nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒09‒21
23 papers chosen by
Stan Miles
Thompson Rivers University

  1. Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid By Marius Lux; Wolfgang Karl H\"ardle; Stefan Lessmann
  2. Dependent Conditional Value-at-Risk for Aggregate Risk Models By Bony Josaphat; Khreshna Syuhada
  3. BITCOIN: Systematic Force of Cryptocurrency Portfolio By Tomić, Bojan
  4. Multi-utility representations of incomplete preferences induced by set-valued risk measures By Cosimo Munari
  5. A Markov-Chain Measure of Systemic Banking Crisis Frequency By Tambakis, D.
  6. Forecasting financial markets with semantic network analysis in the COVID-19 crisis By A. Fronzetti Colladon; S. Grassi; F. Ravazzolo; F. Violante
  7. Deep Replication of a Runoff Portfolio By Thomas Krabichler; Josef Teichmann
  8. The Implications of oil market volatility on the credit risk of some oil-exporting countries By Ibrahima Bah; Jules Sadefo Kamdem; Abdou Salam Diallo
  9. Nonparametric Predictive Inference for Asian options By Ting He
  10. Investing with Cryptocurrencies -- evaluating their potential for portfolio allocation strategies By Alla Petukhina; Simon Trimborn; Wolfgang Karl H\"ardle; Hermann Elendner
  11. A New Approach to Estimating Loss-Given-Default Distribution By Masahiko Egami; Rusudan Kevkhishvili
  12. Power-type derivatives for rough volatility with jumps By Weixuan Xia
  13. A note on large deviations in life insurance By Stefan Gerhold
  14. COVID-19: Tail Risk and Predictive Regressions By Walter Distaso; Rustam Ibragimov; Alexander Semenov; Anton Skrobotov
  15. Pricing ambiguity in catastrophe risk insurance By Dietz, Simon; Niehörster, Falk
  16. Fairness principles for insurance contracts in the presence of default risk By Delia Coculescu; Freddy Delbaen
  17. The use of technology and innovative approaches in disaster and risk management: a characterization of Caribbean countries’ experiences By Fontes de Meira, Luciana; Bello, Omar
  18. Exchange Rate Predictability, Risk Premiums, and Predictive System By Yuhyeon Bak; Cheolbeom Park
  19. Organizing insurance supply for new and undiversifiable risks By David Alary; Catherine Bobtcheff; Carole Haritchabalet
  20. Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning By Eric Benhamou; David Saltiel; Jean-Jacques Ohana; Jamal Atif
  21. Optimal Dynamic Capital Requirements and Implementable Capital Buffer Rules By Matthew B. Canzoneri; Behzad T. Diba; Luca Guerrieri; Arsenii Mishin
  22. Multivariate Stochastic Volatility with Co-Heteroscedasticity By CHAN Joshua; DOUCET Arnaud; Roberto Leon-Gonzalez; STRACHAN Rodney W.
  23. Random Non-Expected Utility: Non-Uniqueness By Yi-Hsuan Lin

  1. By: Marius Lux; Wolfgang Karl H\"ardle; Stefan Lessmann
    Abstract: Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is Value-at-Risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated by the standard generalized autoregressive conditional heteroscedasticity (GARCH) formulation. Based on this, VaR is derived by applying kernel density estimation (KDE). This approach allows for flexible tail shapes of the profit and loss distribution, adapts for a wide class of tail events and is able to capture complex structures regarding mean and volatility. The SVR-GARCH-KDE hybrid is compared to standard, exponential and threshold GARCH models coupled with different error distributions. To examine the performance in different markets, one-day-ahead and ten-days-ahead forecasts are produced for different financial indices. Model evaluation using a likelihood ratio based test framework for interval forecasts and a test for superior predictive ability indicates that the SVR-GARCH-KDE hybrid performs competitive to benchmark models and reduces potential losses especially for ten-days-ahead forecasts significantly. Especially models that are coupled with a normal distribution are systematically outperformed.
    Date: 2020–09
  2. By: Bony Josaphat; Khreshna Syuhada
    Abstract: Risk measure forecast and model have been developed in order to not only provide better forecast but also preserve its (empirical) property especially coherent property. Whilst the widely used risk measure of Value-at-Risk (VaR) has shown its performance and benefit in many applications, it is in fact not a coherent risk measure. Conditional VaR (CoVaR), defined as mean of losses beyond VaR, is one of alternative risk measures that satisfies coherent property. There has been several extensions of CoVaR such as Modified CoVaR (MCoVaR) and Copula CoVaR (CCoVaR). In this paper, we propose another risk measure, called Dependent CoVaR (DCoVaR), for a target loss that depends on another random loss, including model parameter treated as random loss. It is found that our DCoVaR outperforms than both MCoVaR and CCoVaR. Numerical simulation is carried out to illustrate the proposed DCoVaR. In addition, we do an empirical study of financial returns data to compute the DCoVaR forecast for heteroscedastic process.
    Date: 2020–09
  3. By: Tomić, Bojan
    Abstract: Cryptocurrencies represent a new type of digital asset that cannot be linked to the framework of fundamental and systematic factors of existing financial instruments of the traditional capital market. Due to the lack of strictly defined fundamental indicators, supported by the results of research by the academic community, considering cryptocurrencies as investment opportunities can put investors in a subordinate position, a situation of complete uncertainty. Cryptocurrencies and their entire technical infrastructure are still a kind of unknown to the general public. Due to this, but also the lack of a regulatory framework, investors have to rely on sometimes uncertain information gathered through various media platforms. However, regardless of the type of assets and the mentioned shortcomings, when constructing a portfolio, investors should consider the dynamics of returns of potential components of the portfolio in order to identify and quantify the assumed investment risk and define the expected return. Cryptocurrencies are based on the idea of decentralization initially introduced by bitcoin blockchain technology and as such have their own historical sequence of origin. Since bitcoin is the first digital currency based on asymmetric cryptography, the change in its value can serve as a leading indicator of the movement of the cryptocurrency market as a whole. Accordingly, this paper will formally identify and describe the performance of the cryptocurrency portfolio with different optimization goals taking into account the assumption of a significant systematic impact of bitcoin cryptocurrency on the dynamics of the value of the aggregate secondary cryptocurrency market. For this purpose, six optimization targets will be formed: MinVar, MinCVaR, MaxSR, MaxSTARR, MaxUT and MaxMean. The results of the formed portfolios will be compared with the results of portfolios with the same allocation objectives, but which include a limitation on the impact of BTC as a systematic factor. The results suggest that by controlling the exposure by factor, better overall portfolio performance can be achieved through higher returns and Sharpe Ratio in four of the six implemented optimization strategies, while in terms of absolute risk measure five out of six portfolios achieved lower overall risk. Also, the obtained results confirm that the bitcoin transaction system plays a major role in defining the future movement of the value of the secondary cryptocurrency market.
    Keywords: cryptocurrencies, portfolio choice, factor investing, risk management, portfolio return
    JEL: E49 G11 P45
    Date: 2020–05–15
  4. By: Cosimo Munari
    Abstract: We establish a variety of numerical representations of preference relations induced by set-valued risk measures. Because of the general incompleteness of such preferences, we have to deal with multi-utility representations. We look for representations that are both parsimonious (the family of representing functionals is indexed by a tractable set of parameters) and well behaved (the representing functionals satisfy nice regularity properties with respect to the structure of the underlying space of alternatives). The key to our results is a general dual representation of set-valued risk measures that unifies the existing dual representations in the literature and highlights their link with duality results for scalar risk measures.
    Date: 2020–09
  5. By: Tambakis, D.
    Abstract: This study nests historical evidence for credit growth-fuelled financial instability in a 2-state non-homogeneous Markov chain with logistic crisis incidence. A long-run frequency measure is defined and calibrated for 17 advanced economies from 1870-2016. It is found that historical (implied) crisis frequencies display a V (J )-pattern over time. A key implication is that policies strengthening capital adequacy contribute more to systemic stability than expanding deposit insurance or curbing credit booms.
    Keywords: Credit cycle, Systemic banking crises, Markov chain
    JEL: C15 E30 E58 G01
    Date: 2020–09–03
  6. By: A. Fronzetti Colladon; S. Grassi; F. Ravazzolo; F. Violante
    Abstract: This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic related keywords appearing in the text. The index assesses the importance of the economic related keywords, based on their frequency of use and semantic network position. We apply it to the Italian press and construct indices to predict Italian stock and bond market returns and volatilities in a recent sample period, including the COVID-19 crisis. The evidence shows that the index captures well the different phases of financial time series. Moreover, results indicate strong evidence of predictability for bond market data, both returns and volatilities, short and long maturities, and stock market volatility.
    Date: 2020–09
  7. By: Thomas Krabichler; Josef Teichmann
    Abstract: To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimisation of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset Liability Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylised case.
    Date: 2020–09
  8. By: Ibrahima Bah (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Abdou Salam Diallo (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)
    Abstract: The credit risk of oil-exporting countries could depend on the evolution of oil market. Indeed, the instability of oil prices can cause defaults on debt repayments, with a consequent deterioration in the credit quality of exporting countries. In this paper, through an econometric analysis between oil price and other variables of oil market and CDS premium volatilities, we highlight causalities between some variable of the oil market and the variation of the credit default of some oil-exporting countries. For illustration, we have randomly chosen to treat these oil-exporting countries: Saudi Arabia, Venezuela, Russia, Norway, Kazakhstan and Qatar. A particular focus of our analysis is to study the slump of oil market in mid-2014 on the six countries credit default spreads volatility.
    Date: 2021–01
  9. By: Ting He
    Abstract: Asian option, as one of the path-dependent exotic options, is widely traded in the energy market, either for speculation or hedging. However, it is hard to price, especially the one with the arithmetic average price. The traditional trading procedure is either too restrictive by assuming the distribution of the underlying asset or less rigorous by using the approximation. It is attractive to infer the Asian option price with few assumptions of the underlying asset distribution and adopt to the historical data with a nonparametric method. In this paper, we present a novel approach to price the Asian option from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is applied to infer the average value of the future underlying asset price, which attempts to make the prediction reflecting more uncertainty because of the limited information. A rational pairwise trading criterion is also proposed in this paper for the Asian options comparison, as a risk measure. The NPI method for the Asian option is illustrated in several examples by using the simulation techniques or the empirical data from the energy market.
    Date: 2020–08
  10. By: Alla Petukhina; Simon Trimborn; Wolfgang Karl H\"ardle; Hermann Elendner
    Abstract: Cryptocurrencies (CCs) have risen rapidly in market capitalization over the last years. Despite striking price volatility, their high average returns have drawn attention to CCs as alternative investment assets for portfolio and risk management. We investigate the utility gains for different types of investors when they consider cryptocurrencies as an addition to their portfolio of traditional assets. We consider risk-averse, return-seeking as well as diversificationpreferring investors who trade along different allocation frequencies, namely daily, weekly or monthly. Out-of-sample performance and diversification benefits are studied for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. To account for low liquidity in CC markets, we incorporate liquidity constraints via the LIBRO method. Our results show that CCs can improve the risk-return profile of portfolios. In particular, a maximum-diversification strategy (maximizing the Portfolio Diversification Index, PDI) draws appreciably on CCs, and spanning tests clearly indicate that CC returns are non-redundant additions to the investment universe. Though our analysis also shows that illiquidity of CCs potentially reverses the results.
    Date: 2020–09
  11. By: Masahiko Egami; Rusudan Kevkhishvili
    Abstract: We propose a new approach to estimating the loss-given-default distribution. More precisely, we obtain the default-time distribution of the leverage ratio (defined as the ratio of a firm's assets over its debt) by examining its last passage time to a certain level. In fact, the use of the last passage time is particularly relevant because it is not a stopping time: this corresponds to the fact that the timing and extent of severe firm-value deterioration, when default approaching, is neither observed nor easily estimated. We calibrate the model parameters to the credit market, so that we can illustrate the loss-given-default distribution implied in the quoted CDS spreads.
    Date: 2020–09
  12. By: Weixuan Xia
    Abstract: In this paper we propose an efficient pricing-hedging framework for volatility derivatives which simultaneously takes into account path roughness and jumps. Instead of dealing with log-volatility, we directly model the instantaneous variance of a risky asset in terms of a fractional Ornstein-Uhlenbeck process driven by an infinite-activity L\'{e}vy subordinator, which is shown to exhibit roughness under suitable conditions and also eludes the need for an independent Brownian component. This structure renders the characteristic function of forward variance obtainable at least in semi-closed form, subject to a generic integrable kernel. To analyze financial derivatives, primarily swaps and European-style options, on average forward volatility, we introduce a general class of power-type derivatives on the average forward variance, which also provide a way of adjusting the option investor's risk exposure. Pricing formulae are based on numerical inverse Fourier transform and, as illustrated by an empirical study on VIX options, permit stable and efficient model calibration once specified.
    Date: 2020–08
  13. By: Stefan Gerhold
    Abstract: We study large and moderate deviations for a life insurance portfolio, without assuming identically distributed losses. The crucial assumption is that losses are bounded, and that variances are bounded below. From a standard large deviations upper bound, we get an exponential bound for the probability of the average loss exceeding a threshold. A counterexample shows that a full large deviation principle does not follow from our assumptions.
    Date: 2020–09
  14. By: Walter Distaso; Rustam Ibragimov; Alexander Semenov; Anton Skrobotov
    Abstract: Reliable analysis and forecasting of the spread of COVID-19 pandemic and its impacts on global finance and World's economies requires application of econometrically justified and robust methods. At the same time, statistical and econometric analysis of financial and economic markets and of the spread of COVID-19 is complicated by the inherent potential non-stationarity, dependence, heterogeneity and heavy-tailedness in the data. This project focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on the World's financial markets in different countries across the World. Among other results, the study focuses on robust inference in predictive regressions for different countries across the World. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on application of heteroskedasticity and autocorrelation consistent (HAC) inference methods, related approaches using consistent standard errors, recently developed robust $t$-statistic inference procedures and robust tail index estimation approaches.
    Date: 2020–09
  15. By: Dietz, Simon; Niehörster, Falk
    Abstract: Ambiguity about the probability of loss is a salient feature of catastrophe risk insurance. Evidence shows that insurers charge higher premiums under ambiguity, but that they rely on simple heuristics to do so, rather than being able to turn to pricing tools that formally link ambiguity with the insurer’s underlying economic objective. In this paper, we apply an α-maxmin model of insurance pricing to two catastrophe model data sets relating to hurricane risk. The pricing model considers an insurer who maximises expected profit, but is sensitive to how ambiguity affects its risk of ruin. We estimate ambiguity loads and show how these depend on the insurer’s attitude to ambiguity, α. We also compare these results with those derived from applying model blending techniques that have recently gained popularity in the actuarial profession, and show that model blending can imply relatively low aversion to ambiguity, possibly ambiguity seeking.
    Keywords: ambiguity; catastrophe modelling; insurance; model blending; natural disasters; ES/R009708/1
    JEL: D81 G22 Q54
    Date: 2020–08–27
  16. By: Delia Coculescu; Freddy Delbaen
    Abstract: We use the theory of cooperative games for the design of fair insurance contracts. An insurance contract needs to specify the premium to be paid and a possible participation in the benefit (or surplus) of the company. It results from the analysis that when a contract is exposed to the default risk of the insurance company, ex-ante equilibrium considerations require a certain participation in the benefit of the company to be specified in the contracts. The fair benefit participation of agents appears as an outcome of a game involving the residual risks induced by the default possibility and using fuzzy coalitions.
    Date: 2020–09
  17. By: Fontes de Meira, Luciana; Bello, Omar
    Abstract: The application of technologies, research, development, promotion of innovative approaches and local knowledge to confront complex issues posed by hazards are important components of managing disaster risks and guiding informed decision-making. Hence commitments to support and enhance access to technologies and to foster innovative approaches to risk reduction, preparedness and resilient recovery are essential requirements for the management of current and future disasters in the Caribbean subregion. Considering the importance of Disaster and Risk Management (DRM), the aim of this study is to assess and discuss the application of technologies and innovative approaches related to DRM in the subregion. The study will consider the five pillars of DRM: risk identification, risk reduction, preparedness, financial protection and resilient recovery. It will examine the types of available and applied technologies, discuss selected innovative approaches, evaluate and recommend strategies to advance the use, accessibility and uptake of these in all five pillars of DRM in the Caribbean subregion. The research is expected to contribute to the ongoing global discussion on the use of technology and innovation for DRM, with special attention to the sustainable development challenges of the Caribbean Small Islands Developing States (SIDS).
    Date: 2020–09–08
  18. By: Yuhyeon Bak (Department of Economics, Korea University, 145 Anamro, Seongbuk-gu, Seoul, Korea 02841); Cheolbeom Park (Department of Economics, Korea University, 145 Anamro, Seongbukgu, Seoul, Korea 02841)
    Abstract: Uncovered interest rate parity is known to perform poorly in forecasting exchange rate movements, especially in the short run. One possible reason for this failure is the existence of unobservable risk premium. We estimate the unobservable risk premium with a predictive system using the implied volatility of at-the-money currency options as an imperfect predictor. We find that expected exchange rate changes, constructed from forward-spot differentials and estimated risk premiums, track actual exchange rate changes more closely than do the fitted values of the Fama regression. When we add the estimated risk premium from the predictive system in the Fama regression, the UIP puzzle becomes weakened. An out-of-sample analysis reveals that adding the estimated risk premium greatly improves the short-run predictability of exchange rates.
    Keywords: exchange rate, Bayesian approach, predictive system, risk premium
    JEL: F31 F47
    Date: 2020
  19. By: David Alary (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique); Catherine Bobtcheff (PSE - Paris School of Economics, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Carole Haritchabalet (CATT - Centre d'Analyse Théorique et de Traitement des données économiques - UPPA - Université de Pau et des Pays de l'Adour, UPPA - Université de Pau et des Pays de l'Adour)
    Abstract: This paper explores how insurance companies can coordinate to extend their joint capacity for the coverage of new and undiversifiable risks. The undiversifiable nature of such risks causes a shortage of insurance capacity and their limited knowledge makes learning and information sharing necessary. We develop a unified theoretical model to analyse co-insurance agreements. We show that organizing this insurance supply amounts to sharing a common value divisible good between capacity constrained and privately informed insurers with a reserve price. Coinsurance via the creation of an insurance pool turns out to operate as a uniform price auction with an "exit/re-entry" option. We compare it to a discriminatory auction for which no specific agreements are needed. Both auction formats lead to different coverage/premium tradeoffs. If at least one insurer provides an optimistic expertise about the risk, the pool offers higher coverage. This result is reversed when all insurers are pessimistic about the risk. Static comparative results with respect to the severity of the capacity constraints and the reserve price are provided. In the case of completely new risks, a regulator aiming at maximizing the expected coverage should promote the pool when the reserve price is low enough or when competition is high enough.
    Keywords: reserve price,competition,common value divisible good auctions,undiversifiable and new risks,Coinsurance
    Date: 2020–09
  20. By: Eric Benhamou; David Saltiel; Jean-Jacques Ohana; Jamal Atif
    Abstract: Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviations as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markowitz and is able to detect and anticipate crisis like the current Covid one.
    Date: 2020–09
  21. By: Matthew B. Canzoneri; Behzad T. Diba; Luca Guerrieri; Arsenii Mishin
    Abstract: We build a quantitatively relevant macroeconomic model with endogenous risk-taking. In our model, deposit insurance and limited liability can lead banks to make risky loans that are socially inefficient. This excessive risk-taking can be triggered by aggregate or sectoral shocks that reduce the return on safer loans. Excessive risk-taking can be avoided by raising bank capital requirements, but unnecessarily tight requirements lower welfare by limiting liquidity producing bank deposits. Consequently, optimal capital requirements are dynamic (or state contingent). We provide examples in which a Ramsey planner would raise capital requirements: (1) during a downturn caused by a TFP shock; (2) during an expansion caused by an investment-specific shock; and (3) during an increase in market volatility that has little effect on the business cycle. In practice, the economy is driven by a constellation of shocks, and the Ramsey policy is probably beyond the policymaker's ken; so, we also consider implementable policy rules. Some rules can mimic the optimal policy rather well but are not robust to all the calibrations we consider. Basel III guidance calls for increasing capital requirements when the credit to GDP ratio rises, and relaxing them when it falls; this rule does not perform well. In fact, slightly elevated static capital requirements generally do about as well as any implementable rule.
    Keywords: Countercylical capital buffer; DSGE models; Bank capital requirements; Ramsey policy
    JEL: C51 E58 G28
    Date: 2020–08–06
  22. By: CHAN Joshua (Purdue University); DOUCET Arnaud (University of Oxford); Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan.); STRACHAN Rodney W. (University of Queensland)
    Abstract: This paper develops a new methodology that decomposes shocks into homoscedastic and heteroscedastic components. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity; a property known as co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which we show is important for volatility estimation. By incorporating testable co-heteroscedasticity restrictions, the specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle filter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternatingorder particle Gibbs that reduces the amount of particles needed for accurate estimation. We provide an empirical application to a large Vector Autoregression (VAR), in which we find strong evidence for co-heteroscedasticity and that the new method compares favorably to previous ones in terms of forecasting from horizon 3 onward. A Monte Carlo experiment illustrates that the new method estimates well the characteristics of approximate factor models with heteroscedastic errors.
    Keywords: Markov Chain Monte Carlo, Gibbs Sampling, Flexible Parametric Model, Particle Filter, Co-heteroscedasticity, state-space, reparameterization, alternating-order
    Date: 2020–09
  23. By: Yi-Hsuan Lin
    Abstract: In random expected utility (Gul and Pesendorfer, 2006), the distribution of preferences is uniquely recoverable from random choice. This paper shows through two examples that such uniqueness fails in general if risk preferences are random but do not conform to expected utility theory. In the first, non-uniqueness obtains even if all preferences are confined to the betweenness class (Dekel, 1986) and are suitably monotone. The second example illustrates random choice behavior consistent with random expected utility that is also consistent with random non-expected utility. On the other hand, we find that if risk preferences conform to weighted utility theory (Chew, 1983) and are monotone in first-order stochastic dominance, random choice again uniquely identifies the distribution of preferences. Finally, we argue that, depending on the domain of risk preferences, uniqueness may be restored if joint distributions of choice across a limited number of feasible sets are available.
    Date: 2020–09

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