nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒03‒09
eighteen papers chosen by

  1. Quantum Implementation of Risk Analysis-relevant Copulas By Janusz Milek
  2. Foreign exchange rate exposure of companies under dynamic regret By Entrop, Oliver; Fuchs, Fabian U.
  3. Variability in risk-weighted assets: what does the market think? By Edson Bastos e Santos; Neil Esho; Marc Farag; Christopher Zuin
  4. On the statistics of scaling exponents and the Multiscaling Value at Risk By Giuseppe Brandi; T. Di Matteo
  5. The benefits are at the tail: uncovering the impact of macroprudential policy on growth-at-risk By Jorge E. Galán
  6. Leverage and Risk in Hedge Funds By Daniel Barth; Laurel Hammond; Phillip Monin
  7. Equal risk option pricing with deep reinforcement learning By Alexandre Carbonneau; Fr\'ed\'eric Godin
  8. Central Clearing and Systemic Liquidity Risk By G. Thomas Kingsley; Anna L. Paulson; Todd Prono; Travis D. Nesmith
  9. Default Ambiguity: Finding the Best Solution to the Clearing Problem By P\'al Andr\'as Papp; Roger Wattenhofer
  10. Cyber Risk Surveillance: A Case Study of Singapore By Joseph Goh; Heedon Kang; Zhi Xing Koh; Jin Way Lim; Cheng Wei Ng; Galen Sher; Chris Yao
  11. Crowded trades, market clustering, and price instability By Marc van Kralingen; Diego Garlaschelli; Karolina Scholtus; Iman van Lelyveld
  12. Negative interest rate, bank profitability and risk-taking By Whelsy Boungou
  13. Firms Default Prediction with Machine Learning By Tesi Aliaj; Aris Anagnostopoulos; Stefano Piersanti
  14. On the extension property of dilatation monotone risk measures By Massoomeh Rahsepar; Foivos Xanthos
  15. Forecasting Realized Volatility Matrix With Copula-Based Models By Wenjing Wang; Minjing Tao
  16. The Hedge Fund Industry is Bigger (and has Performed Better) Than You Think By Daniel Barth; Juha Joenvaara; Mikko Kauppila; Russ Wermers
  17. Mathematical Foundations of Regression Methods for the approximation of the Forward Initial Margin By Lucia Cipolina Kun
  18. Search for Yield in Large International Corporate Bonds : Investor Behavior and Firm Responses By Calomiris,Charles W.; Larrain,Mauricio; Schmukler,Sergio L.; Williams,Tomas

  1. By: Janusz Milek
    Abstract: Modern quantitative risk management relies on an adequate modeling of the tail dependence and a possibly accurate quantification of risk measures, like Value at Risk (VaR), at high confidence levels like 1 in 100 or even 1 in 2000. Quantum computing makes such a quantification quadratically more efficient than the Monte Carlo method; see (Woerner and Egger, 2018) and, for a broader perspective, (Or\'us et al., 2018). An important element of the risk analysis toolbox is copula, see (Jouanin et al., 2004) regarding financial applications. However, to the best knowledge of the author, no quantum computing implementation for sampling from a risk modeling-relevant copula in explicit form has been published so far. Our focus here is implementation of simple yet powerful copula models, capable of a satisfactory capturing the joint tail behaviour of the modelled risk factors. This paper deals with a few simple copula families, including Multivariate B11 (MB11) copula family, presented in (Milek, 2014). We will show that this copula family is suitable for the risk aggregation as it is exceptionally able to reproduce tail dependence structures; see (Embrechts et al., 2016) for a relevant benchmark as well as necessary and sufficient conditions regarding the ultimate feasible bivariate tail dependence structures. It turns out that such a discretized copula can be expressed using simple constructs present in the quantum computing: binary fraction expansion format, comonotone/independent random variables, controlled gates, and convex combinations, and is therefore suitable for a quantum computer implementation. This paper presents design behind the quantum implementation circuits, numerical and symbolic simulation results, and experimental validation on IBM quantum computer. The paper proposes also a generic method for quantum implementation of any discretized copula.
    Date: 2020–02
  2. By: Entrop, Oliver; Fuchs, Fabian U.
    Abstract: This paper analyzes optimal hedge ratios for foreign exchange (FX) rate risk of companies. Our contribution to the literature is twofold: (i) We present a theoretical two-period regret model that allows us to analyze the determinants of the optimal hedge ratio given the outcome of past hedging decisions and future expectations. The model implies that the optimal hedge ratio depends on the past hedge ratio, the past exchange rate return, the expected exchange rate return and the skewness of its distribution, its covariance to the foreign market return, as well as the company's risk and regret aversion. (ii) We test the related model-derived hypotheses on a broad sample of US non-financial companies over the period 1995 to 2015 and find strong evidence for the model's predictions. By adding a dynamic regret approach to the hedging and FX literature we shed further light on the rationale behind selective hedging.
    Keywords: exchange rate exposure,regret aversion,hedging,risk aversion,derivatives
    JEL: F31 G15 G32
    Date: 2020
  3. By: Edson Bastos e Santos; Neil Esho; Marc Farag; Christopher Zuin
    Abstract: The global financial crisis highlighted a number of weaknesses in the regulatory framework, including concerns about excessive variability in banks' risk-weighted assets (RWAs) stemming from their use of internal models. The Basel III reforms that were finalised in 2017 by the Basel Committee on Banking Supervision seek to reduce this excessive RWA variability. This paper develops a novel approach to measuring RWA variability - the variability ratio - by comparing a market-implied measure of RWAs with banks' reported regulatory RWAs. Using a panel data set comprising a large sample of internationally-active banks over the period 2001 to 16, we find that there was a wide degree of RWA variability among banks, and that market-implied RWA estimates were persistently higher than regulatory RWAs. We then assess the determinants of this variability, and find a strong and statistically-significant association between our measure of RWA variability and (i) the share of opaque assets held by banks (eg derivatives); (ii) the degree to which a bank is capital constrained; and (iii) jurisdiction-specific factors. These results suggest that market participants may be applying an 'opaqueness' premium for banks that hold highly-complex instruments, and that the incentive for banks to game their internal models is particularly acute for capital-constrained banks. The results also point to the importance of jurisdiction-specific factors in explaining RWA variability. In addition, we find that RWA variability directly affects banks' own profitability through higher funding costs. Finally, we find that the 2017 Basel III reforms - most notably the output floor - help to reduce excessive RWA variability.
    Keywords: bank regulation, capital, Basel III, risk-weighted assets, financial stability
    JEL: G20 G21 G28
    Date: 2020–02
  4. By: Giuseppe Brandi; T. Di Matteo
    Abstract: Scaling and multiscaling financial time series have been widely studied in the literature. The research on this topic is vast and still flourishing. One way to analyse the scaling properties of time series is through the estimation of scaling exponents. These exponents are recognized as being valuable measures to discriminate between random, persistent, and anti-persistent behaviours in time series. In the literature, several methods have been proposed to study the multiscaling property and in this paper we use the generalized Hurst exponent (GHE). On the base of this methodology, we propose a novel statistical procedure to robustly estimate and test the multiscaling property and we name it RNSGHE. This methodology, together with a combination of t-tests and F-tests to discriminated between real and spurious scaling. Moreover, we also introduce a new methodology to estimate the optimal aggregation time used in our methodology. We numerically validate our procedure on simulated time series using the Multifractal Random Walk (MRW) and then apply it to real financial data. We also present results for times series with and without anomalies and we compute the bias that such anomalies introduce in the measurement of the scaling exponents. Finally, we show how the use of proper scaling and multiscaling can ameliorate the estimation of risk measures such as Value at Risk (VaR). We also propose a methodology based on Monte Carlo simulation, that we name Multiscaling Value at Risk (MSVaR), which takes into account the statical properties of multiscaling time series. We show that by using this statistical procedure in combination with the robustly estimated multiscaling exponents, the one year forecasted MSVaR mimics the VaR on the annual data for the majority of the stocks analysed.
    Date: 2020–02
  5. By: Jorge E. Galán (Banco de España. Financial Stability and Macroprudential Policy Department)
    Abstract: This paper brings together recent developments on the growth-at-risk methodology and the literature on the impact of macroprudential policy. For this purpose, I extend the recent proposals on the use of quantile regressions of GDP growth by including macrofinancial variables with early warning properties of systemic risk, and macroprudential measures. I identify heterogeneous effects of macroprudential policy on GDP growth, uncovering important benefits on the left tail of its distribution. The positive effect of macroprudential policy on reducing the downside risk of GDP is found to be larger than the negative impact on the median, suggesting a net positive effect in the mid-term. Nonetheless, I identify heterogeneous effects depending on the position in the financial cycle, the direction of the policy, the type of instrument, and the time elapsed since its implementation. In particular, tightening capital measures during expansions may take up to two years in evidencing benefits on growth-at-risk, while the positive impact of borrower-based measures is rapidly observed. This suggests the need of implementing capital measures, such as the countercyclical capital buffer, early enough in the cycle; while borrower-based measures can be tightened in more advanced stages. Conversely, in downturns the benefits of loosening capital measures are immediate, while those of borrower-based measures are limited. Overall, this study provides a useful framework to assess costs and benefits of macroprudential policy in terms of GDP growth, and to identify the term-structure of specific types of instruments.
    Keywords: financial stability, growth-at-risk, systemic risk, macroprudential policy, quantile regressions
    JEL: C32 E32 E58 G01 G28
    Date: 2020–03
  6. By: Daniel Barth (Office of Financial Research); Laurel Hammond (Office of Financial Research); Phillip Monin (Office of Financial Research)
    Abstract: The use of leverage is often considered a key potential systemic risk in hedge funds. Yet, data limitations have made empirical analyses of hedge fund leverage difficult. Traditional theories predict leverage and portfolio risk are positively linearly related. Alternatively, an emerging wave of theories of leverage constraints predict leverage and asset risk are negatively correlated, and therefore leverage and portfolio risk may be unrelated or even negatively related. Consistent with theories of leverage constraints, we find that hedge fund leverage and portfolio risk are weakly negatively correlated. This arises from a strong negative association between leverage and asset risk - in particular, market beta. The average market beta on funds' assets explains 20% of the cross-sectional variation in hedge fund leverage, and 47% for the subsample of equity-style funds. Also consistent with these theories, leverage and portfolio alpha are strongly positively related, but this relationship is entirely explained by market beta. Our findings suggest that the association between leverage and risk in hedge funds is nuanced, and that leverage is in part used to scale the payoffs of low-beta, high-alpha securities, resulting in an essentially flat relationship between leverage and portfolio risk.
    Keywords: hedge funds, leverage, systemic risk, financial stability, low beta anomaly
    Date: 2020–02–25
  7. By: Alexandre Carbonneau; Fr\'ed\'eric Godin
    Abstract: This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free. The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $\epsilon$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty. Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.
    Date: 2020–02
  8. By: G. Thomas Kingsley; Anna L. Paulson (Federal Reserve Bank of Chicago); Todd Prono; Travis D. Nesmith
    Abstract: By stepping between bilateral counterparties, a central counterparty (CCP) transforms credit exposure. CCPs generally improve financial stability. Nevertheless, large CCPs are by nature concentrated and interconnected with major global banks. Moreover, although they mitigate credit risk, CCPs create liquidity risks, because they rely on participants to provide cash. Such requirements increase with both market volatility and default; consequently, CCP liquidity needs are inherently procyclical. This procyclicality makes it more challenging to assess CCP resilience in the rare event that one or more large financial institutions default. Liquidity-focused macroprudential stress tests could help to assess and manage this systemic liquidity risk.
    Keywords: margin; financial systems; Central Counterparties (CCPs); procyclicality; liquidity risk; financial stability
    JEL: G28 E58 N22 G21 G23
    Date: 2019–12–01
  9. By: P\'al Andr\'as Papp; Roger Wattenhofer
    Abstract: We study financial networks with debt contracts and credit default swaps between specific pairs of banks. Given such a financial system, we want to decide which of the banks are in default, and how much of their liabilities these defaulting banks can pay. There can easily be multiple different solutions to this problem, leading to a situation of default ambiguity and a range of possible solutions to implement for a financial authority. In this paper, we study the general properties of the solution space of such financial systems, and analyze a wide range of reasonable objective functions for selecting from the set of solutions. Examples of such objective functions include minimizing the number of defaulting banks, minimizing the amount of unpaid debt, maximizing the number of satisfied banks, maximizing the equity of a specific bank, finding the most balanced distribution of equity, and many others. We show that for all of these objective functions, it is not only NP-hard to find the optimal solution, but it is also NP-hard to approximate this optimum: for each objective function, we show an inapproximability either to an $n^{1/2-\epsilon}$ or to an $n^{1/4-\epsilon}$ factor for any $\epsilon>0$, with $n$ denoting the number of banks in the system. Thus even if an authority has clear criteria to select a solution in case of default ambiguity, it is computationally intractable to find a solution that is reasonably good in terms of this criteria. We also show that our hardness results hold in a wide range of different model variants.
    Date: 2020–02
  10. By: Joseph Goh; Heedon Kang; Zhi Xing Koh; Jin Way Lim; Cheng Wei Ng; Galen Sher; Chris Yao
    Abstract: Cyber risk is an emerging source of systemic risk in the financial sector, and possibly a macro-critical risk too. It is therefore important to integrate it into financial sector surveillance. This paper offers a range of analytical approaches to assess and monitor cyber risk to the financial sector, including various approaches to stress testing. The paper illustrates these techniques by applying them to Singapore. As an advanced economy with a complex financial system and rapid adoption of fintech, Singapore serves as a good case study. We place our results in the context of recent cybersecurity developments in the public and private sectors, which can be a reference for surveillance work.
    Date: 2020–02–10
  11. By: Marc van Kralingen; Diego Garlaschelli; Karolina Scholtus; Iman van Lelyveld
    Abstract: Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.
    Date: 2020–02
  12. By: Whelsy Boungou (Larefi, University of Bordeaux)
    Keywords: Negative interest rates, bank profitability, Bank risk taking, European Union countries,dynamic panel data model
    JEL: E43 E52 E58 G21
    Date: 2019–07
  13. By: Tesi Aliaj; Aris Anagnostopoulos; Stefano Piersanti
    Abstract: Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in \emph{default}, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies' past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies' public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017).
    Date: 2020–02
  14. By: Massoomeh Rahsepar; Foivos Xanthos
    Abstract: Let $\mathcal{X}$ be a subset of $L^1$ that contains the space of simple random variables $\mathcal{L}$ and $\rho: \mathcal{X} \rightarrow (-\infty,\infty]$ a dilatation monotone functional with the Fatou property. In this note, we show that $\rho$ extends uniquely to a $\sigma(L^1,\mathcal{L})$ lower semicontinuous and dilatation monotone functional $\overline{\rho}: L^1 \rightarrow (-\infty,\infty]$. Moreover, $\overline{\rho}$ preserves monotonicity, (quasi)convexity, and cash-additivity of $\rho$. Our findings complement recent extension results for quasiconvex law-invariant functionals proved in [17,20]. As an application of our results, we show that transformed norm risk measures on Orlicz hearts admit a natural extension to $L^1$ that retains the robust representations obtained in [4,6].
    Date: 2020–02
  15. By: Wenjing Wang; Minjing Tao
    Abstract: Multivariate volatility modeling and forecasting are crucial in financial economics. This paper develops a copula-based approach to model and forecast realized volatility matrices. The proposed copula-based time series models can capture the hidden dependence structure of realized volatility matrices. Also, this approach can automatically guarantee the positive definiteness of the forecasts through either Cholesky decomposition or matrix logarithm transformation. In this paper we consider both multivariate and bivariate copulas; the types of copulas include Student's t, Clayton and Gumbel copulas. In an empirical application, we find that for one-day ahead volatility matrix forecasting, these copula-based models can achieve significant performance both in terms of statistical precision as well as creating economically mean-variance efficient portfolio. Among the copulas we considered, the multivariate-t copula performs better in statistical precision, while bivariate-t copula has better economical performance.
    Date: 2020–02
  16. By: Daniel Barth (Office of Financial Research); Juha Joenvaara (School of Business, Aalto University); Mikko Kauppila (Oulu Business School, University of Oulu); Russ Wermers (Smith School of Business, University of Maryland at College Park)
    Abstract: Of first-order importance to the study of potential systemic risks in hedge funds is the aggregate size of the industry. The worldwide hedge fund industry has been estimated by regulators and industry experts as having total net assets under management of $2.3 - 3.7 trillion as of the end of 2016. Using a newly combined database of several hedge fund information vendors, augmented by the first detailed, systematic regulatory collection of data on large hedge funds in the United States, we estimate that the worldwide net assets under management were at least $5.2 trillion in 2016, over 40% larger than the most generous estimate. Gross assets, which represent the balance sheet value of hedge fund assets, exceeds $8.5 trillion. We further decompose hedge fund assets by their self-reported strategy and by fund domicile. We also show that the total returns earned by funds that report to the public databases are significantly lower than the returns of funds that report only on regulatory filings, both in aggregate and within nearly every fund strategy. This difference appears to be driven entirely by alpha, rather than by differences in exposures to systemic risk factors. However, net investor flows are considerably higher for funds reporting publicly, suggesting previous estimates of the flow-performance relationship are likely biased. Our new, and much larger, estimates of the size of the hedge fund industry should help regulators and prudential authorities to better gauge the systemic risks posed by the industry, and to better evaluate potential data gaps in private funds. Our results aslo suggest that systemic risk is roughly similar in publicly and non-publicly reporting funds.
    Keywords: hedge funds, net assets, gross assets, strategy, domicile, returns, flows
    Date: 2020–02–25
  17. By: Lucia Cipolina Kun
    Abstract: Abundant literature has been published on approximation methods for the forward initial margin. The most popular ones being the family of regression methods. This paper describes the mathematical foundations on which these regression approximation methods lie. We introduce mathematical rigor to show that in essence, all the methods propose variations of approximations for the conditional expectation function, which is interpreted as an orthogonal projection on Hilbert spaces. We show that each method is simply choosing a different functional form to numerically estimate the conditional expectation. We cover in particular the most popular methods in the literature so far, Polynomial approximation, Kernel regressions and Neural Networks.
    Date: 2020–02
  18. By: Calomiris,Charles W.; Larrain,Mauricio; Schmukler,Sergio L.; Williams,Tomas
    Abstract: Emerging market corporations have significantly increased their borrowing in international markets since 2008. This paper shows that this increase was driven by large-denomination bond issuances, most of them with face value of US$500 million. Large issuances are eligible for inclusion in international market indexes, which attract institutional investors. Emerging market firms were able to cut their cost of funds by roughly 100 basis points by issuing large-denomination bonds. Firms face a tradeoff: issue large, index-eligible bonds to borrow at a lower cost (about 100 basis points) but pay the expense of hoarding cash. Because of the"size yield discount,"many companies issued index-eligible bonds, increasing their cash holdings. The willingness to issue large bonds and hoard cash was greater for firms in countries with high carry trade opportunities. These post-2008 behaviors reflected a search for yield by institutional investors into higher-risk securities and are not apparent in developed economies.
    Keywords: International Trade and Trade Rules,Mutual Funds,Capital Flows,Capital Markets and Capital Flows,Non Bank Financial Institutions,Public Sector Economics,Public Finance Decentralization and Poverty Reduction,Commodity Risk Management
    Date: 2019–06–17

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