nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒01‒01
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures By Richard Gerlach; Chao Wang
  2. Systematic tail risk By Harris, Richard; Stoja, Evarist; Nguyen, Linh
  3. Assessing portfolio market risk in the BRICS economies: use of multivariate GARCH models By Bonga-Bonga, Lumengo; Nleya, Lebogang
  4. Does Hedging with Derivatives Reduce the Market's Perception of Credit Risk? By Sriya Anbil; Alessio Saretto; Heather Tookes
  5. Options, Equity Risks, and the Value of Capital Structure Adjustments By Paul Borochin; Jie Yang
  6. Measuring the Covariance Risk of Consumer Debt Portfolios By Carlos Madeira
  7. MODELLING THE CREDIT RISK OF THE HUNGARIAN SME SECTOR By Ádám Banai; Gyöngyi Körmendi; Péter Lang; Nikolett Vágó
  8. Distress propagation in complex networks: the case of non-linear DebtRank By Tobias Preis; Marco Bardoscia; Fabio Caccioli; Juan Ignacio Perotti; Gianna Vivaldo; Guido Caldarelli
  9. Portfolio Allocation, Income Uncertainty and Households' Flight from Risk By Sarah Brown; Dan Gray; Mark N. Harris
  10. Generalized Exponential Moving Average (EMA) Model with Particle Filtering and Anomaly Detection (Forthcoming in "Expert Systems With Applications") By Masafumi Nakano; Akihiko Takahashi; Soichiro Takahashi
  11. Dual Moments and Risk Attitudes By Louis R. Eeckhoudt; Roger J. A. Laeven
  12. Model Uncertainty in Risk Analysis and Decision Theory: A Preliminary Investigation By Emanuele Borgonovo; Veronica Cappelli; Fabio Maccheroni; Massimo Marinacci
  13. Common and Fundamental Risk Factors in Shareholder Returns of Norwegian Salmon Producing Companies By Misund, Bard
  14. Asset Pricing and Extreme Event Risk: Common Factors in ILS Fund Returns By Ben Ammar, Semir; Braun, Alexander; Eling, Martin
  15. Abandon ship: Inside debt and risk-taking incentives in bad times By Cambrea, Domenico Rocco; Colonnello, Stefano; Curatola, Giuliano; Fantini, Giulia
  16. A Look Under the Hood : How Banks Use Credit Default Swaps By Cecilia Caglio; R. Matthew Darst; Eric Parolin

  1. By: Richard Gerlach; Chao Wang
    Abstract: A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. Competing realized measures (e.g. Realized Variance and Realized Range) are employed as the dependent variable in the measurement equation and to drive expectile dynamics. The measurement equation here models the contemporaneous dependence between the realized measure and the latent conditional expectile. We also propose employing the quantile loss function as the target criterion, instead of the conventional violation rate, during the expectile level grid search. For the proposed model, the usual search procedure and asymmetric least squares (ALS) optimization to estimate the expectile level and CARE parameters proves challenging and often fails to convergence. We incorporate a fast random walk Metropolis stochastic search method, combined with a more targeted grid search procedure, to allow reasonably fast and improved accuracy in estimation of this level and the associated model parameters. Given the convergence issue, Bayesian adaptive Markov Chain Monte Carlo methods are proposed for estimation, whilst their properties are assessed and compared with ALS via a simulation study. In a real forecasting study applied to 7 market indices and 2 individual asset returns, compared to the original CARE, the parametric GARCH and Realized-GARCH models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed Realized-CARE model, especially when incorporating the Realized Range and the sub-sampled Realized Range as the realized measure in the model.
    Date: 2016–12
  2. By: Harris, Richard (University of Exeter); Stoja, Evarist (University of Bristol); Nguyen, Linh (University of Exeter)
    Abstract: We propose new systematic tail risk measures constructed using two different approaches. The first extends the canonical downside beta and co-moment measures, while the second is based on the sensitivity of stock returns to innovations in market crash risk. Both tail risk measures are associated with a significantly positive risk premium after controlling for other measures of downside risk, including downside beta, co-skewness and co-kurtosis. Using these measures, we examine the relevance of the tail risk premium for investors with different investment horizons.
    Keywords: Asset pricing; downside risk; tail risk; co-moments; value at risk; systematic risk
    JEL: C13 C31 C58 G01 G10 G12
    Date: 2016–12–16
  3. By: Bonga-Bonga, Lumengo; Nleya, Lebogang
    Abstract: This paper compares the performance of the different models used to estimate portfolio value-at-risk (VaR) in the BRICS economies. Portfolio VaR is estimated with three different multivariate risk models, namely the constant conditional correlation (CCC), the dynamic conditional correlation (DCC) and asymmetric DCC (ADCC) GARCH models. Risk performance measures such as the average deviations, quadratic probability function score and the root mean square error are used to back-test the performance of the models at 90%. The results indicate that portfolios with more weight to currency and less to equities prove to be the best way of minimizing loses in BRICS.
    Keywords: portfolio value-at-risk, multivariate GARCH, risk performance measures, BRICS
    JEL: C58 G15
    Date: 2016–12–25
  4. By: Sriya Anbil; Alessio Saretto; Heather Tookes
    Abstract: Risk management is the most widely-cited reason that non-financial corporations use derivatives. If hedging programs are effective, then firms using derivatives should have lower credit risk than those that do not. Surprisingly, we find that firms with derivative positions without a hedge accounting designation (typically higher basis risk) have higher CDS spreads than firms that do not hedge at all. We do not find evidence that these non-designated positions are associated with future credit realizations. We examine alternative explanations and find evidence that is consistent with a market penalty for high basis risk positions when overall market conditions are poor.
    Keywords: Counterparty credit risk ; Derivatives, futures, and options ; Risk management ; Hedging
    Date: 2016–07–20
  5. By: Paul Borochin; Jie Yang
    Abstract: We use exchange-traded options to identify risks relevant to capital structure adjustments in firms. These forward-looking market-based risk measures provide significant explanatory power in predicting net leverage changes in excess of accounting data. They matter most during contractionary periods and for growth firms. We form market-based indices that capture firms' magnitudes of, and propensity for, net leverage increases. Firms with larger predicted leverage increases outperform firms with lower predicted increases by 3.1% to 3.9% per year in buy-and-hold abnormal returns. Finally, consistent with the quality, leverage, and distress risk puzzles, firms with lower predicted leverage increases are riskier but earn lower abnormal returns.
    Keywords: Capital Structure ; Financial Leverage ; Options ; Implied Volatility
    JEL: G30 G32 G12 G14
    Date: 2016–10
  6. By: Carlos Madeira
    Abstract: Consumer loan risk is hard to predict, since households are heterogeneous and suffer significant macro shocks. This work proposes a heterogeneous agents model of household credit risk with shocks to both labor income and credit access. Using the Chilean Household Finance Survey I simulate the default conditions of different households over distinct macro scenarios. I show that banks' loan portfolios have very different covariance risk in relation to macro events, with some banks being prone to high risk during recessions. Also, heterogeneity in covariance risk has a negative impact on loan amounts and the probability of receiving a loan.
    Date: 2016–11
  7. By: Ádám Banai (Magyar Nemzeti Bank (Central Bank of Hungary)); Gyöngyi Körmendi (Magyar Nemzeti Bank (Central Bank of Hungary)); Péter Lang (Magyar Nemzeti Bank (Central Bank of Hungary)); Nikolett Vágó (Magyar Nemzeti Bank (Central Bank of Hungary))
    Abstract: In banking practice, quantifying the probability of default is one of the most important elements of the lending decision, therefore it is also vital from a financial stability perspective. The aim of our research was to model the probability of default as precisely as possible in the case of micro, small and medium-sized enterprises. By linking the data from the Central Credit Information System (KHR) and companies’ financial statements, a database was created that covers all the SMEs with loan contract, thus we were able to examine credit risk based on a uniquely large group of enterprises. In our research, we created models that enabled us to produce estimates based on certain corporate features about the probability of default of micro, small and medium-sized enterprises. Our analysis revealed that modelling these size categories separately and managing non-linear effects in the case of several variables are especially important. In addition, the impact of the macroeconomic environment on credit risk also proved to be important in the fitting of our estimates.
    Keywords: SME, credit risk, credit register, logit model, probability of default
    JEL: C25 G20 G21
    Date: 2016
  8. By: Tobias Preis; Marco Bardoscia; Fabio Caccioli; Juan Ignacio Perotti; Gianna Vivaldo; Guido Caldarelli
    Abstract: We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.
    JEL: G32 F3 G3
    Date: 2016–10–04
  9. By: Sarah Brown (Department of Economics, University of Sheffield); Dan Gray (Department of Economics, University of Sheffield); Mark N. Harris (Curtin Business School, Curtin University)
    Abstract: Analysing the US Panel Study of Income Dynamics, we present a new empirical method to investigate the extent to which households reduce their financial risk exposure when confronted with background risk. Our novel modelling approach - termed a deflated fractional ordered probit model - quantifies how the overall asset composition in a portfolio adjusts with background risk, and is unique in recovering for, any given risky asset class, the shares that are reallocated to a safer asset category. Background risk exerts a significant impact on household portfolios, resulting in a 'flight from risk', away from riskier to safer assets.
    Keywords: Asset Allocation; Background Risk; Flight from Risk; Fractional Models
    JEL: C33 C35 D14 G11
    Date: 2016–12
  10. By: Masafumi Nakano (Graduate School of Ecnonomics, University of Tokyo); Akihiko Takahashi (Faculty of Economics, University of Tokyo); Soichiro Takahashi (Graduate School of Ecnonomics, University of Tokyo)
    Abstract: This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum-variance and risk parity portfolios. Our dataset is monthly total returns of global financial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.
    Date: 2016–12
  11. By: Louis R. Eeckhoudt; Roger J. A. Laeven
    Abstract: In the economics of risk, the primal moments of mean and variance play a central role to define the local index of absolute risk aversion. In this note, we show that in canonical non-EU models dual moments have to be used instead of, or on par with, their primal counterparts to obtain an equivalent index of absolute risk aversion.
    Date: 2016–12
  12. By: Emanuele Borgonovo; Veronica Cappelli; Fabio Maccheroni; Massimo Marinacci
    Abstract: The purpose of this note is to discuss the relation between model uncertainty in risk analysis and decision theory.
    Date: 2016
  13. By: Misund, Bard (UiS)
    Abstract: Salmon farming companies are increasingly gaining attention from investors and portfolio managers. The last decade has seen a substantial growth in the securitization of salmon farming assets and prices. A growing literature demonstrates that industry-specific fundamental, as well as market-wide risk factors help explain stock returns. However, very little is known about the pricing of salmon stocks and especially the contribution of industry-specific fundamental risk factors. Using a multifactor model, we find that stock returns for salmon farming firms are significantly associated with both common market-wide risks and industry-specific risk factors.
    Keywords: Atlantic salmon production; salmon company valuation; stock returns; risk factors; salmon price.
    JEL: G12 G31 Q02 Q14
    Date: 2016–12–15
  14. By: Ben Ammar, Semir; Braun, Alexander; Eling, Martin
    Abstract: The returns of investment funds specializing in insurance-linked securities (ILS) exhibit a unique behavior. We introduce a new peril-based factor model, which explains the time-series and cross-sectional return variation. Despite a strong overall fit, we are left with significantly positive alphas for about one quarter of the funds, some of which can be attributed to beta exposures associated with non-cat-bond ILS. In addition, they are related to fund size, fund age, and performance fees. Although we do not find evidence for market timing abilities, we can rule out pure luck as the source of outperformance by controlling for false discoveries.
    Keywords: Insurance-Linked Securities, Investment Funds, Factor Model, Catastrophe Bonds
    JEL: G13 G22 Q54
    Date: 2016–12
  15. By: Cambrea, Domenico Rocco; Colonnello, Stefano; Curatola, Giuliano; Fantini, Giulia
    Abstract: We develop a model that endogenizes the manager's choice of firm risk and of inside debt investment strategy. Our model delivers two predictions. First, managers have an incentive to reduce the correlation between inside debt and company stock in bad times. Second, managers that reduce such a correlation take on more risk in bad times. Using a sample of U.S. public firms, we provide evidence consistent with the model's predictions. Our results suggest that the weaker link between inside debt and company stock in bad times does not translate into a mitigation of debt-equity conflicts.
    Keywords: Inside Debt,Executive Compensation,Corporate Distress
    JEL: G32 G34
    Date: 2016
  16. By: Cecilia Caglio; R. Matthew Darst; Eric Parolin
    Abstract: This note uses a unique dataset that matches banks' securities and loan portfolios to bank credit derivative transactions to characterize the basic features of how the largest banks in the U.S. use the single name CDS market in their investment portfolios.
    Date: 2016–12–22

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