nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒05‒18
sixteen papers chosen by

  1. RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio By Kei Nakagawa; Shuhei Noma; Masaya Abe
  2. Tail Risk Transmission: A Study of Iran Food Industry By Fatemeh Mojtahedi; Seyed Mojtaba Mojaverian; Daniel Felix Ahelegbey; Paolo Giudici
  3. On the Profitability of Momentum Strategies and Optimal Leverage Rules By Lundström, Christian
  4. A dynamic conditional approach to portfolio weights forecasting By Fabrizio Cipollini; Giampiero M. Gallo; Alessandro Palandri
  5. Avoiding zero probability events when computing Value at Risk contributions: a Malliavin calculus approach By Yuri F. Saporito; Rodrigo S. Targino
  6. Recursive Preferences, the Value of Life, and Household Finance By Antoine Bommier; Daniel Harenberg; François Le Grand; Cormac O'Dea
  7. Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis By Papadamou, Stephanos; Fassas, Athanasios; Kenourgios, Dimitris; Dimitriou, Dimitrios
  8. Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises By Daniel Felix Ahelegbey; Paolo Giudici
  9. Forecasting State- and MSA-Level Housing Returns of the US: The Role of Mortgage Default Risks By Christos Bouras; Christina Christou; Rangan Gupta; Keagile Lesame
  10. Uncertainty and Growth Disasters By Boyan Jovanovic; Sai Ma
  11. Household Portfolios and Financial Preparedness for Retirement By Rowena Crawford; Cormac O'Dea
  12. A constraint-based notion of illiquidity By Thomas Krabichler; Josef Teichmann
  13. Drivers of Bank Default Risk: Bank Business Models, the Sovereign and Monetary Policy By Nicolas Soenen; Rudi Vander Vennet
  14. Direct versus iterated multi-period Value at Risk By Ruiz Ortega, Esther; Nieto Delfin, Maria Rosa
  15. Measuring Oil Price Shocks By Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
  16. Who should bear the risk of economic growth? By Abreu, Rafael Costa Berriel; Costa, Carlos Eugênio da

  1. By: Kei Nakagawa; Shuhei Noma; Masaya Abe
    Abstract: The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance). Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity. CVaR is defined as the expected value of the loss that occurs beyond a certain probability level ($\beta$). However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single $\beta$ and may output significantly different portfolios depending on how the $\beta$ is selected. We confirm even small changes in $\beta$ can result in huge changes in the whole portfolio structure. In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio. We perform experiments on well-known benchmarks to evaluate the proposed portfolio. Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.
    Date: 2020–04
  2. By: Fatemeh Mojtahedi (Sari Agricultural Sciences and Natural Resources University); Seyed Mojtaba Mojaverian (Sari Agricultural Sciences and Natural Resources University); Daniel Felix Ahelegbey (Università di Pavia); Paolo Giudici (Università di Pavia)
    Abstract: This paper extends the extreme downside correlations and hedge (EDC and EDH) methodology of Harris et al. (2019) to model the tail risk co-movement of financial assets under severe firm-level and market conditions. The model is applied to analyze both systematic and systemic exposures in the Iranian food industry. The empirical application address the following questions: 1) which food company is the safest for investors to diversify their investment, and 2) which companies are the risk “transmitters” and “receivers”, especially in turbulent times. To this end, we sampled the time series of 11 manufacturing companies and proxy the market indicator with the food industry index, all of which are publicly listed on the Tehran Stock Exchange (TSE). The data covers daily close prices from October 5, 2015, to January15, 2020. The systematic analysis reveals a positive and statistically significant relationship between the tail risk of the companies and the market index. The centrality analysis of the systemic exposures reveals Mahram Manufacturing as the safest and Behshahr Industries as the riskiest company. We also find evidence that W.Azar.Pegah is the main “transmitter” of tail risk, while Pegah.Fars.Co is the main “receiver” of risk.
    Keywords: Food industry, Extreme downside hedge, Extreme downside correlation, Systematic risk, Systemic risk.
    JEL: C31 C58 G01 G12
    Date: 2020–05
  3. By: Lundström, Christian (Department of Economics, Umeå University)
    Abstract: Paper [I] tests the success rate of trades and the returns of the Opening Range Breakout (ORB) day trading strategy. A trader that trades the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days, and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game when applied to a long time series of crude oil futures contracts. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of low, high, open and close over a given time horizon. Paper [II] assesses the returns of the Opening Range Breakout (ORB) day trading strategy across volatility states of the underlying asset. We calculate the average daily returns of the ORB strategy for each volatility state when applied on long time series of crude oil and S&P 500 index futures contracts. We find an average difference in returns between the highest and lowest volatility state of around 200 basis points per day for crude oil, and of around 150 basis points per day for the S&P 500. Our result suggests that ORB strategy traders can be profitable, even in the long-run, but that the success in day trading to a large extent depend on the volatility of the underlying asset. Paper [III] performs empirical analysis on short-term and long-term Commodity Trading Advisor (CTA) strategies regarding their exposures to unanticipated risk shocks. Previous research documents that CTA strategies in general offer diversification opportunities during equity market crisis situations when evaluated as a group, but do not separate between short-term and long-term CTA strategies. When separating between short-term and long-term CTA strategies, this paper finds that only short-term CTA strategies provide a significant, and consistent, exposure to unanticipated risk shocks while long-term CTA strategies do not. For the purpose of diversifying a portfolio during equity market crisis situations, our result suggests that an investor should allocate to short-term CTA strategies rather than to long-term CTA strategies. Paper [IV] posits that it is possible to obtain an optimal leverage factor for financial instruments equipped with embedded leverage. By applying the Kelly criterion for optimal leverage, we show that there exists a uniquely optimal level of leverage for maximizing the long-run profit of embedded leverage instruments. The implication of an existing unique optimum is that a smaller leverage factor than optimal leads to a lower long-term profit than is feasible, but also that a larger leverage factor leads to a lower long-term profit than is feasible. Our empirical analysis shows how an optimal level of embedded leverage can increase the profitability of Exchange Traded Products. Paper [V] systematically analyses the effect of leverage on long-run profit when trading the Opening Range Breakout (ORB) day trading strategy. This paper clarifies the relation to two optimal leverage rules proposed for maximizing trading profit; the Kelly criterion and the Optimal fraction criterion. Our empirical analysis shows how leverage can increase day trading profit in-sample and out-of-sample when applied to a long time series of DAX 30 index futures contracts.
    Keywords: Bootstrap; Exchange Traded Products; Kelly criterion; Money management; Opening Range Breakout strategies; Optimal fraction criterion; Time series momentum
    JEL: C22 C58 C63 G11 G14 G17
    Date: 2020–05–06
  4. By: Fabrizio Cipollini; Giampiero M. Gallo; Alessandro Palandri
    Abstract: We build the time series of optimal realized portfolio weights from high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW) model for their dynamics. DCW is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio allocations. Next to portfolio variance, certainty equivalent and turnover, we introduce the break-even transaction costs as an additional measure that identifies the range of transaction costs for which one allocation is preferred to another. By comparing minimum-variance portfolios built on the components of the Dow Jones 30 Index, the proposed DCW overall attains the best allocations with respect to the measures considered, for any degree of risk-aversion, transaction costs and exposure.
    Date: 2020–04
  5. By: Yuri F. Saporito; Rodrigo S. Targino
    Abstract: This paper is concerned with the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). Making use of Malliavin calculus, we recast the traditional Euler contributions from an expectation conditional to an event of zero probability to a ratio of conditional expectations, where both the numerator and the denominator's conditioning events have positive probability. For several different models we show empirically that the estimator using this novel representation has no perceivable bias and variance smaller than a standard estimator used in practice.
    Date: 2020–04
  6. By: Antoine Bommier (ETH Zurich); Daniel Harenberg (ETH Zurich); François Le Grand (EMLyon Business School); Cormac O'Dea (Cowles Foundation, Yale University)
    Abstract: We analyze lifecycle saving strategies using a recursive utility model calibrated to match empirical estimates for the value of a statistical life. We show that, with a positive value of life, risk aversion reduces savings and annuity purchase. Risk averse agents are willing to make an early death a not-so-adverse outcome by enjoying greater consumption when young and bequeathing wealth in case of death. We also ï¬ nd that greater risk aversion lowers stock market participation. We show that this model can rationalize low annuity demand while also matching empirically documented levels of wealth and private investments in stocks. Our ï¬ ndings stand in contrast to studies that implicitly assume a negative value of life.
    Keywords: Recursive utility, Lifecycle model, Value of life, Risk aversion, Saving choices, Portfolio choices, Annuity puzzle
    JEL: D91 G11 J14 J17
    Date: 2020–05
  7. By: Papadamou, Stephanos; Fassas, Athanasios; Kenourgios, Dimitris; Dimitriou, Dimitrios
    Abstract: We investigate the effects of a google trend synthetic index concerning corona virus, as a composite indicator of searching term and theme, on the implied volatility of thirteen major stock markets, covering Europe, Asia, USA and Australia regions by using panel data analysis along with several model specifications and robustness tests. Increased search queries for COVID-19 not only have a direct effect on implied volatility, but also have an indirect effect via stock returns highlighting a risk-aversion channel operating over pandemic conditions. We show that these direct and indirect effects are stronger in Europe relative to the rest of the world. Moreover, in a PVAR framework, a positive shock on stock returns may calm down google searching about COVID-19 in Europe. Our findings suggest that google based anxiety about COVID-19 contagion effects leads to elevated risk-aversion in stock markets. Understanding the links between investors’ decision over a pandemic crisis and asset prices variability is critical for understanding the policy measures needed in markets and economies.
    Keywords: COVID-19 pandemic; google trends; implied volatility; stock returns; panel data
    JEL: C33 D83 G12 G14
    Date: 2020–05–02
  8. By: Daniel Felix Ahelegbey (Università di Pavia); Paolo Giudici (Università di Pavia)
    Abstract: We construct a network-based turbulence score that proves useful for analyzing the relationship between financial interconnectedness, and global market risk, and for identifying systemically important markets, with the highest contribution to financial turbulence. We apply our measure to study the integration among the major stock markets over the first two decades of the 21st century, particularly during the tech, sub-prime, and ongoing COVID-19 crises. The result shows that the interconnectedness of the markets amplifies initial global market risks (on average almost four times), to cause financial turbulence. We also found evidence that the United States is central to global market turbulence, followed by Brazil, France, Hong Kong, and Germany.
    Keywords: Centrality, COVID-19, Density, Financial Crises, Financial Networks, VAR
    JEL: C11 C15 C51 C52 C55 C58 G01 G12
    Date: 2020–05
  9. By: Christos Bouras (Department of Banking and Financial Management, University of Piraeus, 18534, Piraeus, Greece); Christina Christou (School of Economics and Management, Open University of Cyprus, 2252, Latsia, Cyprus); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Keagile Lesame (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: We analyze the ability of an index of mortgage default risks (MDRI) for 43 states and 20 MSAs of the US derived from Google search queries, in predicting (in- and out-of-sample) housing returns of the corresponding states and MSAs, based on various panel data and time-series approaches. In general, our results tend to prefer the panel data model based on common correlated effects estimation. We highlight that growth in MDRI negatively impacts housing returns within-sample, with predictive gains primarily concentrated beyond a year. These results are robust to alternative out-of-sample periods and econometric frameworks. Given the role of house prices as a leading indicators, our results are of value to policymakers, especially at the longer-run.
    Keywords: Mortgage Default Risks, Housing Returns, States and MSAs, Panel Data Predictive Models
    JEL: C23 C53 R31
    Date: 2020–05
  10. By: Boyan Jovanovic; Sai Ma
    Abstract: This paper documents several stylized facts on the real effects of economic uncertainty. First, higher uncertainty is associated with a more dispersed and negatively skewed distribution of output growth. Second, the response of economic growth to an increase in uncertainty is highly nonlinear and asymmetric. Third, higher asset volatility magnifies the negative impact of uncertainty on growth. We develop and estimate an analytically tractable model in which rapid adoption of new technology may raise economic uncertainty which causes measured productivity to decline. The equilibrium growth distribution is negatively skewed and higher uncertainty leads to a thicker left tail.
    Keywords: Uncertainty and growth; Volatility; Downside risk; Growth at risk
    JEL: D80 E44 O40 O47
    Date: 2020–05–07
  11. By: Rowena Crawford (Institute for Fiscal Studies); Cormac O'Dea (Cowles Foundation, Yale University)
    Abstract: Using a lifecycle model of consumption, saving and portfolio choice combined with linked survey and administrative data on wealth and lifetime earnings we evaluate measures of retirement preparedness. We estimate heterogeneous discount factors for households and compare the estimates of their patience to their replacement rates { the simple measure of- ten used to evaluate the adequacy of retirement savings. We ï¬ nd ï¬ rst that the speciï¬ cation of the model’s asset structure matters quantitatively for preference parameter estimates { households appear to be much more patient when they are assumed to have access only to a risk-free asset compared to when we account for the fact that much of their wealth is stored in higher-return tax-advantaged private pensions and in housing. Second we ï¬ nd that only the most patient households achieve the replacement rates out of ï¬ nal earnings that are often recommended by policy-makers and industry as sensible benchmarks for retirement preparedness. Notwithstanding this, we ï¬ nd that even quite impatient households in the population we study achieve high replacement rates out of lifetime average income { a more sensible summary measure of preparedness for retirement.
    JEL: D91 D31 E21 D14 H55
    Date: 2020–01
  12. By: Thomas Krabichler; Josef Teichmann
    Abstract: This article introduces a new mathematical concept of illiquidity that goes hand in hand with credit risk. The concept is not volume- but constraint-based, i.e., certain assets cannot be shorted and are ineligible as num\'eraire. If those assets are still chosen as num\'eraire, we arrive at a two-price economy. We utilise Jarrow & Turnbull's foreign exchange analogy that interprets defaultable zero-coupon bonds as a conversion of non-defaultable foreign counterparts. In the language of structured derivatives, the impact of credit risk is disabled through quanto-ing. In a similar fashion, we look at bond prices as if perfect liquidity was given. This corresponds to asset pricing with respect to an ineligible num\'eraire and necessitates F\"ollmer measures.
    Date: 2020–04
  13. By: Nicolas Soenen; Rudi Vander Vennet (-)
    Abstract: In this paper we empirically analyze the determinants of bank default risk (measured by the banks’ CDS spreads) for European banks during the period 2008-2018. We examine the effect of (1) bank business model characteristics, (2) sovereign default risk and (3) ECB monetary policy. We disentangle the effect of monetary policy in a direct channel and an indirect effect operating through a sovereign risk channel. In terms of business model variables, we find that the capital ratio and the reliance on stable deposits lowers the perceived default risk of banks, while non-performing loans significantly increase the CDS spreads. Hence, the CDS market distinguishes resilient banks from risky banks. In terms of monetary policy, we document that accommodative ECB actions in general lower bank default risk. We also show that the downward effect of monetary policy on bank risk is mainly transmitted through the sovereign risk channel. Our findings confirm the importance of the Basel 3 capital and stable funding rules and they suggest policy implications in terms of bank business model choices as well as approaches to tackle the bank-sovereign loop in Europe.
    Keywords: banks, credit risk, bank business model, monetary policy, sovereign risk
    JEL: G01 G1 G12 G21 E52
    Date: 2020–05
  14. By: Ruiz Ortega, Esther; Nieto Delfin, Maria Rosa
    Abstract: Although the Basel Accords require financial institutions to report daily predictions ofValue at Risk (VaR) computed using ten-day returns, a vast part of the literature deals withVaR predictions based on one-day returns. From the practitioner point of view, some ofthe conclusions about the best methods to estimate one-period VaR could not be directlygeneralized to multi-period VaR. Consequently, in the context of two-step VaR predictors,we use simulated and real data to compare direct and iterated predictions of multi-periodVaR based on ten-day returns assuming that the conditional variances of one-period returnsfollow a GARCH-type model. We show that multiperiod VaR predictions based on iteratingan asymmetric GJR model with normal or bootstrapped errors are often preferred whencompared with direct methods that are often biased and inefficient.
    Keywords: Risk; Multi-Step Forecasts; Gjr Model; Feasible Historical Simulation
    JEL: C58 C53 C22 G17
    Date: 2020–05–07
  15. By: Vlastakis, Nikolaos; Triantafyllou, Athanasios; Kellard, Neil
    Abstract: The role of oil price shocks in US economic activity and inflation is controversial but a key input to current economic policy. To clarify these relations, we employ a more refined measure of oil shocks based on decomposing highly accurate realized volatility estimated using intraday oil futures data. In reconciling prior results, we find that shocks driven by price increases (decreases) are associated with rising (falling) inflation while only a symmetric volatility channel affects economic activity.
    Date: 2020–05–07
  16. By: Abreu, Rafael Costa Berriel; Costa, Carlos Eugênio da
    Abstract: How is aggregate risks optimally shared between workers and retirees? We break this question in two parts. First, how ought risk to be shared between two groups of agents: one which must be provided incentives to make effort and other, which no longer be incentivized? Second, since incentives may be backloaded through pension entitlements, how does backloading optimally vary across states of nature? After formalizing these two aspects of the problem, we show that perfect risk sharing is optimal for log utility and when aggregate productivity growth is i.i.d.. For all other cases, departures from perfect risk sharing are welfare improving if more risk is born by retirees (resp. workers) when productivity growth is persistent (resp. mean reverting). Our numerical implementations however suggest that perfect risk sharing is approximately optimal for commonly used parameter values.
    Date: 2020–05–05

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