nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒10‒04
fifteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Tail Risks and Stock Return Predictability: Evidence From Asia-Pacific By Ogbonna, Ahamuefula; Olubusoye, Olusanya E
  2. Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility By Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
  3. Exploring Critical Risk Factors of Office Building Projects By Nguyen, Phong Thanh; Phu Pham, Cuong; Thanh Phan, Phuong; Bich Vu, Ngoc; Tien Ha Duong, My; Le Hoang Thuy To Nguyen, Quyen
  4. Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets By G. Mazzei; F. G. Bellora; J. A. Serur
  5. The benefits of the Legal Entity Identifier for monitoring systemic risk JEL Classification: C81, E44, G28 By Calleja, Romain; Katsigianni, Eleni; Laurent, François; Kaminska, Beata; Aparicio, Carlos; Dworak, Bartosz; Garcia, Luis; Durant, Dominique; Ristori, Lucia; Kirchner, Robert; Vitellas, Dimitrios; Pistelli, Federico
  6. Exploring the sources of loan default clustering using survival analysis with frailty By Enrique Bátiz-Zuk Enrique; Abdulkadir Mohamed; Fátima Sánchez-Cajal
  7. Systemic risk in interbank networks: disentangling balance sheets and network effects By Alessandro Ferracci; Giulio Cimini
  8. Quantifying time-varying forecast uncertainty and risk for the real price of oil By Knut Are Aastveit; Jamie L. Cross; Herman K. van Dijk
  9. Efficient credit portfolios under IFRS 9 By Rui Pedro Gonçalves Brito; Pedro Maria Corte Real Alarcão Judice
  10. Pricing and Hedging Prepayment Risk in a Mortgage Portfolio By Emanuele Casamassima; Lech A. Grzelak; Frank A. Mulder; Cornelis W. Oosterlee
  11. From SMP to PEPP: a further look at the risk endogeneity of the Central Bank By Marco Fruzzetti; Giulio Gariano; Gerardo Palazzo; Antonio Scalia
  12. Self-Organized Critical Markets: Implied Volatility and Avalanche Intensity. By Romain Bocher
  13. Bitcoin Volatility and Intrinsic Time Using Double Subordinated Levy Processes By Abootaleb Shirvani; Stefan Mittnik; W. Brent Lindquist; Svetlozar T. Rachev
  14. Stock Index Prediction using Cointegration test and Quantile Loss By Jaeyoung Cheong; Heejoon Lee; Minjung Kang
  15. Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach By Ali Al-Ameer; Khaled Alshehri

  1. By: Ogbonna, Ahamuefula; Olubusoye, Olusanya E
    Abstract: Hinging on the recently established relevance of tail thickness information, we examine the predictability of fifteen major stocks in the Asia-Pacific region using conditional autoregressive value at risk (CAViaR) model estimates of tail risks. We used a Westerlund and Narayan–type distributed lag model to examine the nexus between returns and tail risk under controlled global and US stocks spillover effects. Country-specific tail risks induce a near-term rise (completely disappears) in returns on “bad” (“good”) days. Our results are robust.
    Keywords: Conditional Autoregressive Value at Risk; Predictability; Returns; Tail Thickness
    JEL: C10 C53 G17
    Date: 2021–04–09
  2. By: Eduardo Ramos-P\'erez; Pablo J. Alonso-Gonz\'alez; Jos\'e Javier N\'u\~nez-Vel\'azquez
    Abstract: Events such as the Financial Crisis of 2007-2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.
    Date: 2021–09
  3. By: Nguyen, Phong Thanh; Phu Pham, Cuong; Thanh Phan, Phuong; Bich Vu, Ngoc; Tien Ha Duong, My; Le Hoang Thuy To Nguyen, Quyen
    Abstract: Risks and uncertainty are unavoidable problems in management of projects. Therefore, project managers should not only prevent risks, but also have to respond and manage them. Risk management has become a critical interest subject in the construction industry for both practitioners and researchers. This paper presents critical risk factors of office building projects in the construction phase in Ho Chi Minh City, Vietnam. Data was collected through a questionnaire survey based on the likelihood and consequence level of risk factors. These factors fell into five groups: (i) financial risk factors; (ii) management risk factors; (iii) schedule risk factors; (iv) construction risk factors; and (v) environment risk factors. The research results showed that critical factors affecting office building projects are natural (i.e., prolonged rain, storms, climate effects) and human-made issues (i.e., soil instability, safety behaviors, owner’s design change) and the schedule-related risk factors contributed to the most significant risks for office buildings projects in the construction phase in Ho Chi Minh City. They give construction management and project management practitioners a new perspective on risks and risk management of office buildings projects in Ho Chi Minh City and are proactive in the awareness, response, and management of risk factors comprehensively.
    Keywords: Construction Management, Office Buildings Projects, Risk Management, Project Management
    JEL: G32 L74 O18
    Date: 2020–08–04
  4. By: G. Mazzei; F. G. Bellora; J. A. Serur
    Abstract: In most real scenarios the construction of a risk-neutral portfolio must be performed in discrete time and with transaction costs. Two human imposed constraints are the risk-aversion and the profit maximization, which together define a nonlinear optimization problem with a model-dependent solution. In this context, an optimal fixed frequency hedging strategy can be determined a posteriori by maximizing a sharpe ratio simil path dependent reward function. Sampling from Heston processes, a convolutional neural network was trained to infer which period is optimal using partial information, thus leading to a dynamic hedging strategy in which the portfolio is hedged at various frequencies, each weighted by the probability estimate of that frequency being optimal.
    Date: 2021–09
  5. By: Calleja, Romain; Katsigianni, Eleni; Laurent, François; Kaminska, Beata; Aparicio, Carlos; Dworak, Bartosz; Garcia, Luis; Durant, Dominique; Ristori, Lucia; Kirchner, Robert; Vitellas, Dimitrios; Pistelli, Federico
    Keywords: Legal entity identifier, Master data, Record Linkage, Systemic risk, Trade register number
    Date: 2021–09
  6. By: Enrique Bátiz-Zuk Enrique; Abdulkadir Mohamed; Fátima Sánchez-Cajal
    Abstract: This paper investigates whether three microeconomic loan characteristics are sources of loan default clustering in the Mexican banking sector by employing survival analysis with frailty. Using a large sample of bank loan level data granted to micro, small and medium sized firms from January 2010 to 2018, we test whether classifying loans by the bank's systemic importance, industry or at individual firm level enhances the predictions of loans defaults. Our results show that loans granted by Domestic Systemically Important Banks contribute to the default clustering in micro and small firm loans. This is due to aggregate default rate levels and clusters that are large for these firms loans compared with loans provided to medium-sized firms. These findings have important implications for bank's expected loss management related to the correlated loan default risk.
    JEL: C53 C41 C25 G38
    Date: 2021–09
  7. By: Alessandro Ferracci; Giulio Cimini
    Abstract: We study the difference between the level of systemic risk that is empirically measured on an interbank network and the risk that can be deduced from the balance sheets composition of the participating banks. Using generalised DebtRank dynamics, we measure observed systemic risk on e-MID network data (augmented by BankFocus information) and compare it with the expected systemic of a null model network, obtained through an appropriate maximum-entropy approach constraining relevant balance sheet variables. We show that the aggregate levels of observed and expected systemic risks are usually compatible but differ significantly during turbulent times (in our case, after the default of Lehman Brothers and the VLTRO implementation by the ECB). At the individual level instead, banks are typically more or less risky than what their balance sheet prescribes due to their position in the network. Our results confirm on one hand that balance sheet information used within a proper maximum-entropy network models provides good systemic risk estimates, and on the other hand the importance of knowing the empirical details of the network for conducting precise stress tests of individual banks, especially after systemic events.
    Date: 2021–09
  8. By: Knut Are Aastveit; Jamie L. Cross; Herman K. van Dijk
    Abstract: We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.
    Keywords: oil price, forecast density combination, bayesian forecasting, instabilities, model uncertainty
    JEL: C11 C32 C53 Q43 Q47
    Date: 2021–06–01
  9. By: Rui Pedro Gonçalves Brito (University of Coimbra, Centre for Business and Economics Research, CeBER and Faculty of Economic); Pedro Maria Corte Real Alarcão Judice (ISCTE Business Research Unit)
    Abstract: In this paper, we devise a forward-looking methodology to determine efficient credit portfolios under the IFRS 9 framework. We define and implement a credit loss model based on prospective point-in-time probabilities of default. We determine these probabilities of default and the credits’ stage allocation through a credit stochastic simulation. This simulation is based on the estimation of transition matrices. Using data from 1981 to 2019, in a non-homogeneous Markov chain setting, we estimate transition matrices conditional on the global real gross domestic product growth. This allows considering the effects of the economic cycle, which are of great importance in bank management. Finally, we develop a robust optimization model that allows the bank manager to analyze the tradeoff between the annual average portfolio income and the corresponding portfolio volatility. According to the proposed bi-objective model, we compute the efficient credit portfolios constructed based on 10-year maturity credits. We compare their structure to those generated by the IAS 39 and CECL accounting frameworks. The results indicate that the IFRS 9 and CECL frameworks generate efficient credit portfolios whose structure penalizes riskier-rated credits. In turn, the riskier efficient credit portfolios under the IAS 39 framework concentrate entirely on speculative-grade credits. This pattern is also encountered in efficient credit portfolios constructed based on credits with different maturities, namely 5 and 15 years. Moreover, the longer the maturity of the credits that enter into the composition of the efficient portfolios, the more the speculative-grade credits tend to be penalized.
    Keywords: IFRS 9, IAS 39, CECL, credit risk, transition matrices,stochastic simulation.
    JEL: C44 C50 C61 C63 G11 G17 G24
    Date: 2021–07
  10. By: Emanuele Casamassima; Lech A. Grzelak; Frank A. Mulder; Cornelis W. Oosterlee
    Abstract: Understanding mortgage prepayment is crucial for any financial institution providing mortgages, and it is important for hedging the risk resulting from such unexpected cash flows. Here, in the setting of a Dutch mortgage provider, we propose to include non-linear financial instruments in the hedge portfolio when dealing with mortgages with the option to prepay part of the notional early. Based on the assumption that there is a correlation between prepayment and the interest rates in the market, a model is proposed which is based on a specific refinancing incentive. The linear and non-linear risks are addressed by a set of tradeable instruments in a static hedge strategy. We will show that a stochastic model for the notional of a mortgage unveils non-linear risk embedded in a prepayment option. Based on a calibration of the refinancing incentive on a data set of more than thirty million observations, a functional form of the prepayments is defined, which accurately reflects the borrowers' behaviour. We compare this functional form with a fully rational model, where the option to prepay is assumed to be exercised rationally.
    Date: 2021–09
  11. By: Marco Fruzzetti (Bank of Italy); Giulio Gariano (Bank of Italy); Gerardo Palazzo (Bank of Italy); Antonio Scalia (Bank of Italy)
    Abstract: This paper examines the evolution of credit risk arising from monetary policy operations and ELA on the Eurosystem balance sheet over the last decade. We employ a dynamic, market-driven risk model relying on the expected default frequencies for sovereigns, banks and corporates provided by Moody’s Analytics. Dependence between defaults is modeled with a multivariate Student t distribution with time-varying parameters. We find that at the end of 2020, risk is slightly above its average value in 2010 and approximately equal to one quarter of the value measured at the peak of the sovereign debt crisis in 2012, notwithstanding the threefold increase in the Eurosystem monetary policy exposure occurred since then. This is due to the launch of the OMT and PEPP, which succeeded in quelling market turmoil, thereby reducing the Eurosystem’s own balance sheet credit risk. The OMT in particular has had a long lasting effect in lowering sovereign risk in the euro area. Our findings support the view that, in periods of severe financial distress, risk for a central bank is largely endogenous.
    Keywords: financial risk measurement, unconventional monetary policy, ELA, sovereign risk, Eurosystem financial risk
    JEL: E58 E52 C15
    Date: 2021–10
  12. By: Romain Bocher (Département d'addictologie et de psychiatrie de liaison [CHU de Nantes] - CHU Nantes - Centre hospitalier universitaire de Nantes)
    Abstract: Assuming self-organized criticality to characterize capital markets, this paper seeks to explain why equity implied volatility is a relevant proxy for avalanche intensity. Historical data analysis of the CBOE Volatility Index (VIX) shows that implied volatility spikes are distributed following a power law, making financial stress similar to earthquakes as anticipated long ago by Bak.
    Keywords: Implied Volatility,Power Law,Self-Organized Criticality
    Date: 2020
  13. By: Abootaleb Shirvani; Stefan Mittnik; W. Brent Lindquist; Svetlozar T. Rachev
    Abstract: We propose a doubly subordinated Levy process, NDIG, to model the time series properties of the cryptocurrency bitcoin. NDIG captures the skew and fat-tailed properties of bitcoin prices and gives rise to an arbitrage free, option pricing model. In this framework we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Cboe VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both are compared to a volatility based upon historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures observed, in-sample, volatility.
    Date: 2021–09
  14. By: Jaeyoung Cheong; Heejoon Lee; Minjung Kang
    Abstract: Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches.
    Date: 2021–09
  15. By: Ali Al-Ameer; Khaled Alshehri
    Abstract: We propose a convex formulation for a trading system with the Conditional Value-at-Risk as a risk-adjusted performance measure under the notion of Direct Reinforcement Learning. Due to convexity, the proposed approach can uncover a lucrative trading policy in a "pure" online manner where it can interactively learn and update the policy without multi-epoch training and validation. We assess our proposed algorithm on a real financial market where it trades one of the largest US trust funds, SPDR, for three years. Numerical experiments demonstrate the algorithm's robustness in detecting central market-regime switching. Moreover, the results show the algorithm's effectiveness in extracting profitable policy while meeting an investor's risk preference under a conservative frictional market with a transaction cost of 0.15% per trade.
    Date: 2021–09

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