the empirical evidence is sparse, especially when it comes to developed countries. We contribute to this literature by investigating whether experiencing financial and health-related damage caused by storms affects risk preference of individuals in Germany. Using unique panel data, we find that households who report storm damage increased their risk taking. We do not find evidence of exposure to storm per see (regardless of damage experience), which suggests that households have to suffer damage for their risk preference to be affected. These results are robust across a battery of alternative model specifications and alternative storm damage measures (magnitude of financial damage). We rule out other potential explanations such as health-related and economic shocks. The self-reported storm damage data is broadly confirmed by regional storm damage data provided by the insurance industry. While we cannot identify the channels through which experiencing storm damage affects risk preference from our data, we suggest and discuss some potential channels. The results may have important policy implications as risk preference affects, for instance, individuals' savings and investment behaviour, adoption of self-protection and self-insurance strategies, and technology adoption.
    Keywords: extreme weather,risk preference,risk seeking,storm damage,panel data
    JEL: C23 D03 D81 Q54
    Date: 2016

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