nep-rmg New Economics Papers
on Risk Management
Issue of 2016‒07‒09
fourteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Comments on the BCBS proposal for a New Standardized Approach for Operational Risk By Giulio Mignola; Roberto Ugoccioni; Eric Cope
  2. Derivative use and its impact on Systematic Risk of Indian Banks: Evidence using Tobit model By Sinha, Pankaj; Sharma, Sakshi
  3. Dynamic D-Vine copula model with applications to Value-at-Risk (VaR) By Tófoli, Paula Virgínia; Ziegelmann, Flávio Augusto; Silva Filho, Osvaldo Candido; Pereira, Pedro L. Valls
  4. Dynamic Corporate Risk Management: Motivations and Real Implications By Dionne, Georges; Gueyie, Jean-Pierre; Mnasri, Mohamed
  5. Stock markets reconstruction via entropy maximization driven by fitness and density By Tiziano Squartini; Guido Caldarelli; Giulio Cimini
  6. Mixed-frequency multivariate GARCH By Geert Dhaene; Jianbin Wu
  7. Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone By Monica Billio; Lorenzo Frattarolo; Hayette Gatfaoui; Philippe de Peretti
  8. A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities By Seyed Amir Hejazi; Kenneth R. Jackson
  9. Dodd-Frank: Washington, We Have a Problem By lopez, claude; Saeidinezhad, Elham
  10. An econometric analysis of ETF and ETF futures in financial and energy markets using generated regressors By Chia-Lin Chang; Michael McAleer; Chien-Hsun Wang
  11. The Asset Management Industry and Systemic Risk: Is There a Connection? By Lopez, Claude; Markwardt, Donald; Savard, Keith
  12. The risk-return tradeoff in international stock markets: one-step multivariate GARCH-M estimation with many assets By Geert Dhaene; Piet Sercu; Jianbin Wu
  13. Are Correlations Constant? Empirical and Theoretical Results on Popular Correlation Models in Finance By Adams, Zeno; Fuess, Roland; Glueck, Thorsten
  14. Prudential regulation in an artificial banking system By Curto, José Dias; Quinaz, Pedro Miguel Mateus Dias

  1. By: Giulio Mignola; Roberto Ugoccioni; Eric Cope
    Abstract: On March 4th 2016 the Basel Committee on Banking Supervision published a consultative document where a new methodology, called the Standardized Measurement Approach (SMA), is introduced for computing Operational Risk regulatory capital for banks. In this note, the behavior of the SMA is studied under a variety of hypothetical and realistic conditions, showing that the simplicity of the new approach is very costly on other aspects: we find that the SMA does not respond appropriately to changes in the risk profile of a bank, nor is it capable of differentiating among the range of possible risk profiles across banks; that SMA capital results generally appear to be more variable across banks than the previous AMA option of fitting the loss data; that the SMA can result in banks over- or under-insuring against operational risks relative to previous AMA standards. Finally, we argue that the SMA is not only retrograde in terms of its capability to measure risk, but perhaps more importantly, it fails to create any link between management actions and capital requirement.
    Date: 2016–07
  2. By: Sinha, Pankaj; Sharma, Sakshi
    Abstract: The use of derivatives by Indian banks has increased in the recent past. Derivatives are complicated assets, and many characteristics of these relatively new assets have been evolving day by day. The fast growth of bank involvement in derivative markets has raised concerns about the potential hazards of this activity. On the flipside, certain characteristics of derivatives make them highly useful in hedging risks. It is a well-known fact that derivative activity is concentrated among relatively larger banks. However, very little is known about other factors that govern the decisions regarding derivative usage by banks. In theory, an exposure of bank to interest rate risk should impact the derivative transaction volume. Furthermore, the use of derivative will vary according to bank capital, bank size and its use of alternatives to hedge. The paper uses the financial characteristics of banks those trade in derivatives and banks those do not trade in derivatives , by using bank level data for 46 Indian Scheduled Commercial banks for the year 2013. A Tobit Model is used to analyse censored data on notional amount of derivative use and its relationship with various financial characteristics of banks. These financial characteristics include bank size, capital adequacy, exposure to credit and interest rate risk, profitability and liquidity. We find that derivative user banks have higher liquidity, lower interest margins, are larger. Additionally, there is evidence in support of the “assurance” capital hypothesis highlighting the use of derivatives by large well capitalised banks. The larger banks exposed with lower interest margins and higher capital ratios are more likely to use derivatives to hedge their interest rate risk. Using an augmented market model, we further calculate systematic risk exposure of banks for the year 2013 and test whether usage of derivatives and interest rate derivatives contribute towards an aggravation in the systematic risk exposure of banks. The results point towards a significant decrease in exchange rate riskiness using derivatives as well as a significant decline in the long term interest risk as well. It implies and motivates banks to indulge in derivative trading, as the systemic risk do not seem to be potentially aggravated by using them. Nevertheless, derivative activity is concentrated among well capitalised banks which can safely manage risks.
    Keywords: derivatives , systematic risk, hedging, exchange rate exposure, interest rate risk
    JEL: G1 G2 G28
    Date: 2016–03–08
  3. By: Tófoli, Paula Virgínia; Ziegelmann, Flávio Augusto; Silva Filho, Osvaldo Candido; Pereira, Pedro L. Valls
    Abstract: Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.
    Date: 2016–06–22
  4. By: Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management); Gueyie, Jean-Pierre (HEC Montreal, Canada Research Chair in Risk Management); Mnasri, Mohamed (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We investigate the dynamics of corporate hedging programs by US oil producers and examine the effects of hedging maturity choice on firm value. We find evidence of a concave relation between hedging maturity and the likelihood of financial distress and oil spot prices. We further investigate the motivations of the early termination of outstanding hedging contracts. We evaluate the causal effects of hedging and show that hedging maturity increases firm value. Using the essential heterogeneity approach, we find that firms value is more strongly related to short-term hedging maturities. This is the first time this approach is applied in corporate finance.
    Keywords: Hedging maturity; early termination of contracts; firm value; heterogeneous treatment effects; essential heterogeneity models; oil industry
    JEL: D80 G32
    Date: 2016–07–06
  5. By: Tiziano Squartini; Guido Caldarelli; Giulio Cimini
    Abstract: The spreading of financial distress in capital markets and the resulting systemic risk strongly depend on the detailed structure of financial interconnections. Yet, while financial institutions have to disclose their aggregated balance sheet data, the information on single positions is often unavailable due to privacy issues. The resulting challenge is that of using the aggregate information to statistically reconstruct financial networks and correctly predict their higher-order properties. However, standard approaches generate unrealistically dense networks, which severely underestimate systemic risk. Moreover, reconstruction techniques are generally cast for networks of bilateral exposures between financial institutions (such as the interbank market), whereas, the network of their investment portfolios (i.e., the stock market) has received much less attention. Here we develop an improved reconstruction method, based on statistical mechanics concepts and tailored for bipartite market networks. Technically, our approach consists in the preliminary estimation of connection probabilities by maximum-entropy inference driven by entities capitalizations and link density, followed by a density-corrected gravity model to assign position weights. Our method is successfully tested on NASDAQ, NYSE and AMEX filing data, by correctly reproducing the network topology and providing reliable estimates of systemic risk over the market.
    Date: 2016–06
  6. By: Geert Dhaene; Jianbin Wu
    Abstract: We introduce and evaluate mixed-frequency multivariate GARCH models for forecasting low-frequency (weekly or monthly) multivariate volatility based on high-frequency intra-day returns (at five-minute intervals) and on the overnight returns. The low-frequency conditional volatility matrix is modelled as a weighted sum of an intra-day and an overnight component, driven by the intra-day and the overnight returns, respectively. The components are specified as multivariate GARCH (1,1) models of the BEKK type, adapted to the mixed-frequency data setting. For the intra-day component, the squared high-frequency returns enter the GARCH model through a parametrically specified mixed-data sampling (MIDAS) weight function or through the sum of the intra-day realized volatilities. For the overnight component, the squared overnight returns enter the model with equal weights. Alternatively, the low-frequency conditional volatility matrix may be modelled as a single-component BEKK-GARCH model where the overnight returns and the high-frequency returns enter through the weekly realized volatility (defined as the unweighted sum of squares of overnight and high-frequency returns), or where the overnight returns are simply ignored. All model variants may further be extended by allowing for a non-parametrically estimated slowly-varying long-run volatility matrix. The proposed models are evaluated using five-minute and overnight return data on four DJIA stocks (AXP, GE, HD, and IBM) from January 1988 to November 2014. The focus is on forecasting weekly volatilities (defined as the low frequency). The mixed-frequency GARCH models are found to systematically dominate the low-frequency GARCH model in terms of in-sample fit and out-of-sample forecasting accuracy. They also exhibit much lower low-frequency volatility persistence than the low-frequency GARCH model. Among the mixed-frequency models, the low-frequency persistence estimates decrease as the data frequency increases from daily to five-minute frequency, and as overnight returns are included. That is, ignoring the available high-frequency information leads to spuriously high volatility persistence. Among the other findings are that the single-component model variants perform worse than the two-component variants; that the overnight volatility component exhibits more persistence than the intra-day component; and that MIDAS weighting performs better than not weighting at all (i.e., than realized volatility).
    Date: 2016–06
  7. By: Monica Billio (University Ca' Foscari of Venice - Department of Economics); Lorenzo Frattarolo (University Ca' Foscari of Venice - Department of Economics); Hayette Gatfaoui (IESEG School of Management (LEM) et Centre d'Economie de la Sorbonne); Philippe de Peretti (Centre d'Economie de la Sorbonne)
    Abstract: In this paper, we analyze the dynamic relationships between ten stock exchanges of the euro zone using Granger causal networks. Using returns for which we allow the variance to follow a Markov-Switching GARCH or a Changing-Point GARCH, we first show that over different periods, the topology of the network is highly unstable. In particular, over very recent years, dynamic relationships vanish. Then, expanding on this idea, we analyze patterns of information transmission. Using rolling windows to analyze the topologies of the network in terms of clustering, we show that the nodes' state changes continually, and that the system exhibits a high degree of flickering in information transmission. During periods of flickering, the system also exhibits desynchronization in the information transmission process. These periods do precede tipping points or phase transitions on the market, especially before the global financial crisis, and can thus be used as early warnings of phase transitions. To our knowledge, this is the first time that flickering clusters are identified on financial markets, and that flickering is related to phase transitions
    Keywords: Causal Network; Topology; Clustering; Flickering; Desynchronisation; Phase transitions
    JEL: G17 C18 C52
    Date: 2016–05
  8. By: Seyed Amir Hejazi; Kenneth R. Jackson
    Abstract: Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.
    Date: 2016–06
  9. By: lopez, claude; Saeidinezhad, Elham
    Abstract: The Dodd-Frank Act was the most far-reaching financial regulatory reform in the U.S. since the nation emerged from the Great Depression in the 1930s. The act aims to limit systemic risk, allow for the safe resolution of the largest intermediaries, submit risky nonbanks to greater scrutiny, and reform derivative trading. The public debate is often highly politicized and opinionated when it comes to Dodd-Frank. With that in mind, this paper seeks to assess Dodd-Frank implementation with respect to its initial goal of building “a safer, more stable financial system,” where proprietary trading and the business of banking are separated, and where taxpayers and small business will not have to bail out failing large financial firms.” To make the assessment, the paper first establishes a timeline summarizing the Dodd-Frank final-rule milestones and then compares their implementation to the initial goals.
    Keywords: Dodd-Frank, macroprudential policy, systemic risk, regulation
    JEL: E6 G0 G1 G2
    Date: 2016–06
  10. By: Chia-Lin Chang (Department of Applied Economics Department of Finance National Chung Hsing University Taichung, Taiwan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain.); Chien-Hsun Wang (Institute of Statistics National Tsing Hua University, Taiwan.)
    Abstract: It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), which is a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” may be used to construct both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the covolatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and also subdivided into three subset time periods. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.
    Keywords: Exchange traded funds, Financial and energy sectors, Co-volatility spillovers, Spot and futures prices, Generated regressors, Diagonal BEKK.
    JEL: C58 G13 G23 G31 Q41
    Date: 2016–06
  11. By: Lopez, Claude; Markwardt, Donald; Savard, Keith
    Abstract: In the aftermath of the financial crisis, new legislation and regulation have pressured banks (and insurances) to reduce their size, leverage, and riskier lines of business in order to avoid another too-big-to-fail debacle. Nonbank financial intermediaries have naturally taken up some of that slack and, not surprisingly, regulatory scrutiny has turned toward these intermediaries to evaluate whether they could pose similar risks to financial stability that banks did pre-crisis. Owing to their stunning growth in the past decade, focus among nonbank intermediaries is now centering on asset managers, which include firms offering mutual funds, exchange-traded funds, hedge funds and private equity funds. This report explores whether there is a demonstrable link between the asset management industry and systemic risk. Key points: Systemic risk is distinct from run-of-the-mill financial or operational risk, an important difference when determining whether the sector poses a risk to the broader financial system with the potential for negative spillovers into the real economy. Because asset managers do not take on nearly the same level of leverage and do not guarantee balances on customer accounts as banks do with deposits, it is unlikely that the industry is the epicenter of (or creating) systemic risk in the financial system. Theoretically, however, they hold the potential transmit or amplify systemic risk in the system based on unique risk factors such as herding and liquidity mismatches. One major regulatory concern is the mismatch between asset management firms offering investors highly liquid investment terms for funds investing in highly illiquid assets, which could create fire sale scenarios that negatively impact financial markets. A close look at the role of high-yield debt markets suggests that major disruptions to the sector’s funding environment could have a significant impact on the real economy. However, even during periods of acute investor outflows, high-yield mutual funds have managed liquidity risk effectively to-date, and high-yield ETFs have actually been a supplemental liquidity source for institutional investors. In a post-crisis world, regulators have as much power (if not more) than financial firms’ shareholders. Considerations must include: i. The dynamic relationship between financial regulation and financial activity ii. The necessity of proper fiscal and monetary policies to complement prudential oversight iii. The reality that financial markets are connected globally .
    Keywords: systemic risk, asset managers, macroprudential policy, financial stability
    JEL: E6 F4 G1 G2
    Date: 2016–06
  12. By: Geert Dhaene; Piet Sercu; Jianbin Wu
    Abstract: We study international asset pricing in a large-dimensional multivariate GARCH-in-mean framework. We examine different estimation methods and find that the two-step estimation method proposed by Bali and Engle (2010) tends to underestimate the risk-return coefficient and the corresponding standard error. We also show that the estimate is improved by one-step estimation and by increasing the cross-sectional dimension. Using stock index returns for up to 24 countries and 4 major currencies in the period 2001-2015, one-step estimation gives a market risk-return coefficient of around 6. The estimate is robust to variations in model specification, data frequency, and the number of stock markets considered.
    Date: 2016–06
  13. By: Adams, Zeno; Fuess, Roland; Glueck, Thorsten
    Abstract: Multivariate GARCH models have been designed as an extension of their univariate counterparts. Such a view is appealing from a modeling perspective but imposes correlation dynamics that are similar to time-varying volatility. In this paper, we argue that correlations are quite different in nature. We demonstrate that the highly unstable and erratic behavior that is typically observed for the correlation among financial assets is to a large extent a statistical artefact. We provide evidence that spurious correlation dynamics occur in response to financial events that are sufficiently large to cause a structural break in the time-series of correlations. A measure for the autocovariance structure of conditional correlations allows us to formally demonstrate that the volatility and the persistence of daily correlations are not primarily driven by financial news but by the level of the underlying true correlation. Our results indicate that a rolling-window sample correlation is often a better choice for empirical applications in finance.
    Keywords: Change-point tests; correlation breaks; dynamic conditional correlation (DCC); multivariate GARCH models; spurious conditional correlation
    JEL: C12 C52 G01 G11
    Date: 2016–06
  14. By: Curto, José Dias; Quinaz, Pedro Miguel Mateus Dias
    Abstract: This study constitutes an exploratory analysis of the economic role of banks under different prudential frameworks. It considers an agent-based computational model populated by consumers, firms, banks and a central bank whose out-of-equilibrium interactions replicate the conjunct dynamics of a banking system, a financial market and the real economy. A calibrated version of the model is shown to provide an intelligible account of several recurrent economic phenomena and it can be a privileged ground for policy analysis. The authors' investigation provides a relevant methodological contribution to the field of banking research and sheds new light into the role of banks and their prudential regulation. Specifically, the results suggest that banks are key economic agents. Through their financial intermediation activity, credit institutions facilitate investment and promote growth.
    Keywords: agent-based computational model,financial intermediation,prudential policy,bank regulation
    JEL: C63 G28
    Date: 2016

This nep-rmg issue is ©2016 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.