nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒07‒16
fourteen papers chosen by

  1. Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution By Chao Wang; Qian Chen; Richard Gerlach
  2. The impact of Solvency II regulations on life insurers’ investment behaviour By Douglas, Graeme; Noss, Joseph; Vause, Nicholas
  3. Portfolio Risk Assessment using Copula Models By Mikhail Semenov; Daulet Smagulov
  4. Systemic Financial Sector and Sovereign Risks By Xisong Jin; Francisco Nadal De Simone
  5. How UK Banks are Changing Their Corporate Culture & Practice Following the Financial Crisis of 2007-08 By Ian W. Jones; Michael G. Pollitt
  6. Trading Activities at Systemically Important Banks, Part 2 : What Happened during Recent Risk Events? By Diana A. Iercosan; Ashish Kumbhat; Michael Ng; Jason J. Wu
  7. A Model of Interbank Flows, Borrowing, and Investing By Aditya Maheshwari; Andrey Sarantsev
  8. On the Predictability of Stock Market Bubbles: Evidence from LPPLS ConfidenceTM Multi-scale Indicators By Riza Demirer; Guilherme Demos; Rangan Gupta; Didier Sornette
  9. Asymptotics for Greeks under the constant elasticity of variance model By Oleg L. Kritski; Vladimir F. Zalmezh
  10. Decumulation, Sequencing Risk and the Safe Withdrawal Rate: Why the 4% Withdrawal Rule leaves Money on the Table By Andrew Clare; James Seaton; Peter N. Smith; Stephen Thomas
  11. Dynamic Quantile Function Models By Wilson Ye Chen; Gareth W. Peters; Richard H. Gerlach; Scott A. Sisson
  12. Hedge Fund Returns under Crisis Scenarios: A Holistic Approach By Stoforos, Chrysostomos; Degiannakis, Stavros; Palaskas, Theodosios
  13. The one-trading-day-ahead forecast errors of intra-day realized volatility By Degiannakis, Stavros
  14. Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov Model By Massimo Caccia; Bruno R\'emillard

  1. By: Chao Wang (Discipline of Business Analytics, The University of Sydney); Qian Chen (HSBC Business School, Peking University); Richard Gerlach (Discipline of Business Analytics, The University of Sydney)
    Abstract: The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. An adaptive Bayesian Markov Chain Monte Carlo method is developed and employed for estimation and forecasting, whose properties are assessed and compared with maximum likelihood, via a simulation study. Compared to a range of well-known parametric GARCH, GARCH with two-sided Weibull distribution and realized GARCH models, tail risk forecasting results across 7 market index return series and 2 individual assets clearly favor the realized GARCH models incorporating two-sided Weibull distribution, especially models employing the sub-sampled realized variance and sub-sampled realized range, over a six year period that includes the global financial crisis.
    Date: 2017–07
  2. By: Douglas, Graeme (Bank of England); Noss, Joseph (Bank of England); Vause, Nicholas (Bank of England)
    Abstract: This paper provides a means of estimating how ‘Solvency II’ regulations — introduced in the European Union in January 2016 — might affect UK life insurers’ incentives to hold different types of financial assets, and how these asset holdings are likely to vary in the face of hypothetical changes to market prices. To do so, it sets out a structural model of firms’ equity to assess their investment behaviour under different regulatory regimes. It finds that, while Solvency II may partly protect insurers’ solvency positions from falls in risky asset prices, the new regulations might encourage certain types of UK life insurers to de-risk — that is, move to holding safe assets in place of risky — following falls in risk-free interest rates. This behaviour is driven by changes in the so-called ‘risk margin’, which, under its current design within the Solvency II framework, reduces insurers’ solvency positions following falls in risk-free interest rates, thereby encouraging them to sell risky assets to reduce their probability of regulatory insolvency. The model also suggests that, once Solvency II is fully implemented by 2032, UK life insurers may have markedly reduced their holdings of long-term, risky assets. In the model, this behaviour is also driven by the risk margin, which, by increasing the volatility of insurers’ solvency, encourages them to de-risk to reduce the variance of their asset portfolios.
    Keywords: Insurance; procyclicality; regulation; Solvency II; liquidity
    JEL: G11 G12 G18 G22 G23
    Date: 2017–07–07
  3. By: Mikhail Semenov; Daulet Smagulov
    Abstract: In the paper, we use and investigate copulas models to represent multivariate dependence in financial time series. We propose the algorithm of risk measure computation using copula models. Using the optimal mean-$CVaR$ portfolio we compute portfolio's Profit and Loss series and corresponded risk measures curves. Value-at-risk and Conditional-Value-at-risk curves were simulated by three copula models: full Gaussian, Student's $t$ and regular vine copula. These risk curves are lower than historical values of the risk measures curve. All three models have superior prediction ability than a usual empirical method. Further directions of research are described.
    Date: 2017–07
  4. By: Xisong Jin; Francisco Nadal De Simone
    Abstract: This study takes a comprehensive approach to systemic risk stemming from Luxembourg’s Other Systemically Important Institutions (OSIIs), from the Global Systemically Important Banks (G-SIBs) to which they belong, from the investment funds sponsored by the OSIIs, from the housing market, from the non-financial corporate sector and from the sovereign. All sectoral balance sheets are integrated and the resulting systemic contingent claims are linked into a stochastic version of the general government balance sheet to gauge their impact on sovereign risk. Explicitly modelling default dependence and capturing the time-varying non-linearities and feedback effects typical of financial markets, the approach evaluates systemic losses and potential public sector costs from contingent liabilities stemming directly or indirectly from the financial sector. Various vulnerability and risk indicators suggest the sovereign is robust to a variety of shocks. The analysis highlights the key role of a sustainable fiscal position for financial stability.
    Keywords: financial stability; sovereign risk; macro-prudential policy; banking sector; investment funds; default probability; non-linearities; generalized dynamic factor model; dynamic copulas
    JEL: C1 E5 F3 G1
    Date: 2017–06
  5. By: Ian W. Jones; Michael G. Pollitt
    Abstract: This paper looks at positive case studies of organisational change at significant UK banks in response to the financial crisis. We present examples of good practice, which specifically address the identified need to change the culture and practice of UK banking. Our aim is to identify cases that can be of value in teaching. Our research complements the existing research on ethical banking and on culture change in UK banking. We begin by reviewing some of the literature on the crisis as it relates to the culture of banking in the UK. We go on to document three case studies from each of five banks with a significant retail business in the UK – Barclays, Lloyds, TSB, Santander and Hoare. We finish with a conclusion that draws out some over-arching lessons on culture change in UK banking from our case studies.
    Keywords: Corporate Culture, Barclays, Lloyds, TSB, Santander and Hoare
    Date: 2016–09
  6. By: Diana A. Iercosan; Ashish Kumbhat; Michael Ng; Jason J. Wu
    Abstract: As documented in the FEDS Notes article "Trading Activities at Systemically Important Banks, Part 1: Recent Trends in Trading Performance," trading performance at systemically important banks, measured by trading revenue per dollar of value-at-risk (VaR) committed, has trended up over the past few years.
    Date: 2017–07–10
  7. By: Aditya Maheshwari; Andrey Sarantsev
    Abstract: We consider a model when private banks with interbank cash flows as in (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets. The cash flow between private banks is also facilitated by the central bank. Each private bank aims to maximize its expected terminal logarithmic utility. The central bank, in turn, aims to control the overall size of financial system, and the rate of circulation between banks. A default occurs when the net worth of a bank goes below a certain threshold. We consider systemic risk by studying probability of a certain number of defaults over fixed finite time horizon.
    Date: 2017–07
  8. By: Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, USA); Guilherme Demos (ETH Zürich, Dept. of Management, Technology and Economics, Zürich, Switzerland); Rangan Gupta (Department of Economics, University of Pretoria, South Africa and IPAG Business School, Paris, France); Didier Sornette (ETH Zürich, Dept. of Management, Technology and Economics, Zürich, Switzerland and Swiss Finance Institute)
    Abstract: We examine the predictive power of market-based indicators over the positive and negative stock market bubbles via an application of the LPPLS ConfidenceTM Multi-scale Indicators to the S&P500 index. We find that the LPPLS framework is able to successfully capture, ex-ante, some of the prominent bubbles across different time scales, such as the Black Monday, Dot-com, and Subprime Crisis periods. We then show that measures of short selling activity have robust predictive power over negative bubbles across both short and long time horizons, in line with the previous studies suggesting that short sellers have predictive ability over stock price crash risks. Market liquidity, on the other hand, is found to have robust predictive power over both the negative and positive bubbles, while its predictive power is largely limited to short horizons. Short selling and liquidity are thus identified as two important factors contributing to the LPPLS-based bubble indicators. The evidence overall points to the predictability of stock market bubbles using market-based proxies of trading activity and can be used as a guideline to model and monitor the occurrence of bubble conditions in financial markets.
    Keywords: Financial bubble indicators, LPPL method, Markov switching, Predictability, Short interest
    JEL: C13 C58 G14
    Date: 2017–07
  9. By: Oleg L. Kritski; Vladimir F. Zalmezh
    Abstract: This paper is concerned with the asymptotics for Greeks of European-style options and the risk-neutral density function calculated under the constant elasticity of variance model. Formulae obtained help financial engineers to construct a perfect hedge with known behaviour and to price any options on financial assets.
    Date: 2017–06
  10. By: Andrew Clare; James Seaton; Peter N. Smith; Stephen Thomas
    Abstract: We examine the consequences of alternative popular investment strategies for the decumulation of funds invested for retirement through a defined contribution pension scheme. We examine in detail the viability of specific ‘safe’ withdrawal rates including the ‘4%-rule’ of Bengen (1994). We find two powerful conclusions; first that smoothing the returns on individual assets by simple trend following techniques is a potent tool to enhance withdrawal rates. Secondly, we show that while diversification across asset classes does lead to higher withdrawal rates than simple equity/bond portfolios, ’smoothing’ returns in itself is far more powerful a tool for raising withdrawal rates. in fact, smoothing the popular equity/bond portfolios (such as the 60/40 portfolio) is in itself an excellent and simple solution to constructing a retirement portfolio. Alternatively, trend following enables portfolios to contain more risky assets, and the greater upside they offer, for the same level of overall risk compared to standard portfolios.
    Keywords: Sequence Risk; Perfect Withdrawal Rate; Decumulation; Trend Following.
    JEL: G10 G11 G22
    Date: 2017–07
  11. By: Wilson Ye Chen; Gareth W. Peters; Richard H. Gerlach; Scott A. Sisson
    Abstract: We offer a novel way of thinking about the modelling of the time-varying distributions of financial asset returns. Borrowing ideas from symbolic data analysis, we consider data representations beyond scalars and vectors. Specifically, we consider a quantile function as an observation, and develop a new class of dynamic models for quantile-function-valued (QF-valued) time series. In order to make statistical inferences and account for parameter uncertainty, we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution. Compared to modelling realised measures, modelling the entire quantile functions of intra-daily returns allows one to gain more insight into the dynamic structure of price movements. Via simulations, we show that the proposed MCMC algorithm is effective in recovering the posterior distribution, and that the posterior means are reasonable point estimates of the model parameters. For empirical studies, the new model is applied to analysing one-minute returns of major international stock indices. Through quantile scaling, we further demonstrate the usefulness of our method by forecasting one-step-ahead the Value-at-Risk of daily returns.
    Date: 2017–07
  12. By: Stoforos, Chrysostomos; Degiannakis, Stavros; Palaskas, Theodosios
    Abstract: The assets of the hedge fund industry are nearly equivalent to the GDP of the UK. The industry, which claims returns independent of markets conditions and has been blamed for economic crises, has attracted the interest of a wide range of financial and political players and academics. This paper, using monthly series performance data since January 1995, at a fund strategy level and S&P500, and a holistic and a developed dynamic correlation quantitative approach, aims to challenge the allegations and the claims, which have been made on rather incomplete research grounds. Statistically, the results strongly reject the claims of the vast majority of fund strategies, excluding the case of the macro and short strategies, over the crisis periods, suggesting that they cannot protect their investors like S&P500. Regarding the allegations, it is inferred that Hedge Funds are used in most cases as a scapegoat rather than actually being the cause of the crises.
    Keywords: Absolute Returns, Carhart’s Model, Dynamic Conditional Correlation, Financial Crisis, Hedge Funds, Structural Breaks.
    JEL: C10 G1 G11 G23
    Date: 2016–10
  13. By: Degiannakis, Stavros
    Abstract: Two volatility forecasting evaluation measures are considered; the squared one-day-ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts. We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models.
    Keywords: ARFIMA model, HAR model, intra-day data, predictive ability, realized volatility, ultra-high frequency modelling.
    JEL: C14 C32 C50 G11 G15
    Date: 2016–01
  14. By: Massimo Caccia; Bruno R\'emillard
    Abstract: In this paper we solve the discrete time mean-variance hedging problem when asset returns follow a multivariate autoregressive hidden Markov model. Time dependent volatility and serial dependence are well established properties of financial time series and our model covers both. To illustrate the relevance of our proposed methodology, we first compare the proposed model with the well-known hidden Markov model via likelihood ratio tests and a novel goodness-of-fit test on the S\&P 500 daily returns. Secondly, we present out-of-sample hedging results on S\&P 500 vanilla options as well as a trading strategy based on theoretical prices, which we compare to simpler models including the classical Black-Scholes delta-hedging approach.
    Date: 2017–07

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