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on Risk Management |
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Issue of 2019‒09‒02
nineteen papers chosen by |
| By: | Sander Barendse (University of Oxford); Erik Kole (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam) |
| Abstract: | We investigate the effect of estimation error on backtests of (multi-period) expected shortfall (ES) forecasts. These backtests are based on first order conditions of a recently introduced family of jointly consistent loss functions for Value-at-Risk (VaR) and ES. We provide explicit expressions for the additional terms in the asymptotic covariance matrix that result from estimation error, and propose robust tests that account for it. Monte Carlo experiments show that the tests that ignore these terms suffer from size distortions, which are more pronounced for higher ratios of out-of-sample to in-sample observations. Robust versions of the backtests perform well, although this also depends on the choice of conditioning variables. In an application to VaR and ES forecasts for daily FTSE 100 index returns as generated by AR-GARCH, AR-GJR-GARCH, and AR-HEAVY models, we find that estimation error substantially impacts the outcome of the backtests. |
| Keywords: | expected shortfall, backtesting, risk management, tail risk, Value-at-Risk |
| JEL: | C12 C53 C58 G17 |
| Date: | 2019–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:2019058&r=all |
| By: | Jingyuan Wang; Yang Zhang; Ke Tang; Junjie Wu; Zhang Xiong |
| Abstract: | Recent years have witnessed the successful marriage of finance innovations and AI techniques in various finance applications including quantitative trading (QT). Despite great research efforts devoted to leveraging deep learning (DL) methods for building better QT strategies, existing studies still face serious challenges especially from the side of finance, such as the balance of risk and return, the resistance to extreme loss, and the interpretability of strategies, which limit the application of DL-based strategies in real-life financial markets. In this work, we propose AlphaStock, a novel reinforcement learning (RL) based investment strategy enhanced by interpretable deep attention networks, to address the above challenges. Our main contributions are summarized as follows: i) We integrate deep attention networks with a Sharpe ratio-oriented reinforcement learning framework to achieve a risk-return balanced investment strategy; ii) We suggest modeling interrelationships among assets to avoid selection bias and develop a cross-asset attention mechanism; iii) To our best knowledge, this work is among the first to offer an interpretable investment strategy using deep reinforcement learning models. The experiments on long-periodic U.S. and Chinese markets demonstrate the effectiveness and robustness of AlphaStock over diverse market states. It turns out that AlphaStock tends to select the stocks as winners with high long-term growth, low volatility, high intrinsic value, and being undervalued recently. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.02646&r=all |
| By: | Xiao,Tim |
| Abstract: | The incremental risk charge (IRC) is a new regulatory requirement from the Basel Committee in response to the recent financial crisis. Notably few models for IRC have been developed in the literature. This paper proposes a methodology consisting of two Monte Carlo simulations. The first Monte Carlo simulation simulates default, migration, and concentration in an integrated way. Combining with full re-valuation, the loss distribution at the first liquidity horizon for a subportfolio can be generated. The second Monte Carlo simulation is the random draws based on the constant level of risk assumption. It convolutes the copies of the single loss distribution to produce one year loss distribution. The aggregation of different subportfolios with different liquidity horizons is addressed. Moreover, the methodology for equity is also included, even though it is optional in IRC. |
| Keywords: | Incremental risk charge (IRC),constant level of risk,,liquidity horizon,constant loss distribution,Merton-type model,concentration |
| JEL: | E44 G21 G24 G32 G33 G18 G28 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:201810&r=all |
| By: | Stefania Albanesi; Domonkos F. Vamossy |
| Abstract: | We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. |
| JEL: | C45 D14 D18 E44 G0 G2 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26165&r=all |
| By: | Agnieszka Borowska (Vrije Universiteit Amsterdam); Lennart Hoogerheide (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam); Herman van Dijk (Erasmus University Rotterdam) |
| Abstract: | A novel approach to inference for a specific region of the predictive distribution is introduced. An important domain of application is accurate prediction of financial risk measures, where the area of interest is the left tail of the predictive density of logreturns. Our proposed approach originates from the Bayesian approach to parameter estimation and time series forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region of interest in case of misspecification. The first main contribution of the paper is the novel concept of the Partially Censored Posterior (PCP), where the set of model parameters is partitioned into two subsets: for the first subset of parameters we consider the standard marginal posterior, for the second subset of parameters (that are particularly related to the region of interest) we consider the conditional censored posterior. The censoring means that observations outside the region of interest are censored: for those observations only the probability of being outside the region of interest matters. This quasi-Bayesian approach yields more precise parameter estimation than a fully censored posterior for all parameters, and has more focus on the region of interest than a standard Bayesian approach. The second main contribution is that we introduce two novel methods for computationally efficient simulation: Conditional MitISEM, a Markov chain Monte Carlo method to simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is introduced to further decrease the numerical standard errors of the Value-at-Risk and Expected Shortfall estimators. The third main contribution is that we consider the effect of using a time-varying boundary of the region of interest, which may provide more information about the left tail of the distribution of the standardized innovations. Extensive simulation and empirical studies show the ability of the introduced method to outperform standard approaches. |
| Keywords: | Bayesian inference, censored likelihood, censored posterior, partially censored posterior, misspecification, density forecasting, Markov chain Monte Carlo, importance sampling, mixture of Student's t, Value-at-Risk, Expected Shortfall |
| JEL: | C11 C53 C58 |
| Date: | 2019–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20190057&r=all |
| By: | Mavrakana, Christina; Psillaki, Maria |
| Abstract: | This paper investigates the impact of bank governance on European bank performance and risk- taking. More precisely, using a sample of 75 banks from 18 European countries over the 2004-2016 period, we examine the relationship between bank governance variables namely board size, age of directors, financial experience, board independency, gender diversity, governance system and compensation on bank performance and risk-taking. Our empirical analysis shows that experienced directors increase bank performance and reduce risk-taking. Moreover, female directors have a positive impact on bank performance but the results are mixed for risk-taking. We also find that the one-tier system improves bank performance and reduces credit risk. Moreover, compensation is positively related with bank performance. The empirical findings are inconclusive regarding risk-taking. In addition, the impact of board size and age on bank performance differs, depending on the measure. We find that older members increase risk-taking. Finally, equity linked wealth leads to better bank performance but it also increases risk-taking. Our results differ according to time period and location criteria. |
| Keywords: | Bank governance, financial crises, corporate governance, bank performance, executive compensation |
| JEL: | G01 G21 G28 G34 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95776&r=all |
| By: | Konstantinos Gkillas (Department of Business Administration, University of Patras, Patras, Greece); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); David Roubaud (Montpellier Business School, Montpellier, France) |
| Abstract: | In this paper, we extend existing studies by considering the relationships across crude oil, gold, and Bitcoin markets. Using high-frequency data from December 2, 2014 to June 10, 2018, we analyze spillovers in volatility jumps and realized second, third, and fourth moments across crude oil, gold, and Bitcoin markets via Granger causality and generalized impulse response analyses in daily frequency. Results suggest evidence of predictability and emphasize, among others, the need of jointly modeling linkages across those three markets with higher-order moments; otherwise, inaccurate risk assessment and investment inferences may arise. The responses of realized volatility shocks and volatility jump are generally positive. Furthermore, results indicate evidence of a weaker relationship between gold – crude oil, and Bitcoin – crude oil compared to the case of Bitcoin - gold. Practical implications are discussed. |
| Keywords: | crude oil, gold, Bitcoin, realized moments, spillover effect |
| JEL: | C46 G10 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201965&r=all |
| By: | Lotfi Boudabsa (Ecole Polytechnique Fédérale de Lausanne - School of Basic Sciences); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute) |
| Abstract: | We introduce a computational framework for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the replicating martingale of a portfolio from a finite sample of its terminal cumulative cash flow. The learned replicating martingale is given in closed form thanks to a suitable choice of the kernel. We develop an asymptotic theory and prove convergence and a central limit theorem. We also derive finite sample error bounds and concentration inequalities. Numerical examples show good results for a relatively small training sample size. |
| Keywords: | dynamic portfolio valuation, kernel ridge regression, learning theory, reproducing kernel Hilbert space, portfolio risk management |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1934&r=all |
| By: | Mariano Max Croce; Thien T. Nguyen; Steve Raymond |
| Abstract: | When government debt is sluggish, consumption exhibits lower expected growth, more long-run uncertainty, and more long-run downside risk. Simultaneously, the risk premium on the consumption claim (Koijen et al. (2010), Lustig et al. (2013)) increases and features more positive (adverse) skewness. We rationalize these findings in an endogenous growth model in which fiscal policy is distortionary, the value of innovation depends on fiscal risk, and the representative agent is sensitive to the resulting distribution of consumption risk. Our model suggests that committing to a rapid reduction of the debt-to-output ratio can enhance the value of innovation, aggregate wealth, and welfare. |
| JEL: | E62 G1 H2 H3 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26177&r=all |
| By: | Bago, Jean-Louis; Souratié, Wamadini M.; Ouédraogo, Moussa; Ouédraogo, Ernest; Dembélé, Alou |
| Abstract: | We provide new empirical evidence of bubbles timing in the stock market of South Africa. We apply the generalized sup ADF (GSADF) unit root test of Phillips et al. (2015) to monthly share prices from January 1960 to July 2019, to detect explosive behaviors. Results indicate that, overall, South Africa’s stock market has been exuberant during the period 1960-2019. We find strong evidence of three bubble episodes during the periods of April 1968 to July 1969, December 1979 to November 1980 and April 2006 to May 2008 in the stock market of South Africa. The last two bubbles correspond to the 1979 international oil crisis and the 2008 financial crisis suggesting that the south african stock market is still vulnerable to exogenous shocks. |
| Keywords: | Bubble, Stock market, GSADF test, South Africa |
| JEL: | G12 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95685&r=all |
| By: | Olkhov, Victor |
| Abstract: | This paper develops methods and framework of economic theory free from general equilibrium tools and assumptions. We model macroeconomics as system of agents those perform transactions with other agents under action of numerous expectations. Agents expectations are formed by economic and financial variables, transactions, expectations of other agents, other factors that impact macro economy. We use risk ratings of agents as their coordinates on economic domain and approximate description of economic variables, transactions and expectations of numerous separate agents by density functions of variables, transactions and expectations of aggregated agents on economic domain. Motion of separate agents on economic domain due to change of agents risk rating produce economic flows of variables, transactions and expectations. These risk flows define dynamics of economic variables and disturb any supposed market equilibrium states all the time. Permanent evolution of market supply-demand states due to risk flows makes general equilibrium concept too doubtful. As example we apply our methods to model assets pricing and return fluctuations. |
| Keywords: | economic theory; risk ratings; economic flows; density functions |
| JEL: | C00 C50 E30 G0 |
| Date: | 2019–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95628&r=all |
| By: | Guillaume Ouellet Leblanc; Maxime Leboeuf |
| Abstract: | Lending to business is central to economic growth because it supports investment by firms. Knowing how market participants view risk in the financial system can give the Bank of Canada information about future growth in business loans. In this note, we look at three market-based risk measures and find that sudden increases in the perception of risk in the Canadian banking system are associated with a weaker outlook for business loans and real gross domestic product. |
| Keywords: | Business fluctuations and cycles; Financial markets |
| JEL: | E32 E44 G12 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocsan:19-26&r=all |
| By: | Schuknecht, Ludger |
| Abstract: | The paper analyses the linkages from financial developments to public finances. It maps and discusses the transmission channels to fiscal variables. These channels include asset prices, financing conditions, balance sheets of banks, non-banks and central banks and international linkages. The study argues that the fiscal effects via each and all these channels can be very serious in magnitude and can put the sustainability of public finances at risk. However, there is an only limited in-depth analysis of these channels and risks. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:safewh:62&r=all |
| By: | Enno Mammen (University of Heidelberg, Germany); Jens Perch Nielsen (Cass Business School, City, University of London, UK); Michael Scholz (University of Graz, Austria); Stefan Sperlich (Universite de Geneve, Switzerland) |
| Abstract: | In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and inflation. In particular, we apply and implement in a two-step procedure a fully nonparametric smoother with the covariates and the smoothing parameters chosen via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realized conditional variance for both the one-year and five-year horizon. |
| Keywords: | Benchmark; Cross-validation; Prediction; Stock return volatility; Long-term forecasts; Overlapping returns; Autocorrelation |
| JEL: | C14 C53 C58 G17 G22 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:grz:wpaper:2019-08&r=all |
| By: | N. Meade; J. E. Beasley; C. J. Adcock |
| Abstract: | In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates. Secondly, we extend ex-post efficient set mathematics to overcome the biases in the estimation of the ex-ante efficient frontier. A density forecasting approach is used to measure the accuracy of ex-ante estimates using the Berkowitz statistic, we develop this statistic to increase its sensitivity to changes in the data generating process. The area of risk-expected return space where the density forecasts are accurate, where ex-post performance matches ex-ante estimates, is termed the consistency region. Under the 'laboratory' conditions of a simulated multivariate normal data set, we compute the consistency region and the estimated ex-post frontier. Over different sample sizes used for estimation, the behaviour of the consistency region is shown to be both intuitively reasonable and to enclose the estimated ex-post frontier. Using actual data from the constituents of the US Dow Jones 30 index, we show that the size of the consistency region is time dependent and, in volatile conditions, may disappear. Using our development of the Berkowitz statistic, we demonstrate the superior performance of an investment strategy based on consistent rather than efficient portfolios. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08442&r=all |
| By: | Sakai Ando |
| Abstract: | This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks. |
| Date: | 2019–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/181&r=all |
| By: | Christian Bayer; Blanka Horvath; Aitor Muguruza; Benjamin Stemper; Mehdi Tomas |
| Abstract: | Techniques from deep learning play a more and more important role for the important task of calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a catalyst for resurfacing interest in research in this area. In this paper we advocate an alternative (two-step) approach using deep learning techniques solely to learn the pricing map -- from model parameters to prices or implied volatilities -- rather than directly the calibrated model parameters as a function of observed market data. Having a fast and accurate neural-network-based approximating pricing map (first step), we can then (second step) use traditional model calibration algorithms. In this work we showcase a direct comparison of different potential approaches to the learning stage and present algorithms that provide a suffcient accuracy for practical use. We provide a first neural network-based calibration method for rough volatility models for which calibration can be done on the y. We demonstrate the method via a hands-on calibration engine on the rough Bergomi model, for which classical calibration techniques are diffcult to apply due to the high cost of all known numerical pricing methods. Furthermore, we display and compare different types of sampling and training methods and elaborate on their advantages under different objectives. As a further application we use the fast pricing method for a Bayesian analysis of the calibrated model. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08806&r=all |
| By: | C. A. Valle; J. E. Beasley |
| Abstract: | In this paper we consider the problem of minimising drawdown in a portfolio of financial assets. Here drawdown represents the relative opportunity cost of the single best missed trading opportunity over a specified time period. We formulate the problem (minimising average drawdown, maximum drawdown, or a weighted combination of the two) as a nonlinear program and show how it can be partially linearised by replacing one of the nonlinear constraints by equivalent linear constraints. Computational results are presented (generated using the nonlinear solver SCIP) for three test instances drawn from the EURO STOXX 50, the FTSE 100 and the S&P 500 with daily price data over the period 2010-2016. We present results for long-only drawdown portfolios as well as results for portfolios with both long and short positions. These indicate that (on average) our minimal drawdown portfolios dominate the market indices in terms of return, Sharpe ratio, maximum drawdown and average drawdown over the (approximately 1800 trading day) out-of-sample period. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.08684&r=all |
| By: | Jang, Youngsoo; Lee, Soyoung |
| Abstract: | We develop an endogenous grid method for models with the option to default in which price schedules are endogenously determined in equilibrium and depend on individuals’ states. The algorithm has noticeable computational benefits in efficiency and accuracy. We obtain these computational benefits by combining Fella’s (2014) identification for non-concave regions with our algorithm that numerically searches for risky borrowing limits. These two procedures identify the region of solution sets to which Carroll’s (2006) endogenous grid method is applicable. To demonstrate the method, we apply our method to Nakajima and Rios-Rull’s(2014) model. In terms of computation time, this method is seven to twenty-seven times faster than the conventional grid search method. Moreover, various types of accuracy tests indicate that our method yields more accurate results than the grid search method. |
| Keywords: | Endogenous grid method, Default, Bankruptcy |
| JEL: | C63 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95721&r=all |