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on Risk Management |
| By: | Karlis, Alexandros (Department of Physics University of Warwick); Galanis, Giorgos (Department of Economics University of Warwick); Terovitis, Spyridon (Department of Economics University of Warwick); Turner, Matthew |
| Abstract: | This paper focuses on the use of interest rates as a tool for hedging against the default risk of heterogeneous hedge funds (HFs) in a leveraged market. We assume that the banks study the HFs survival statistics in order to compute default risk and hence the correct interest rate. The emergent non-trivial (heavy-tailed) statistics observed on the aggregate level, prevents the accurate estimation of risk in a leveraged market with heterogeneous agents. Moreover, we show that heterogeneity leads to the clustering of default events and constitutes thus a source of systemic risk. |
| Keywords: | survival statistics ; interest rate ; leverage ; financial fragility.creation-date: 2015 |
| JEL: | G23 G24 G32 G33 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:warwec:1084&r=rmg |
| By: | Richard Harris; Linh Nguyen; Evarist Stoja |
| Abstract: | We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the market by combining the framework of Bali, Demirtas, and Levy (2009) with a Markov switching mechanism. We show that the risk-return relationship identified by Bali, Demirtas, and Levy (2009) is highly significant in the low volatility state but disappears during periods of market turbulence. This is puzzling since it is during such periods that downside risk should be most prominent. We show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility feedback effects arising from increased persistence in volatility. To better filter out these effects, we propose a simple modification that yields a positive tail risk-return relationship under all states of market volatility. |
| Keywords: | Downside risk; Markov switching; Value-at-Risk; Leverage effect; Volatility feedback effect. |
| JEL: | C13 C14 C58 G10 G11 G12 |
| Date: | 2015–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:bri:accfin:15/2&r=rmg |
| By: | Matt V. Leduc; Sebastian Poledna; Stefan Thurner |
| Abstract: | We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way that makes it more resilient to insolvency cascades. A regulator can use information about the topology of the interbank network to devise a systemic insurance surcharge that is added to the CDS spread. CDS contracts are thus effectively penalized according to how much they contribute to increasing systemic risk. CDS contracts that decrease systemic risk remain untaxed. We simulate this regulated CDS market using an agent-based model (CRISIS macro-financial model) and we demonstrate that it leads to an interbank system that is more resilient to insolvency cascades. |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.02156&r=rmg |
| By: | Chris Bardgett (University of Zurich and Swiss Finance Institute (SFI)); Elise Gourier (Queen Mary University of London); Markus Leippold (University of Zurich and Swiss Finance Institute (SFI)) |
| Abstract: | This paper studies the information content of the S&P 500 and VIX markets on the volatility of the S&P 500 returns. We estimate a flexible affine model based on a joint time series of underlying indexes and option prices on both markets. An extensive model specification analysis reveals that jumps and a stochastic level of reversion for the variance help reproduce risk-neutral distributions as well as the term structure of volatility smiles and of variance risk premia. We find that the S&P 500 and VIX derivatives prices are consistent in times of market calm but contain conflicting information on the variance during market distress. |
| Keywords: | S&P 500 and VIX joint modeling, Volatility dynamics, Particle filter, Variance risk premium |
| JEL: | G12 G13 C58 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp780&r=rmg |
| By: | Ilya Khankov (National Research University Higher School of Economics, Moscow); Henry Penikas (National Research University Higher School of Economics, Moscow) |
| Abstract: | Research is devoted to examination of the classifier, based on copula discriminant analysis (CODA). Performance of the classification of this algorithm was assessed. On samples, modelled with some typical features of corporate default data, sensitivity of the classifier was tested, to sample size, to default rate and to different patterns of variables’ interdependence. Alternative copula families’ selection method is proposed based on certain performance metric optimization. Difference in classification performance of different algorithms are investigated. On real data of Russian corporate defaults, CODA classifier was built. It was supported by single factor analysis, based on discriminant analysis too. Final model demonstrates better classification performance than Linear Discriminant Analysis and Random Forest algorithm, and is comparable to Quadratic Discriminant Analysis. Another experiment was set on data of Russian banks. Single factor analysis was assessed via standard procedure. CODA performance appeared to be lower than of Random Forest here, it was similar to QDA |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0113&r=rmg |
| By: | Cathy Yi-Hsuan Chen; Thomas C. Chiang; Wolfgang Karl Härdle; |
| Abstract: | This paper This paper This paper This paper presents presents presents a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrat a fractionally cointegrat a fractionally cointegrata fractionally cointegrata fractionally cointegrata fractionally cointegrat a fractionally cointegrata fractionally cointegrat a fractionally cointegrated vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression ed vector autoregression (FCVAR) (FCVAR) (FCVAR) (FCVAR) model to examine to examine to examine to examine to examine to examine to examine various relations various relations various relations various relations various relations between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside riskbetween stock returns and downside risk between stock returns and downside risk between stock returns and downside risk between stock returns and downside risk . Evidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanceEvidence from major advanceEvidence from major advance Evidence from major advance Evidence from major advanced markets markets markets markets markets supports the supports the notion that notion that notion that downside risk measured by measured by measured by measured by measured by measured by measured by value value value-at -risk ( risk (VaRVaRVaR) has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information has significant information content content that reflects that reflects that reflects that reflects that reflects lagged long lagged long lagged longlagged long lagged long -run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and run variance and higher momentshigher moments higher moments higher moments higher moments higher momentshigher moments of risk for for predict redict ing stock returns. stock returns. stock returns. stock returns. The e The e vidence vidence vidence supports the positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesispositive tradeoff hypothesis positive tradeoff hypothesis positive tradeoff hypothesis and and the leverage effect leverage effect leverage effectleverage effectleverage effect leverage effectleverage effectleverage effectleverage effectleverage effect in the long in the long in the long run and and for for some markets in the short run. some markets in the short run. some markets in the short run. some markets in the short run. some markets in the short run. some markets in the short run. some markets in the short run. some markets in the short run.some markets in the short run. We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, We find that US downside risk accounts for 54.36% of price discovery, whereas the whereas the whereas the whereas the own effect from own effect from own effect from own effect from own effect from own effect from own effect from the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes the country itself contributes only only only 27.06%. 27.06%. |
| Keywords: | Downside risk, Value-at-Risk, long memory, fractional integration, Risk-return |
| JEL: | G11 G12 G15 C24 F30 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2016-001&r=rmg |
| By: | Isabel Argimón (Banco de España); Ángel Estrada (Banco de España); Michel Dietsch (ACPR-Banque de France) |
| Abstract: | European banks hold 10% of their total assets in portfolios that give rise to unrealised gains and losses which under Basel III will no longer be allowed to be removed from banks’ regulatory capital. Using a sample of European banks, and taking advantage of the different treatment afforded, under Basel II, to such gains and losses among jurisdictions and instruments and over time, we find evidence that: a) the inclusion of unrealised gains and losses in capital ratios increases their volatility; b) the partial inclusion of unrealised gains and total inclusion of losses on fixed-income securities in regulatory capital, compared with the complete exclusion of both (neutralisation), reduces the volume of securities categorised as Available For Sale (AFS), thus potentially affecting liquidity management and demand for bonds (most of which are currently government bonds); and c) the higher the partial inclusion of gains from debt instruments, the lower the holdings of such instruments in the AFS category and the higher the regulatory Tier 1 capital ratio, thus affecting banks’ capital buffer strategy. We do not find evidence that the removal of neutralisation would impact capital ratios. |
| Keywords: | prudential regulation, regulatory capital, fair value accounting, prudential filters |
| JEL: | G21 M41 |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:1538&r=rmg |
| By: | Lev B Klebanov |
| Abstract: | Failure of the main argument for the use of heavy tailed distribution in Finance is given. More precisely, one cannot observe so many outliers for Cauchy or for symmetric stable distributions as we have in reality. keywords:outliers; financial indexes; heavy tails; Cauchy distribution; stable distributions |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1601.00566&r=rmg |
| By: | Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw) |
| Abstract: | This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (untransformed data, PCA analysis and factor analysis). Additionally an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, GINI coefficient analysis and KROC criterion based on the ROC curve. Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability. |
| Keywords: | prediction, state of turbulence, regime switching, risk management, risk measure, market risk |
| JEL: | C53 C58 G17 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:war:wpaper:2016-01&r=rmg |