nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒11‒01
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Assessing financial distress dependencies in OTC markets: a new approach by Trade Repositories data By Michele Bonollo; Irene Crimaldi; Andrea Flori; Laura Gianfagna; Fabio Pammolli
  2. Law invariant risk measures and information divergences By Daniel Lacker
  3. Computer-Suported Risk Identification for the Holistic Management of Risks By Jochen L. Leidner
  4. TIME-VARYING RISK PREMIUM IN LARGE CROSS-SECTIONAL EQUITY DATASETS By Ossola, Elisa; Gagilardini, Patrick; Scaillet, Olivier
  5. Bitcoin, Gold and the Dollar – a GARCH Volatility Analysis By Anne Haubo Dyhrberg
  6. Hedging Capabilities of Bitcoin. Is it the virtual gold? By Anne Haubo Dyhrberg
  7. Hedge fund predictability and optimal asset allocation By Ekaterini Panopoulou; Theologos Pantelidis; Spyridon Vrontos
  8. "Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets By Li Lin; Didier Sornette
  9. Managing Portfolio Risk in Strategic Technology Management: Evidence from a Panel Data Set of the World’s Largest R&D Performers By Neuhäusler , Peter; Schubert, Torben; Frietsch , Rainer; Blind , Knut
  10. On the exposure of insurance companies to sovereign risk: Portfolio investments and market forces By Düll, Robert; König, Felix; Ohls, Jana

  1. By: Michele Bonollo (Iason Ltd and IMT Institute for Advanced Studies Lucca); Irene Crimaldi (IMT Institute for Advanced Studies Lucca); Andrea Flori (IMT Institute for Advanced Studies Lucca); Laura Gianfagna (IMT Institute for Advanced Studies Lucca); Fabio Pammolli (IMT Institute for Advanced Studies Lucca)
    Abstract: After the recent financial crisis, it is undoubtedly recognized the importance of assessing not only the risk of distress for a single \financial entity", but also the distress dependencies between the different \entities", where by \entities" we mean in a broad sense any relevant cluster of products, risk factors, counterparties. In this paper, we focus on the Interest Rate Swap (IRS) segment as a significant fraction of the OTC market. We define a distress indicator by combining some distress drivers, such as averaged volumes, liquidity, volatility and bid-ask proxies. Hence, we analyse the distress dependencies among sub-markets identified by the segmentation of the IRS market according to contractual and financial features. We try to combine in an innovative way some new ingredients, namely the more granular data on OTC derivatives available from the trade repositories along with the classical JPoD approach introduced in the recent years by the IMF for studying the distress interdependence structure among financial institutions. The proposed technique seems to be quite promising. Indeed, the results are quite close to the practical intuition. At the best of our knowledge, this work is the first empirical study based on trade repositories' data for assessing systemic risk.
    Keywords: Financial distress interdependence, Joint probability of distress, Interest rate swap, Systemic risk, Trade repositories
    JEL: G01 G18 G19
    Date: 2015–10
  2. By: Daniel Lacker
    Abstract: A one-to-one correspondence is drawn between law invariant risk measures and divergences, which we define as functionals of pairs of probability measures on arbitrary standard Borel spaces satisfying a few natural properties. Divergences include many classical information divergence measures, such as relative entropy and $f$-divergences. Several properties of divergence and their duality with law invariant risk measures are developed, most notably relating their chain rules or additivity properties with certain notions of time consistency for dynamic law invariant risk measures known as acceptance and rejection consistency. These properties are linked also to a peculiar property of the acceptance sets on the level of distributions, analogous to results of Weber on weak acceptance and rejection consistency. Finally, the examples of shortfall risk measures and optimized certainty equivalents are discussed in some detail, and it is shown that the relative entropy is essentially the only divergence satisfying the chain rule.
    Date: 2015–10
  3. By: Jochen L. Leidner
    Abstract: Risk is part of the fabric of every business; surprisingly, there is little work on establishing best practices for systematic, repeatable risk identification, arguably the first step of any risk management process. In this paper, we present a proposal that constitutes a more holistic risk management approach, a methodology for computer-supported risk identification is proposed that may lead to more consistent (objective, repeatable) risk analysis.
    Date: 2015–10
  4. By: Ossola, Elisa; Gagilardini, Patrick; Scaillet, Olivier
    Abstract: We develop an econometric methodology to infer the path of risk premia from a large unbalanced panel of individual stock returns. We estimate the time-varying risk premia implied by conditional linear asset pricing models where the conditioning includes both instruments common to all assets and asset specific instruments. The estimator uses simple weighted two-pass cross-sectional regressions, and we show its consistency and asymptotic normality under increasing cross-sectional and time series dimensions. We address consistent estimation of the asymptotic variance by hard thresholding, and testing for asset pricing restrictions induced by the no-arbitrage assumption. We derive the restrictions given by a continuum of assets in a multi-period economy under an approximate factor structure robust to asset repackaging. The empirical analysis on returns for about ten thousands US stocks from July 1964 to December 2009 shows that risk premia are large and volatile in crisis periods. They exhibit large positive and negative strays from time-invariant estimates, follow the macroeconomic cycles, and do not match risk premia estimates on standard sets of portfolios. The asset pricing restrictions are rejected for a conditional four-factor model capturing market, size, value and momentum effects.
    JEL: C12 C13 C23 C51 C52 G12
    Date: 2015
  5. By: Anne Haubo Dyhrberg
    Abstract: This paper explores the financial asset capabilities of bitcoin using GARCH models. The initial model showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. The asymmetric GARCH showed that bitcoin may be useful in risk management and ideal for risk averse investors in anticipation of negative shocks to the market. Overall bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar on a scale from pure medium of exchange advantages to pure store of value advantages.
    Keywords: Bitcoin; GARCH; Volatility
    JEL: G15 Q02
    Date: 2015–09
  6. By: Anne Haubo Dyhrberg
    Abstract: This paper sets out to explore the hedging capabilities of bitcoin by applying the asymmetric GARCH methodology used in investigation of gold. The results show that bitcoin can clearly be used as a hedge against stocks in the Financial Times Stock Exchange Index. Additionally bitcoin can be used as a hedge against the American dollar in the short-term. Bitcoin thereby possess some of the same hedging abilities as gold and can be included in the variety of tools available to market analysts to hedge market specific risk.
    Keywords: Bitcoin; Risk Management; Gold; Hedging
    JEL: G1 G11
    Date: 2015–10
  7. By: Ekaterini Panopoulou (University of Kent); Theologos Pantelidis (University of Macedonia); Spyridon Vrontos (University of Essex)
    Abstract: The degree of both return and volatility hedge fund predictability is revealed using a regime switching framework. Optimal combinations of regime switching model forecasts allow us to capture the stylized facts of hedge fund returns and construct superior hedge fund return forecasts in the presence of parameter instability and model uncertainty. Our dataset consists of individual hedge fund data from the Barclays hedge fund database for the period January 1994 to December 2013. Our extensive set of predictors contains the Fung and Hsieh factors, factors related to style investing and to investment policies, macro related / business indicators variables and market-oriented factors. The economic value of the proposed predictability models is investigated by studying its effects on asset allocation and active portfolio management.
    Keywords: Hedge fund predictability; regime switching model; asset allocation
    JEL: C53 G11
  8. By: Li Lin; Didier Sornette
    Abstract: We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion (GBM) and a bubble regime represented by the stochastic super-exponential Sornette-Andersen (2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset $i$ onto another asset $j$, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005-2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity (NSII) variable as the difference between the transfer entropies from $i$ to $j$ and from $j$ to $i$, which is used in a series of rank ordered regressions to predict the maximum loss (\%{MaxLoss}) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is a clear prediction skill obtained by using the transfer entropy involving industrial sectors to explain the \%{MaxLoss} of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.
    Date: 2015–10
  9. By: Neuhäusler , Peter (Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe; Berlin University of Technology); Schubert, Torben (Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe; CIRCLE, Lund University); Frietsch , Rainer (Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe); Blind , Knut (Berlin University of Technology; Fraunhofer Institute for Open Communication Systems FOKUS, Berlin; Erasmus University Rotterdam, Rotterdam School of Management)
    Abstract: In this article we analyze the impact of firms’ technology bases on their financial performance. By taking a strategic perspective of technology, we argue that it is not sufficient to analyze only the size or novelty/quality of the technology base as technology bases can best be understood as portfolios of individual technologies. In such a framework, risk consideration should be taken into account. More specifically, we argue that increasing technological breadth can serve as a hedge against the inherent uncertainties of developing and commercializing technology, in particular when the technology base is very large or novel. We also propose that technology has higher impacts on financial performance for firms with broader technology portfolios. A similar argument proposes that technological breadth can offset the increased risks of addressing foreign markets. We test our hypotheses using an international panel dataset of large R&D-performing firms. Our results suggest that broad technology portfolios can indeed serve as a hedge against technological and commercialization risks.
    Keywords: patents; financial performance; firms; technology base
    JEL: O32 O34
    Date: 2015–10–23
  10. By: Düll, Robert; König, Felix; Ohls, Jana
    Abstract: A sovereign debt crisis can have significant knock-on effects in the financial markets and put financial stability at risk. This paper focuses on the transmission of sovereign risk to insurance companies as some of the largest institutional investors in the sovereign bond market. We use a firm level panel dataset that covers large insurance companies, banks and non-financial firms from nine countries in the time period January 1st 2008 to May 1st 2013. We find significant and robust transmission effects from sovereign risk to domestic insurers. The impact on insurers is larger than for non-financial firms and slightly smaller than for banks. We find that systemically important insurers were more closely linked to the domestic sovereign. Based on European data, we show that risks in sovereign bond portfolios are an important driver of insurer risk, which is not reflected in current insurance regulation (incl. upcoming Solvency II in Europe).
    Keywords: insurance,sovereign risk,sovereign bond portfolio
    JEL: G22 G28 G15
    Date: 2015

This nep-rmg issue is ©2015 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.