nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒08‒31
thirty-one papers chosen by

  1. Portfolio Optimization on the Dispersion Risk and the Asymmetric Tail Risk By Young Shin Kim
  2. A Natural Disasters Index By Thilini V. Mahanama; Abootaleb Shirvani
  3. The Greek Letters. Scenario Analysis with a Reverse Butterfly Spread. By Rashid, Muhammad Mustafa
  4. Modeling Portfolio Loss by Interval Distributions By Yang, Bill Huajian; Yang, Jenny; Yang, Haoji
  5. Deep Hedging of Long-Term Financial Derivatives By Alexandre Carbonneau
  6. Benchmarked Risk Minimizing Hedging Strategies for Life Insurance Policies By Jin Sun; Eckhard Platen
  7. Equity Tail Risk in the Treasury Bond Market By Mirco Rubin; Dario Ruzzi
  8. Conditional tail risk expectations for location-scale mixture of elliptical distributions By Baishuai Zuo; Chuancun Yin
  9. Are Characteristics Covariances or Characteristics? By Lars Hornuf; Christian Fieberg
  10. Nonparametric Inference of Jump Autocorrelation By Kwok, Simon
  11. Banking risk management between the prudential and the operational approaches : case of Moroccan banks ACHIBANE Mustapha By Mustapha Achibane; Imane Allam
  12. Supervised Machine Learning Techniques: An Overview with Applications to Banking By Linwei Hu; Jie Chen; Joel Vaughan; Hanyu Yang; Kelly Wang; Agus Sudjianto; Vijayan N. Nair
  13. The interbank market, Keynes’s degree of confidence and the link between banks’ liquidity and solvency By Konstantinos Loizos
  14. Discussing copulas with Sergey Aivazian: a memoir By Fantazzini, Dean
  15. Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach By Chen, Yanhua; Li, Youwei; Pantelous, Athanasios; Stanley, Eugene
  16. Pricing foreseeable and unforeseeable risks in insurance portfolios By Weihong Ni; Corina Constantinescu; Alfredo Eg\'idio dos Reis; V\'eronique Maume-Deschamps
  17. Distributionally Robust Markov Decision Processes and their Connection to Risk Measures By Nicole B\"auerle; Alexander Glauner
  18. Size-biased transform and conditional mean risk sharing, with application to P2P insurance and tontines By Denuit, Michel
  19. Economic Determinants of Oil Futures Volatility: A Term Structure Perspective By Boda Kang; Christina Sklibosios Nikitopoulos; Marcel Prokopczuk
  20. Cost Pass-through in Commercial Aviation: Theory and Evidence By Gayle, Philip; Lin, Ying
  21. Discounting Damage: Non-Linear Discounting and Default Compensation. Valuation of Non-Replicable Value and Damage By Christian P. Fries
  22. Expected Value Under Normative Uncertainty By Franz Dietrich; Brian Jabarian
  23. Log-modulated rough stochastic volatility models By Christian Bayer; Fabian Andsem Harang; Paolo Pigato
  24. Credit risk modelling with fractional self-excited processes By Hainaut, Donatien
  25. Saving Markowitz: A Risk Parity approach based on the Cauchy Interlacing Theorem By Fernando Fernandes; Rodrigo De Losso, Rogerio Oliveira, Angelo J D Soto, Pedro D Cavalcanti, Gabriel M S Campos
  26. Risks on Others By Takaaki HAMADA; Tomohiro HARA
  27. Optimal allocation using the Sortino ratio By Tarek Nassar; Sandro Ephrem
  28. Empirical tail copulas for functional data By Einmahl, John; Segers, Johan
  29. Economic Uncertainty before and during the COVID-19 Pandemic By David E. Altig; Scott Baker; Jose Maria Barrero; Nick Bloom; Phil Bunn; Scarlet Chen; Steven J. Davis; Brent Meyer; Emil Mihaylov; Paul Mizen; Nicholas B. Parker; Pawel Smietanka; Greg Thwaites
  30. Predicting Recessions: A New Measure of Output Gap as Predictor By Mastromarco, Camilla; Simar, Leopold; Wilson, Paul
  31. A decomposition formula for fractional Heston jump diffusion models By Marc Lagunas-Merino; Salvador Ortiz-Latorre

  1. By: Young Shin Kim
    Abstract: In this paper, we propose a multivariate market model with returns assumed to follow a multivariate normal tempered stable distribution defined by a mixture of the multivariate normal distribution and the tempered stable subordinator. This distribution is able to capture two stylized facts: fat-tails and asymmetric tails, that have been empirically observed for asset return distributions. On the new market model, a new portfolio optimization method, which is an extension of Markowitz's mean-variance optimization, is discussed. The new optimization method considers not only reward and dispersion but also asymmetry. The efficient frontier is also extended from the mean-variance curve to a surface on three dimensional space of reward, dispersion, and asymmetry. We also propose a new performance measure which is an extension of Sharpe Ratio. Moreover, we derive closed-form solutions for two important measures used by portfolio managers in portfolio construction: the marginal Value-at-Risk (VaR) and the marginal Conditional VaR (CVaR). We illustrate the proposed model using stocks comprising the Dow Jones Industrial Average. First perform the new portfolio optimization and then demonstrating how the marginal VaR and marginal CVaR can be used for portfolio optimization using the model. Based on the empirical evidence presented in this paper, our framework offers realistic portfolio optimization and tractable methods for portfolio risk management.
    Date: 2020–07
  2. By: Thilini V. Mahanama; Abootaleb Shirvani
    Abstract: Natural disasters, such as tornadoes, floods, and wildfire pose risks to life and property, requiring the intervention of insurance corporations. One of the most visible consequences of changing climate is an increase in the intensity and frequency of extreme weather events. The relative strengths of these disasters are far beyond the habitual seasonal maxima, often resulting in subsequent increases in property losses. Thus, insurance policies should be modified to endure increasingly volatile catastrophic weather events. We propose a Natural Disasters Index (NDI) for the property losses caused by natural disasters in the United States based on the "Storm Data" published by the National Oceanic and Atmospheric Administration. The proposed NDI is an attempt to construct a financial instrument for hedging the intrinsic risk. The NDI is intended to forecast the degree of future risk that could forewarn the insurers and corporations allowing them to transfer insurance risk to capital market investors. This index could also be modified to other regions and countries.
    Date: 2020–08
  3. By: Rashid, Muhammad Mustafa
    Abstract: The management of risk is the goal of a financial institution that sells an option to a client in the over-the-counter markets. In addition to monitoring risks such as Delta( ), Gamma ( ) and Vega(v), option traders often also carry out, a scenario analysis. The analysis involves calculating the gain or loss on their portfolio over a specified period under a variety of different scenarios. The time period chosen is likely to depend on the liquidity of the instrument. The scenarios can either be chose by management or generated by a model.
    Keywords: Financial Institutions, Scenario Analysis, Risk Management, Portfolio Management, Reverse Butterfly Spread.
    JEL: G10 G11 G17 G2 H2
    Date: 2020–03–19
  4. By: Yang, Bill Huajian; Yang, Jenny; Yang, Haoji
    Abstract: Models for a continuous risk outcome has a wide application in portfolio risk management and capital allocation. We introduce a family of interval distributions based on variable transformations. Densities for these distributions are provided. Models with a random effect, targeting a continuous risk outcome, can then be fitted by maximum likelihood approaches assuming an interval distribution. Given fixed effects, regression function can be estimated and derived accordingly when required. This provides an alternative regression tool to the fraction response model and Beta regression model.
    Keywords: Interval distribution, model with a random effect, tailed index, expected shortfall, heteroscedasticity, Beta regression model, fraction response model, maximum likelihood.
    JEL: C0 C01 C02 C5 C51 C53 C6 C61 C8
    Date: 2020–07–20
  5. By: Alexandre Carbonneau
    Abstract: This study presents a deep reinforcement learning approach for global hedging of long-term financial derivatives. A similar setup as in Coleman et al. (2007) is considered with the risk management of lookback options embedded in guarantees of variable annuities with ratchet features. The deep hedging algorithm of Buehler et al. (2019a) is applied to optimize neural networks representing global hedging policies with both quadratic and non-quadratic penalties. To the best of the author's knowledge, this is the first paper that presents an extensive benchmarking of global policies for long-term contingent claims with the use of various hedging instruments (e.g. underlying and standard options) and with the presence of jump risk for equity. Monte Carlo experiments demonstrate the vast superiority of non-quadratic global hedging as it results simultaneously in downside risk metrics two to three times smaller than best benchmarks and in significant hedging gains. Analyses show that the neural networks are able to effectively adapt their hedging decisions to different penalties and stylized facts of risky asset dynamics only by experiencing simulations of the financial market exhibiting these features. Numerical results also indicate that non-quadratic global policies are significantly more geared towards being long equity risk which entails earning the equity risk premium.
    Date: 2020–07
  6. By: Jin Sun; Eckhard Platen (Finance Discipline Group, UTS Business School, University of Technology Sydney)
    Abstract: Traditional life insurance policies offer no equity investment opportunities for the premium paid, and suffer from low returns over the long insurance terms. Modern equity-linked insurance policies offer equity investment opportunities exposed to equity market risk. To combine the low-risk of traditional policies with the high returns offered by equity-linked policies, we consider insurance policies under the benchmark approach (BA), where the policyholders’ funds are invested in the growth-optimal portfolio and the locally risk-free savings account. Under the BA, life insurance policies can be delivered at their minimal costs, lower than the classical actuarial theory predicts. Due to unhedgeable mortality risk, life insurance policies cannot be fully hedged. In this case benchmarked risk-minimization can be applied to obtain hedging strategies with minimally fluctuating profit and loss processes, where the fluctuations can further be reduced through diversification.
    Keywords: benchmark approach; benchmarked risk minimization; life insurance; mortality model
    JEL: G13 G22
    Date: 2019–03–01
  7. By: Mirco Rubin; Dario Ruzzi
    Abstract: This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk with option-implied stock market volatility that stems from large negative price jumps, and we assess its value in reduced-form predictive regressions for Treasury returns and a term structure model for interest rates. We find that the left tail volatility of the stock market significantly predicts one-month excess returns on Treasuries both in- and out-of-sample. The incremental value of employing equity tail risk as a return forecasting factor can be of economic importance for a mean-variance investor trading bonds. The estimated term structure model shows that equity tail risk is priced in the US government bond market and, consistent with the theory of flight-to-safety, Treasury prices increase when the perception of tail risk is higher. Our results concerning the predictive power and pricing of equity tail risk extend to major government bond markets in Europe.
    Date: 2020–07
  8. By: Baishuai Zuo; Chuancun Yin
    Abstract: We present general results on the univariate tail conditional expectation (TCE) and multivariate tail conditional expectation for location-scale mixture of elliptical distributions. Examples include the location-scale mixture of normal distributions, location-scale mixture of Student-$t$ distributions, location-scale mixture of Logistic distributions and location-scale mixture of Laplace distributions. We also consider portfolio risk decomposition with TCE for location-scale mixture of elliptical distributions.
    Date: 2020–07
  9. By: Lars Hornuf; Christian Fieberg
    Abstract: In this article, we shed more light on the covariances versus characteristics debate by investigating the explanatory power of the instrumented principal component analysis (IPCA), recently proposed by Kelly et al. (2019). They conclude that characteristics are covariances because there is no residual return predictability from characteristics above and beyond that in factor loadings. Our findings indicate that there is no residual return predictability from factor loadings above and beyond that in characteristics either. In particular, we find that stock returns are best explained by characteristics (characteristics are characteristics) and that a one-factor IPCA model is sufficient to explain stock risk (characteristics are covariances). We therefore conclude that characteristics are covariances or characteristics, depending on whether the goal is to explain stock returns or risk.
    Keywords: cross-section of stock returns, covariances, characteristics, IPCA
    JEL: C23 G11 G12
    Date: 2020
  10. By: Kwok, Simon
    Abstract: Understanding the jump dynamics of market prices is important for asset pricing and risk management. Despite their analytical tractability, parametric models may impose unrealistic restrictions on the temporal dependence structure of jumps. In this paper, we introduce a nonparametric inference procedure for the presence of jump autocorrelation in the DGP. Our toolkit includes (i) an omnibus test that jointly detect the autocorrelation of stationary jumps over all lags, and (ii) a jump autocorrelogram that enables visualization and pointwise inference of jump autocorrelation. We establish asymptotic normality and local power of our procedure for a rich set of local alternatives (e.g., self-exciting and/or self-inhibitory jumps). Under a unified framework that combines infill and long-span asymptotics, the joint test for jump autocorrelations is robust to the choice of sampling scheme and different degree of jump activity. Simulation study confirms its robustness property and reveals its competitive size and power performance relative to existing tests. In an empirical study on high-frequency stock returns, our procedure uncovers a wide array of jump autocorrelation profiles for different stocks in different time periods.
    Keywords: jump autocorrelation, self-excited jumps, nonparametric inference, financial contagion, high-frequency returns
    Date: 2020–08
  11. By: Mustapha Achibane (UIT - Université Ibn Tofaïl); Imane Allam (UIT - Université Ibn Tofaïl)
    Date: 2019–09–28
  12. By: Linwei Hu; Jie Chen; Joel Vaughan; Hanyu Yang; Kelly Wang; Agus Sudjianto; Vijayan N. Nair
    Abstract: This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques. This is followed by a description of: i) tree-based ensemble algorithms including Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion of hyper-parameter optimization techniques, and iv) machine learning interpretability. The paper concludes with a comparison of the features of different ML algorithms. Examples taken from credit risk modeling in banking are used throughout the paper to illustrate the techniques and interpret the results of the algorithms.
    Date: 2020–07
  13. By: Konstantinos Loizos (University of Athens (GR))
    Abstract: The link between banks’ liquidity and solvency is not adequately addressed in the literature, despite the central role of the interbank market in the spread of the recent crisis. This paper proposes a possible way by which the interbank rate and the required return on equity capital are determined, and are related to each other. Thereby, a link between liquidity and insolvency risk is derived on the grounds of Keynes's concept of ‘degree of confidence’ on held expectations about economic prospects. High degree of confidence and trust prevailing in the interbank market makes risk sharing possible at prices which render bank capital regulation ineffective in the rising phase of the cycle, and overly restricted in the downswing. Basel’s III higher capital, liquidity and leverage ratios might not be enough if measures, in the sense of Minsky’s Big Government-Big Bank, targeting overconfidence in booms and redressing the lack of confidence in the downturns are not taken into account.
    Keywords: Degree of confidence, Interbank market, Liquidity preference, Insolvency risk, Financial cycles
    JEL: E12 E32 G21
    Date: 2020–08
  14. By: Fantazzini, Dean
    Abstract: Sergey Aivazian was the head of my department at the Moscow School of Economics, but he was much more than that. He played an important role in my life, and he contributed to my studies devoted to copula modelling. This small memoir reports how this amazingly polite and smart scientist helped me to develop my academic skills and to further stimulate my interest in multivariate modelling and risk management. Some open questions related to multivariate discrete models that were among the last topics I discussed with Sergey are reported, hoping they can be of interest to young researchers for further studies.
    Keywords: Copula; multivariate models; market risk; operational risk; discrete distribution; risk management
    JEL: C32 C51 C53 C58 G17 G32 G33
    Date: 2020–08
  15. By: Chen, Yanhua; Li, Youwei; Pantelous, Athanasios; Stanley, Eugene
    Abstract: In this paper, we propose a network-based analytical framework that exploits cointegration and the error correction model to systematically investigate the directional interconnectedness of the short-run disequilibrium adjustment towards long-run equilibrium affecting the international stock market during the period of 5 January 2007 to 30 June 2017. Under this setting, we investigate whether and how the cross-border directional interconnectedness within the world's 23 developed and 23 emerging stock markets altered during the 2007-2009 Global Financial Crisis, 2010-2012 European Sovereign Debt Crisis, and the entire period of 2007-2017. The main results indicate that changes in directional interconnectedness within stock markets worldwide did occur under the impact of the recent financial crises. The extent of the short-run disequilibrium adjustment towards long-run equilibrium for individual stock markets is not homogeneous over different time scales. The derived networks of stock markets interconnectedness allow us to visually characterize how specific stock markets from different regions form interconnected groups when exhibiting similar behaviours, which none the less provides significant information for strategic portfolio and risk management.
    Keywords: International Stock Markets; Cointegration; Error Correction Model; Complex Network Theory; Financial Crisis
    JEL: C12 G01 G15
    Date: 2020–05–29
  16. By: Weihong Ni (ICJ, PSPM); Corina Constantinescu (ICJ, PSPM); Alfredo Eg\'idio dos Reis (ICJ, PSPM); V\'eronique Maume-Deschamps (ICJ, PSPM)
    Abstract: In this manuscript we propose a method for pricing insurance products that cover not only traditional risks, but also unforeseen ones. By considering the Poisson process parameter to be a mixed random variable, we capture the heterogeneity of foreseeable and unforeseeable risks. To illustrate, we estimate the weights for the two risk streams for a real dataset from a Portuguese insurer. To calculate the premium, we set the frequency and severity as distributions that belong to the linear exponential family. Under a Bayesian setup , we show that when working with a finite mixture of conjugate priors, the premium can be estimated by a mixture of posterior means, with updated parameters, depending on claim histories. We emphasise the riskiness of the unforeseeable trend, by choosing heavy-tailed distributions. After estimating distribution parameters involved using the Expectation-Maximization algorithm, we found that Bayesian premiums derived are more reactive to claim trends than traditional ones.
    Date: 2020–07
  17. By: Nicole B\"auerle; Alexander Glauner
    Abstract: We consider robust Markov Decision Processes with Borel state and action spaces, unbounded cost and finite time horizon. Our formulation leads to a Stackelberg game against nature. Under integrability, continuity and compactness assumptions we derive a robust cost iteration for a fixed policy of the decision maker and a value iteration for the robust optimization problem. Moreover, we show the existence of deterministic optimal policies for both players. This is in contrast to classical zero-sum games. In case the state space is the real line we show under some convexity assumptions that the interchange of supremum and infimum is possible with the help of Sion's minimax Theorem. Further, we consider the problem with special ambiguity sets. In particular we are able to derive some cases where the robust optimization problem coincides with the minimization of a coherent risk measure. In the final section we discuss two applications: A robust LQ problem and a robust problem for managing regenerative energy.
    Date: 2020–07
  18. By: Denuit, Michel
    Date: 2019–01–01
  19. By: Boda Kang; Christina Sklibosios Nikitopoulos (Finance Discipline Group, UTS Business School, University of Technology Sydney); Marcel Prokopczuk
    Abstract: To assess the economic determinants of oil futures volatility, we firstly develop and estimate a multi-factor oil futures pricing model with stochastic volatility that is able to disentangle long-term, medium-term and short-term variations in commodity markets volatility. The volatility estimates reveal that in line with theory, the volatility factors are unspanned, persistent and carry negative market price of risk, while crude oil markets are becoming more integrated with financial markets. After 2004, short-term volatility is driven by industrial production, term and credit spreads, the S&P 500 and the US dollar index, along with the traditional drivers including hedging pressure and VIX. Medium-term volatility is consistently related to open interest and credit spreads, while after 2004 oil sector variables such as inventory and consumption also impact this part of the term structure. Interest rates mostly matter for long-term futures price volatility.
    Keywords: oil market; volatility; term structure; macroeconomy
    JEL: G12 G13 C58 Q40
    Date: 2019–07–01
  20. By: Gayle, Philip; Lin, Ying
    Abstract: The significant worldwide decline in crude oil price beginning in mid-2014 through to 2015, which resulted in substantial fuel expense reductions for airlines, but no apparent commensurate reductions in industry average airfares has caused much public debate. This paper examines the market mechanisms through which crude oil price may influence airfare, which facilitates identifying the possible market and airline-specific characteristics that influence the extent to which crude oil price changes affect airfare. Interestingly, and new, our analysis reveals that the crude oil-airfare pass-through relationship can be either positive or negative, depending on various market and airline-specific characteristics. We find evidence that airline-specific jet fuel hedging strategy and market origin-destination distance contribute significantly to pass-through rates being negative. Specifically, the value of pass-through rate decreases with airline fuel hedging ratios and with market origin-destination distance, but increases with competition in origin-destination markets. Even when the pass-through relationship is positive, suggesting that a portion of airlines’ fuel cost savings is passed on to consumers via lower airfares, this research reveals the market and airline-specific factors that limit the size of these savings passed on to consumers via lower airfares.
    Keywords: Crude oil price-Airfare Cost Pass-through; Jet fuel hedging
    JEL: L13 L93
    Date: 2020
  21. By: Christian P. Fries
    Abstract: In this short note we develop a model for discounting. A focus of the model is the discounting, when discount factors cannot be derived from market products. That is, a risk-neutralizing trading strategy cannot be performed. This is the case, when one is in need of a risk-free (default-free) discounting, but default protection on funding providers is not traded. For this case, we introduce a default compensation factor ($\exp(+\tilde{\lambda} T)$) that describes the present value of a strategy to compensate for default (like buying default protection would do). In a second part, we introduce a model, where the survival probability depends on the required notional. This model is different from the classical modelling of a time-dependent survival probability ($\exp(-\lambda T)$). The model especially allows that large liquidity requirements are instantly more likely do default than small ones. Combined the two approaches build a framework in which discounting (valuation) is non-linear. The framework can lead to the effect that discount-factors for very large liquidity requirements or projects are an increasing function of time.
    Date: 2020–07
  22. By: Franz Dietrich (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics, CNRS - Centre National de la Recherche Scientifique); Brian Jabarian (PSE - Paris School of Economics, Princeton University)
    Abstract: Maximising expected value is the classic doctrine in choice theory under empirical uncertainty, and a prominent proposal in the emerging philosophical literature on normative uncertainty, i.e., uncertainty about the standard of evaluation. But how should Expectationalism be stated in general, when we can face both uncertainties simultaneously, as in common in life? Surprisingly, different possibilities arise, ranging from Ex-Ante to Ex-Post Expectationalism, with several hybrid versions. The difference lies in the perspective from which expectations are taken, or equivalently the amount of uncertainty packed into the prospect evaluated. Expectationalism thus faces the classic dilemma between ex-ante and ex-post approaches, familiar elsewhere in ethics and aggregation theory under uncertainty. We analyse the spectrum of expectational theories, showing that they reach diverging evaluations, use different modes of reasoning, take different attitudes to normative risk as well as empirical risk, but converge under an interesting (necessary and sufficient) condition.
    Keywords: normative versus empirical uncertainty,expected value theory,expectationalism,ex-ante versus ex-post approach
    Date: 2020–07
  23. By: Christian Bayer; Fabian Andsem Harang; Paolo Pigato
    Abstract: We propose a new class of rough stochastic volatility models obtained by modulating the power-law kernel defining the fractional Brownian motion (fBm) by a logarithmic term, such that the kernel retains square integrability even in the limit case of vanishing Hurst index $H$. The so-obtained log-modulated fractional Brownian motion (log-fBm) is a continuous Gaussian process even for $H = 0$. As a consequence, the resulting super-rough stochastic volatility models can be analysed over the whole range $0 \le H
    Date: 2020–08
  24. By: Hainaut, Donatien
    Date: 2020–01–01
  25. By: Fernando Fernandes; Rodrigo De Losso, Rogerio Oliveira, Angelo J D Soto, Pedro D Cavalcanti, Gabriel M S Campos
    Abstract: It is well known that Markowitz Portfolio Optimization often leads to unreasonable and unbalanced portfolios that are optimal in-sample but perform very poorly out-of-sample. There is a strong relationship between these poor returns and the fact that covariance matrices that are used within the Markowitz framework are degenerated and ill-posed, leading to unstable results by inverting them, as a consequence of very small eigenvalues. In this paper we circumvent this problem in two steps: the enhancement of traditional risk parity techniques, which consider only volatility, aiming to avoid matrix inversions (including the widespread Naive Risk Parity model) within the Markowitz framework; the preservation of the correlation structure, as much as possible, aiming to isolate a "healthy" portion of the correlation matrix, that can be inverted without generating unstable and risky portfolios, aiming to rescue the original Markowitz framework, by means of using the Cauchy Interlacing Theorem. Using Brazilian and US market data, we show that the discussed framework enables one to build portfolios that outperform the traditional and the newest risk parity techniques.
    Keywords: Markowitz; Cauchy Interlacing Theorem; NRP; CIRP
    JEL: C38 C61 G11 G17
    Date: 2020–08–18
  26. By: Takaaki HAMADA (Faculty of Management and Administration, Shumei University, Research Institute for Economics and Business Administration, Kobe University); Tomohiro HARA (Department of Economics, University of Maryland at College Park, U.S.A.)
    Abstract: We investigate other regarding preferences when others are involved in some risks. We introduce two concepts to examine risk attitudes towards others: "absolute level of risk attitudes toward others" (ARAO) which capture general risk attitudes toward recipients' risky payoffs, and "relative level of risk attitudes toward others" (RRAO) which capture behavioral difference depending on types of risks conditional on same expected outcomes. For RRAO, we compare two different types of risks: "state risk" where recipients' initial endowments are risky and "non-state risk" where recipients' states are not risky but transfers are risky. We provide a novel experimental design to measure both ARAO and RRAO, and theoretically explore potential mechanisms behind behaviors based on these concepts by extending representative inequality aversion models under risks. In our experiment, we find decision makers exhibit robust risk averse behaviors on ARAO, which cannot be predicted by existing theories. Yet, we find no strong evidence on RRAO.
    Keywords: Other-regarding preferences; Charity; Risk preferences; Cognitive bias
    Date: 2020–08
  27. By: Tarek Nassar; Sandro Ephrem
    Abstract: In this paper we present an asset allocation strategy based on the maximization of the Sortino ratio. Unlike the Sharpe ratio, the Sortino ratio penalizes negative return variances only. The resulting allocation is valid for any time horizon unlike. The returns of a strategy based on such an allocation are empirically illustrated using historical Dow Jones data and display a significant upgrade on more traditional allocation strategies such as the Kelly criterion.
    Date: 2020–07
  28. By: Einmahl, John; Segers, Johan
    Date: 2020–01–01
  29. By: David E. Altig; Scott Baker; Jose Maria Barrero; Nick Bloom; Phil Bunn; Scarlet Chen; Steven J. Davis; Brent Meyer; Emil Mihaylov; Paul Mizen; Nicholas B. Parker; Pawel Smietanka; Greg Thwaites
    Abstract: We consider several economic uncertainty indicators for the United States and the UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based economic policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about future business growth, and disagreement among professional forecasters about future gross domestic product growth. Three results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly—from an 80 percent rise (relative to January 2020) in two-year implied volatility on the S&P 500 to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: implied volatility rose rapidly from late February and peaked in mid-March, falling back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street.
    Keywords: volatility; COVID-19; coronavirus; forward-looking uncertainty measures
    JEL: E22 E66 G18 L50 D80
    Date: 2020–07–10
  30. By: Mastromarco, Camilla; Simar, Leopold; Wilson, Paul
    Date: 2019–01–01
  31. By: Marc Lagunas-Merino; Salvador Ortiz-Latorre
    Abstract: We present an option pricing formula for European options in a stochastic volatility model. In particular, the volatility process is defined using a fractional integral of a diffusion process and both the stock price and the volatility processes have jumps in order to capture the market effect known as leverage effect. We show how to compute a martingale representation for the volatility process. Finally, using It\^o calculus for processes with discontinuous trajectories, we develop a first order approximation formula for option prices. There are two main advantages in the usage of such approximating formulas to traditional pricing methods. First, to improve computational effciency, and second, to have a deeper understanding of the option price changes in terms of changes in the model parameters.
    Date: 2020–07

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