nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒01‒25
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation By Saissi Hassani, Samir; Dionne, Georges
  2. Optimal Hedging with Margin Constraints and Default Aversion and its Application to Bitcoin Perpetual Futures By Carol Alexander; Jun Deng; Bin Zou
  3. Insurance valuation: A two-step generalised regression approach By Karim Barigou; Valeria Bignozzi; Andreas Tsanakas
  4. Deep Portfolio Optimization via Distributional Prediction of Residual Factors By Kentaro Imajo; Kentaro Minami; Katsuya Ito; Kei Nakagawa
  5. Climate Finance By Stefano Giglio; Bryan Kelly; Johannes Stroebel
  6. Forbearance Patterns in the Post-Crisis Period By Katharina Bergant
  7. The Thermodynamic Approach to Whole-Life Insurance: A Method for Evaluation of Surrender Risk By Jir\^o Akahori; Yuuki Ida; Maho Nishida; Shuji Tamada
  8. Monetary Risk Measures By Guangyan Jia; Jianming Xia; Rongjie Zhao
  9. Canada; Financial System Stability Assessment By International Monetary Fund
  10. Tensoring volatility calibration Calibration of the rough Bergomi volatility model via Chebyshev Tensors By Mariano Zeron; Ignacio Ruiz
  12. Dynamic Reinsurance in Discrete Time Minimizing the Insurer's Cost of Capital By Alexander Glauner
  13. Time-Varying Mixture Copula Models with Copula Selection By Bingduo Yang; Zongwu Cai; Christian M. Hafner; Guannan Liu
  14. Quantification and analysis of risk exposure in the maritime industry By Knapp, S.
  15. Expected Credit Loss Modeling from a Top-Down Stress Testing Perspective By Marco Gross; Dimitrios Laliotis; Mindaugas Leika; Pavel Lukyantsau
  16. Risk & returns around FOMC press conferences: a novel perspective from computer vision By Alexis Marchal
  17. Theory and Applications of Financial Chaos Index By Masoud Ataei; Shengyuan Chen; Zijiang Yang; M. Reza Peyghami
  18. Strategic Asset Allocation of a Reserves' Portfolio: Hedging against Shocks By Pablo Orazi; Mario Torriani; Matias Vicens
  19. Does Going Tough on Banks Make the Going Get Tough? Bank Liquidity Regulations, Capital Requirements, and Sectoral Activity By Deniz O Igan; Ali Mirzaei
  20. Managing Macrofinancial Risk By Tobias Adrian; Francis Vitek
  21. Forward indifference valuation and hedging of basis risk under partial information By Mahan Tahvildari
  22. Bankruptcy prediction using disclosure text features By Sridhar Ravula
  23. The Causal Learning of Retail Delinquency By Yiyan Huang; Cheuk Hang Leung; Xing Yan; Qi Wu; Nanbo Peng; Dongdong Wang; Zhixiang Huang
  24. To VaR, or Not to VaR, That is the Question By Victor Olkhov
  25. Does Bitcoin Improve Investment Portfolio Efficiency? By Paweł Sakowski; Daria Turovtseva
  26. The Bahamas; Financial Sector Assessment Program-Technical Note on Financial Stability and Stress Testing By International Monetary Fund
  27. A Multivariate GARCH-Jump Mixture Model By Li, Chenxing; Maheu, John M

  1. By: Saissi Hassani, Samir (HEC Montreal, Canada Research Chair in Risk Management); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We model the new quantitative aspects of market risk management for banks that Basel established in 2016 and came into effect in January 2019. Market risk is measured by Conditional Value at Risk (CVaR) or Expected Shortfall at a confidence level of 97.5%. The regulatory backtest remains largely based on 99% VaR. As additional statistical procedures, in line with the Basel recommendations, supplementary VaR and CVaR backtests must be performed at different confidence levels. We apply these tests to various parametric distributions and use non-parametric measures of CVaR, including CVaR- and CVaR+ to supplement the modelling validation. Our data relate to a period of extreme market turbulence. After testing eight parametric distributions with these data, we find that the information obtained on their empirical performance is closely tied to the backtesting conclusions regarding the competing models.
    Keywords: Basel III; VaR; CVaR; Expected Shortfall; backtesting; parametric model; non-parametric model; mixture of distributions; fat-tail distribution
    JEL: C44 C46 C52 G21 G24 G28 G32
    Date: 2021–01–12
  2. By: Carol Alexander; Jun Deng; Bin Zou
    Abstract: We consider a futures hedging problem subject to a budget constraint that limits the ability of a hedger with default aversion to meet margin requirements. We derive a semi-closed form for an optimal hedging strategy with dual objectives -- to minimize both the variance of the hedged portfolio and the probability of forced liquidations due to margin calls. An empirical analysis of bitcoin shows that the optimal strategy not only achieves superior hedge effectiveness, but also reduces the probability of forced liquidations to an acceptable level. We also compare how the hedger's default aversion impacts the performance of optimal hedging based on minute-level data across major bitcoin spot and perpetual futures markets.
    Date: 2021–01
  3. By: Karim Barigou (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Valeria Bignozzi (Department of Statistics and Quantitative Methods University of Milano-Bicocca); Andreas Tsanakas (The Business School (formerly Cass), City, University of London)
    Abstract: Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.
    Keywords: Market-consistent valuation,Quantile regression,Solvency II,Cost-of-capital,Dynamic risk measurement
    Date: 2020–12–07
  4. By: Kentaro Imajo; Kentaro Minami; Katsuya Ito; Kei Nakagawa
    Abstract: Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a highly non-stationary nature hindering the application of typical data-hungry machine learning methods, leveraging financial inductive biases is important to ensure better sample efficiency and robustness. In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors. The key technical ingredients are twofold. First, we introduce a computationally efficient extraction method for the residual information, which can be easily combined with various prediction algorithms. Second, we propose a novel neural network architecture that allows us to incorporate widely acknowledged financial inductive biases such as amplitude invariance and time-scale invariance. We demonstrate the efficacy of our method on U.S. and Japanese stock market data. Through ablation experiments, we also verify that each individual technique contributes to improving the performance of trading strategies. We anticipate our techniques may have wide applications in various financial problems.
    Date: 2020–12
  5. By: Stefano Giglio; Bryan Kelly; Johannes Stroebel
    Abstract: We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance.
    Keywords: climate change, climate risk, physical risk, transition risk, ESG
    Date: 2020
  6. By: Katharina Bergant
    Abstract: Using supervisory loan-level data on corporate loans, we show that banks facing high levels of non-performing loans relative to their capital and provisions were more likely to grant forbearance measures to the riskiest group of borrowers. More specifically, we find that risky borrowers are more likely to get an increase in the overall limit or the maturity of a loan product from a distressed lender. As a second step, we analyse the effectiveness of this practice in reducing the probability of default. We show that the most common measure of forbearance is effective in the short run but no forbearance measure significantly reduces the probability of default in the long run. Our evidence also suggests that forbearance and new lending are substitutes for banks, as high shares of forbearance are negatively correlated with new lending to the same group of borrowers. Taken together, these findings can help policy makers shape surveillance and regulation in a future recovery from the Covid-19 pandemic.
    Keywords: Loans;Banking;Nonperforming loans;Capital adequacy requirements;Credit;WP,risky borrower,outstanding balance,x Time,bank characteristic,borrower rating,interest rate,Time level
    Date: 2020–07–24
  7. By: Jir\^o Akahori; Yuuki Ida; Maho Nishida; Shuji Tamada
    Abstract: We introduce a collective model for life insurance where the heterogeneity of each insured, including the health state, is modeled by a diffusion process. This model is influenced by concepts in statistical mechanics. Using the proposed framework, one can describe the total pay-off as a functional of the diffusion process, which can be used to derive a level premium that evaluates the risk of lapses due tothe so-called adverse selection. Two numerically tractable models are presented to exemplify the flexibility of the proposed framework.
    Date: 2020–12
  8. By: Guangyan Jia; Jianming Xia; Rongjie Zhao
    Abstract: In this paper, we study general monetary risk measures (without any convexity or weak convexity). A monetary (respectively, positively homogeneous) risk measure can be characterized as the lower envelope of a family of convex (respectively, coherent) risk measures. The proof does not depend on but easily leads to the classical representation theorems for convex and coherent risk measures. When the law-invariance and the SSD (second-order stochastic dominance)-consistency are involved, it is not the convexity (respectively, coherence) but the comonotonic convexity (respectively, comonotonic coherence) of risk measures that can be used for such kind of lower envelope characterizations in a unified form. The representation of a law-invariant risk measure in terms of VaR is provided.
    Date: 2020–12
  9. By: International Monetary Fund
    Abstract: This Financial System Stability Assessment paper discusses that Canada has enjoyed favorable macroeconomic outcomes over the past decades, and its vibrant financial system continues to grow robustly. However, macrofinancial vulnerabilities—notably, elevated household debt and housing market imbalances—remain substantial, posing financial stability concerns. Various parts of the financial system are directly exposed to the housing market and/or linked through housing finance. The financial system would be able to manage severe macrofinancial shocks. Major deposit-taking institutions would remain resilient, but mortgage insurers would need additional capital in a severe adverse scenario. Housing finance is broadly resilient, notwithstanding some weaknesses in the small non-prime mortgage lending segment. Although banks’ overall capital buffers are adequate, additional required capital for mortgage exposures, along with measures to increase risk-based differentiation in mortgage pricing, would be desirable. This would help ensure adequate through-the cycle buffers, improve mortgage risk-pricing, and limit procyclical effects induced by housing market corrections.
    Keywords: Mortgages;Housing prices;Insurance;Insurance companies;Banking;ISCR,CR,housing market,financial system,credit risk,government bond,capital ratio
    Date: 2019–06–24
  10. By: Mariano Zeron; Ignacio Ruiz
    Abstract: Inspired by a series of remarkable papers in recent years that use Deep Neural Nets to substantially speed up the calibration of pricing models, we investigate the use of Chebyshev Tensors instead of Deep Neural Nets. Given that Chebyshev Tensors can be, under certain circumstances, more efficient than Deep Neural Nets at exploring the input space of the function to be approximated, due to their exponential convergence, the problem of calibration of pricing models seems, a priori, a good case where Chebyshev Tensors can be used. In this piece of research, we built Chebyshev Tensors, either directly or with the help of the Tensor Extension Algorithms, to tackle the computational bottleneck associated with the calibration of the rough Bergomi volatility model. Results are encouraging as the accuracy of model calibration via Chebyshev Tensors is similar to that when using Deep Neural Nets, but with building efforts that range between 5 and 100 times more efficient in the experiments run. Our tests indicate that when using Chebyshev Tensors, the calibration of the rough Bergomi volatility model is around 40,000 times more efficient than if calibrated via brute-force (using the pricing function).
    Date: 2020–12
  11. By: Loïc Berger (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux]); Louis Eeckhoudt (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Diversification is a basic economic principle that helps to hedge against uncertainty. It is therefore intuitive that both risk aversion and ambiguity aversion should positively affect the value of diversification. In this paper, we show that this intuition (1) is true for risk aversion but (2) is not necessarily true for ambiguity aversion. We derive sufficient conditions, showing that, contrary to the economic intuition, ambiguity and ambiguity aversion may actually reduce the diversification value.
    Keywords: Diversification,ambiguity aversion,model uncertainty,hedging
    Date: 2020
  12. By: Alexander Glauner
    Abstract: In the classical static optimal reinsurance problem, the cost of capital for the insurer's risk exposure determined by a monetary risk measure is minimized over the class of reinsurance treaties represented by increasing Lipschitz retained loss functions. In this paper, we consider a dynamic extension of this reinsurance problem in discrete time which can be viewed as a risk-sensitive Markov Decision Process. The model allows for both insurance claims and premium income to be stochastic and operates with general risk measures and premium principles. We derive the Bellman equation and show the existence of a Markovian optimal reinsurance policy. Under an infinite planning horizon, the model is shown to be contractive and the optimal reinsurance policy to be stationary. The results are illustrated with examples where the optimal policy can be determined explicitly.
    Date: 2020–12
  13. By: Bingduo Yang (Lingnan (University) College, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China); Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Christian M. Hafner (Department of Economics, Tulane University, New Orleans, LA 70118, USA); Guannan Liu (School of Economics and WISE, Xiamen University, Xiamen, Fujian 361005, China)
    Abstract: Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models for dependence often encounter a lack of fit in the joint tails, calling for additional flexibility. This paper introduces a new semiparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic and unspecified functions of time. We propose penalized time-varying mixture copula models with group smoothly clipped absolute deviation penalty functions to do the estimation and copula selection simultaneously. Monte Carlo simulation results suggest that the shrinkage estimation procedure performs well in selecting and estimating both constant and time-varying mixture copula models. Using the proposed model and method, we analyze the evolution of the dependence among four international stock markets, and find substantial changes in the levels and patterns of the dependence, in particular around crisis periods.
    Keywords: Copula Selection; EM Algorithm; Mixture Copula; SCAD; Time-Varying Distribution.
    JEL: C14 C22
    Date: 2021–01
  14. By: Knapp, S.
    Abstract: Shipping facilitates global trade and provides essential services even during global pandemics such as SARS-CoV-2 (Covid19). An improved understanding of the magnitude and change of risk exposure has become more important for all maritime stake holders. The present approach quantifies global and regional risk exposure at ship level expressed as the monetary value at risk (MVR) and measures the amount of averted or mitigated incident costs due to inspections which can maritime stakeholders better understand risk exposure and develop strategies and policies to mitigate risk with improved risk control options such as improved risk profiling. The analysis is based on the global fleet using many data sources including ship particulars, inspections, incidents, cargo values, secondhand prices of vessels, special drawing right limits, arrival data and traffic movement data of 133,799 unique IMO. Estimation scenarios are run for the years 2017 to 2020 resulting in millions of computations as risk components are estimated at the individual ship level. The analysis confirms the importance to estimate all components at ship level as safety qualities differ and each vessel benefits differently from an inspection. Estimates of MVR (TLVSS, total loss, very serious and serious incidents) are slightly higher than global insurance premiums and global MVR stands at 13.7 to 17.8 billion USD. Over half of risk exposure is due to other marine liabilities and hull and machinery with cruise vessels leading loss of life and injuries and oil tankers pollution. The top 25 flags account for 87.9% of MVR with open registries in the lead reflecting the structure of the world fleet. In terms of MVR per GRT value, traditional flags, Non-IACS flags and owners located in low to upper middle-income countries show the highest values. Total MVR decreased by 4.18% due to the effects of the pandemic but pollution risk exposure increased by 6% in 2020 compared to 2019. Averted yearly incident costs are estimated to be 25% to 40% of global MVR which highlights the importance of port state control inspection programs but as inspection coverage decreased, this translated into a reduction of 6 to 11% of averted incident costs due to inspections in 2020 due to the pandemic.
    Keywords: Risk exposure, monetary value at risk, binary logistic regression, averted incident costs, inspection, effect, port state control inspections
    Date: 2020–09–01
  15. By: Marco Gross; Dimitrios Laliotis; Mindaugas Leika; Pavel Lukyantsau
    Abstract: The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for institutions wishing to include accounting-based approaches for credit risk modeling in top-down stress tests.
    Keywords: International Financial Reporting Standards;Stress testing;Stocks;Loans;Banking;WP,transition matrix,financial asset,balance sheet
    Date: 2020–07–03
  16. By: Alexis Marchal
    Abstract: I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.
    Date: 2020–12
  17. By: Masoud Ataei; Shengyuan Chen; Zijiang Yang; M. Reza Peyghami
    Abstract: We develop a new stock market index that captures the chaos existing in the market by measuring the mutual changes of asset prices. This new index relies on a tensor-based embedding of the stock market information, which in turn frees it from the restrictive value- or capitalization-weighting assumptions that commonly underlie other various popular indexes. We show that our index is a robust estimator of the market volatility which enables us to characterize the market by performing the task of segmentation with a high degree of reliability. In addition, we analyze the dynamics and kinematics of the realized market volatility as compared to the implied volatility by introducing a time-dependent dynamical system model. Our computational results which pertain to the time period from January 1990 to December 2019 imply that there exist a bidirectional causal relation between the processes underlying the realized and implied volatility of the stock market within the given time period, where it is shown that the later has a stronger causal effect on the former as compared to the opposite. This result connotes that the implied volatility of the market plays a key role in characterization of the market's realized volatility.
    Date: 2021–01
  18. By: Pablo Orazi (Central Bank of Argentina); Mario Torriani (Central Bank of Argentina); Matias Vicens (Central Bank of Argentina)
    Abstract: Central bank reserves function as a liquidity buffer to mitigate country exposure and vulnerability to external shocks. Emerging Market Economies are the countries most exposed to the volatility of capital flows and have usually preferred to build up large war-chests of international reserves as a self-insurance mechanism, as it is under their full discretion. Nevertheless, the standard practice of immobilizing large amounts of “cash” to insure against jumps in volatility and riskaversion could be enhanced. The inclusion in the strategic asset allocation decision of external shocks´ hedging strategies, which may increase the market value of the reserves´ portfolio when reserves are more needed, can help to enhance the risk management of the national balance sheet. This paper presents a framework that seeks to enhance the strategic asset allocation of a central bank, by including in the portfolio construction the analysis of correlations between the reserves’ portfolio and the country’s main vulnerabilities to external shocks.
    Keywords: asset allocation, Central Bank, external shocks, hedging strategies, international reserves
    JEL: E58 F32 G11
    Date: 2020–07
  19. By: Deniz O Igan; Ali Mirzaei
    Abstract: Whether and to what extent tougher bank regulation weighs on economic growth is an open empirical question. Using data from 28 manufacturing industries in 50 countries, we explore the extent to which cross-country differences in bank liquidity and capital levels were related to differences in sectoral activity around the period of the global financial crisis. We find that industries which are more dependent on external finance, in countries where banks had higher liquidity and capital ratios, performed relatively better during the crisis, with regard to investment rates and the creation of new enterprises. This relationship, however, exists only for bank-based systems and emerging market economies. In the pre-crisis period, we find only a marginal link to bank capital. These findings survive a battery of robustness checks and provide some solid support for the tighter prudential measures introduced under Basel III.
    Keywords: Banking;Liquidity requirements;Liquidity;Capital adequacy requirements;Financial crises;WP,capital level,economic activity,capital requirement,capital position
    Date: 2020–06–19
  20. By: Tobias Adrian; Francis Vitek
    Abstract: We augment a linearized dynamic stochastic general equilibrium (DSGE) model with a tractable endogenous risk mechanism, to support the joint analysis of monetary and macroprudential policy. This state dependent conditional heteroskedasticity mechanism specifies the conditional variances of structural shocks as functions of the business or financial cycle. The resultant heteroskedastic linearized DSGE model preserves the satisfactory simulation and forecasting performance of its nested homoskedastic counterpart for the conditional means of endogenous variables, while substantially improving its goodness of fit to their conditional distributions. In particular, the model matches the key stylized facts of growth at risk. Accounting for state dependent conditional heteroskedasticity makes it optimal for monetary policy to respond more aggressively to the business cycle, and for macroprudential policy to manage the resilience of the banking sector more actively over the financial cycle.
    Keywords: Mortgages;Production growth;Macroprudential policy;Short term interest rates;Banking;WP,math display
    Date: 2020–08–07
  21. By: Mahan Tahvildari
    Abstract: We study the hedging and valuation of European and American claims on a non-traded asset $Y$, when a traded stock $S$ is available for hedging, with $S$ and $Y$ following correlated geometric Brownian motions. This is an incomplete market, often called a basis risk model. The market agent's risk preferences are modelled using a so-called forward performance process (forward utility), which is a time-decreasing utility of exponential type. Moreover, the market agent (investor) does not know with certainty the values of the asset price drifts. This market setting with drift parameter uncertainty is the partial information scenario. We discuss the stochastic control problem obtained by setting up the hedging portfolio and derive the optimal hedging strategy. Furthermore, a (dual) forward indifference price representation of the claim and its PDE are obtained. With these results, the residual risk process representing the basis risk (hedging error), pay-off decompositions and asymptotic expansions of the indifference price in the European case are derived. We develop the analogous stochastic control and stopping problem with an American claim and obtain the corresponding forward indifference price valuation formula.
    Date: 2021–01
  22. By: Sridhar Ravula
    Abstract: A public firm's bankruptcy prediction is an important financial research problem because of the security price downside risks. Traditional methods rely on accounting metrics that suffer from shortcomings like window dressing and retrospective focus. While disclosure text-based metrics overcome some of these issues, current methods excessively focus on disclosure tone and sentiment. There is a requirement to relate meaningful signals in the disclosure text to financial outcomes and quantify the disclosure text data. This work proposes a new distress dictionary based on the sentences used by managers in explaining financial status. It demonstrates the significant differences in linguistic features between bankrupt and non-bankrupt firms. Further, using a large sample of 500 bankrupt firms, it builds predictive models and compares the performance against two dictionaries used in financial text analysis. This research shows that the proposed stress dictionary captures unique information from disclosures and the predictive models based on its features have the highest accuracy.
    Date: 2021–01
  23. By: Yiyan Huang; Cheuk Hang Leung; Xing Yan; Qi Wu; Nanbo Peng; Dongdong Wang; Zhixiang Huang
    Abstract: This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.
    Date: 2020–12
  24. By: Victor Olkhov
    Abstract: This paper discusses the value-at-risk (VaR) concept and assesses the financial adequacy of the price probability determined by frequency of trades at price p. We take the price definition as the ratio of executed trade value to volume and show that it leads to price statistical moments, which differ from those, generated by frequency price probability. We derive the price n th statistical moments as ratio of n th statistical moments of the value and the volume of executed transactions. We state that the price probability determined by frequency of trades at price p does not describe probability of executed trade prices and VaR based on frequency price probability may be origin for unexpected and excessive losses. We explain the need to replace frequency price probability by frequency probabilities of the value and the volume of executed transactions and derive price characteristic function. After 50 years of the VaR usage main problems of the VaR concept are still open. We believe that VaR commitment to forecast the price probability for the time horizon T seems to be one of the most tough and expensive puzzle of modern finance.
    Date: 2021–01
  25. By: Paweł Sakowski (Quantitative Finance Research Group, Department of Quantitative Finance, Faculty of Economic Sciences, University of Warsaw); Daria Turovtseva
    Abstract: The aim of the paper is to check if cryptocurrency Bitcoin – a new investable asset class representative – is able to improve the performance of an optimal portfolio. Using two Markowitz criteria of optimization – expected return maximization and expected shortfall (CVaR) minimization – we test the investment opportunities after adding Bitcoin to the portfolio of 10 traditional assets (among them equity, fixed income, money, commodities and money market indices). Using daily observations from 1.05.2013 till 24.05.2019, we examine the behavior of the portfolios without and with Bitcoin and check if the return-risk ratio improves for the latter. Discussing the results, we conduct the sensitivity analysis by changing the lookback window (LB) and rebalancing frequency (RB) parameters. Empirical analysis suggests that Bitcoin-inclusive portfolios provide an investor with wider diversification opportunities. Robustness check confirms the findings and also advocates for the cryptocurrency to be added to the portfolio.
    Keywords: Portfolio optimization, portfolio theory, cryptocurrency, Bitcoin, Markowitz model, asset allocation, portfolio diversification, investment opportunities
    JEL: C20 C22 C61 C80 G14 G17
    Date: 2020
  26. By: International Monetary Fund
    Abstract: Macrofinancial risks stem from the economy’s vulnerability to external shocks to tourism and real estate investment, exposure to frequent and severe hurricanes, and a small and illiquid real estate market. Stress tests reveal the overall banking system is resilient to a range of adverse scenarios given large aggregate capital and liquidity buffers. Some domestic banks and the two largest credit unions are more vulnerable to asset quality shocks and tail risk conditions. Asset quality and profitability are key determinants of financial institutions’ resilience to adverse shocks. Liquidity, market, sovereign and financial contagion risks are low. The offshore banking sector is not a source of traditional banking risks.
    Keywords: Banking;Nonperforming loans;Stress testing;Credit bureaus;Credit;ISCR,CR,central bank,interest rate,banking sector,credit risk,NPL ratio
    Date: 2019–07–01
  27. By: Li, Chenxing; Maheu, John M
    Abstract: This paper proposes a new parsimonious multivariate GARCH-jump (MGARCH-jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns as well as beta dynamics of a stock. Applied to daily stock returns, the model identifies co-jumps well and shows that both jump arrivals and jump sizes are highly correlated. The jump model has better predictions compared to a benchmark multivariate GARCH model.
    Keywords: Multivariate GARCH; Jumps; Multinomial; Co-jump; beta dynamics; Value-at-Risk
    JEL: C32 C53 C58 G1 G10
    Date: 2020–12

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