nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒07‒27
27 papers chosen by
Stan Miles
Thompson Rivers University

  1. Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility By Sana Ben Hamida; Wafa Abdelmalek; Fathi Abid
  2. Dynamic Hedging using Generated Genetic Programming Implied Volatility Models By Fathi Abid; Wafa Abdelmalek; Sana Ben Hamida
  3. Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network By E. Ramos-P\'erez; P. J. Alonso-Gonz\'alez; J. J. N\'u\~nez-Vel\'azquez
  4. Optimal Hedging in Incomplete Markets By George Bouzianis; Lane P. Hughston
  5. Does Going Tough on Banks Make the Going Get Tough? Bank Liquidity Regulations, Capital Requirements, and Sectoral Activity By Deniz O Igan; Ali Mirzaei
  6. Numerical aspects of integration in semi-closed option pricing formulas for stochastic volatility jump diffusion models By Josef Dan\v{e}k; J. Posp\'i\v{s}il
  7. Large Time-Varying Volatility Models for Electricity Prices By Angelica Gianfreda; Francesco Ravazzolo; Luca Rossini
  8. Predicting the VIX and the Volatility Risk Premium: The Role of Short-run Funding Spreads Volatility Factors By Elena Andreou; Eric Ghysels
  9. Contagious Margin Calls: How Covid-19 threatened global stock market liquidity By Foley, Sean; Kwan, Amy; Philip, Richard; Ødegaard, Bernt Arne
  10. Bank Default Risk Propagation along Supply Chains: Evidence from the U.K. By Spatareanu, M.; Manole, V.; Kabiri, A.; Roland, I.
  11. Systemic Risk Modeling: How Theory Can Meet Statistics By Raphael A Espinoza; Miguel A. Segoviano; Ji Yan
  12. The International Spread of COVID-19 Stock Market Collapses By Contessi, Silvio; De Pace, Pierangelo
  13. Valuing mortality risk in the time of covid-19 By Hammitt, James K.
  14. Variance and interest rate risk in unit-linked insurance policies By David R. Ba\~nos; Marc Lagunas-Merino; Salvador Ortiz-Latorre
  15. Riding the Yield Curve: Risk Taking Behavior in a Low Interest Rate Environment By Ralph Chami; Thomas F. Cosimano; Celine Rochon; Julieta Yung
  16. Cyber Attacks, Spillovers and Contagion in the Cryptocurrency Markets By Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo
  17. How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm By Gambacorta, Leonardo; Huang, Yiping; Qiu, Han; Wang, Jingyi
  18. Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects By Soheil Ghili; Ben Handel; Igal Hendel; Michael D. Whinston
  19. Risk Management and Return Prediction By Qingyin Ge; Yunuo Ma; Yuezhi Liao; Rongyu Li; Tianle Zhu
  20. Measuring exchange rate risks during periods of uncertainty By Laurent Ferrara; Joseph Yapi
  21. Optimal Risk-Sharing Across a Network of Insurance Companies By Nicolas Ettlin; Walter Farkas; Andreas Kull; Alexander Smirnow
  22. How to Control the Fiscal Costs of Public-Private Partnerships By Timothy C Irwin; Samah Mazraani; Sandeep Saxena
  23. Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency By Donggyu Kim; Xinyu Song; Yazhen Wang
  24. Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit By Massimo Guidolin; Manuela Pedio
  25. Tempered Stable Processes with Time Varying Exponential Tails By Young Shin Kim; Kum-Hwan Roh; Raphael Douady
  26. Networks in risk spillovers: A multivariate GARCH perspective By Monica Billio; Massimiliano Caporin; Lorenzo Frattarolo; Loriana Pelizzon
  27. The Ins and Outs of Selling Houses: Understanding Housing Market Volatility By Ngai, Liwa Rachel; Sheedy, Kevin D.

  1. By: Sana Ben Hamida; Wafa Abdelmalek; Fathi Abid
    Abstract: Volatility is a key variable in option pricing, trading and hedging strategies. The purpose of this paper is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out of sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models which are not adapted to some out of sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training-subset selection methods are proposed based on random, sequential or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases errors. Using real data from SP500 index options, these techniques are compared to the static subset selection method. Based on MSE total and percentage of non fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, specially those obtained from the adaptive random training subset selection method applied to the whole set of training samples.
    Date: 2020–06
  2. By: Fathi Abid; Wafa Abdelmalek; Sana Ben Hamida
    Abstract: The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.
    Date: 2020–06
  3. By: E. Ramos-P\'erez; P. J. Alonso-Gonz\'alez; J. J. N\'u\~nez-Vel\'azquez
    Abstract: An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.
    Date: 2020–06
  4. By: George Bouzianis; Lane P. Hughston
    Abstract: We consider the problem of optimal hedging in an incomplete market with an established pricing kernel. In such a market, prices are uniquely determined, but perfect hedges are usually not available. We work in the rather general setting of a L\'evy-Ito market, where assets are driven jointly by an $n$-dimensional Brownian motion and an independent Poisson random measure on an $n$-dimensional state space. Given a position in need of hedging and the instruments available as hedges, we demonstrate the existence of an optimal hedge portfolio, where optimality is defined by use of an expected least squared-error criterion over a specified time frame, and where the numeraire with respect to which the hedge is optimized is taken to be the benchmark process associated with the designated pricing kernel.
    Date: 2020–06
  5. By: Deniz O Igan; Ali Mirzaei
    Abstract: Whether and to what extent tougher bank regulation weighs on economic growth is an open empirical question. Using data from 28 manufacturing industries in 50 countries, we explore the extent to which cross-country differences in bank liquidity and capital levels were related to differences in sectoral activity around the period of the global financial crisis. We find that industries which are more dependent on external finance, in countries where banks had higher liquidity and capital ratios, performed relatively better during the crisis, with regard to investment rates and the creation of new enterprises. This relationship, however, exists only for bank-based systems and emerging market economies. In the pre-crisis period, we find only a marginal link to bank capital. These findings survive a battery of robustness checks and provide some solid support for the tighter prudential measures introduced under Basel III.
    Date: 2020–06–19
  6. By: Josef Dan\v{e}k; J. Posp\'i\v{s}il
    Abstract: In mathematical finance, a process of calibrating stochastic volatility (SV) option pricing models to real market data involves a numerical calculation of integrals that depend on several model parameters. This optimization task consists of large number of integral evaluations with high precision and low computational time requirements. However, for some model parameters, many numerical quadrature algorithms fail to meet these requirements. We can observe an enormous increase in function evaluations, serious precision problems and a significant increase of computational time. In this paper we numerically analyse these problems and show that they are especially caused by inaccurately evaluated integrands. We propose a fast regime switching algorithm that tells if it is sufficient to evaluate the integrand in standard double arithmetic or if a higher precision arithmetic has to be used. We compare and recommend numerical quadratures for typical SV models and different parameter values, especially for problematic cases.
    Date: 2020–06
  7. By: Angelica Gianfreda; Francesco Ravazzolo; Luca Rossini
    Abstract: We study the importance of time-varying volatility in modelling hourly electricity prices when fundamental drivers are included in the estimation. This allows us to contribute to the literature of large Bayesian VARs by using well-known time series models in a huge dimension for the matrix of coefficients. Based on novel Bayesian techniques, we exploit the importance of both Gaussian and non-Gaussian error terms in stochastic volatility. We find that by using regressors as fuels prices, forecasted demand and forecasted renewable energy is essential in order to properly capture the volatility of these prices. Moreover, we show that the time-varying volatility models outperform the constant volatility models in both the in-sample model- fit and the out-of-sample forecasting performance.
    Keywords: Electricity, Hourly Prices, Renewable Energy Sources, Non-Gaussian, Stochastic-Volatility, Forecasting
    Date: 2020–07
  8. By: Elena Andreou; Eric Ghysels
    Abstract: This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities - in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price equity volatility risk using factors which go beyond the equity class namely Volatility Factors extracted from panels of volatilities of short-run funding spreads. The role of these Volatility Factors is compared with the corresponding factors extracted from the panels of the above spreads as well as related factors proposed in the literature. Our monthly short-run funding spreads Volatility Factors provide both in- and out-of-sample predictive gains for forecasting the monthly VIX, RV as well as the equity premium, while the corresponding daily volatility factors via Mixed Data Sampling (MIDAS) models provide further improvements.
    Keywords: Factor asset pricing models; Volatility Factors; ARCH filters
    JEL: C2 C5 G1
    Date: 2020–03
  9. By: Foley, Sean (Macquarie University, Australia); Kwan, Amy (University of Sydney, Australia); Philip, Richard (University of Sydney, Australia); Ødegaard, Bernt Arne (University of Stavanger)
    Abstract: The Covid-19 epidemic has caused some of the largest - and fastest - market dislocations in modern history. Contemporaneous with the significant fall in equity market values is the evaporation of market liquidity. We document the evolution of transactions costs, depth and rewards to liquidity suppliers across a variety of countries affected by the virus. We show that transactions costs increase sharply in a coordinated fashion across global markets, with depth drying up almost overnight. The withdrawal of global liquidity suppliers is correlated with the increase of over 400% in margin requirements, driving a pro-cyclical downwards liquidity spiral. These affects are shown to be concentrated in securities most exposed to electronic market-makers.
    Keywords: Covid-19; Margin requirements; Stock market liquidity
    JEL: G01 G12 G14 G15
    Date: 2020–07–08
  10. By: Spatareanu, M.; Manole, V.; Kabiri, A.; Roland, I.
    Abstract: How does banks’ default risk affect the probability of default of non-financial businesses? The literature has addressed this question by focusing on the direct effects on the banks’ corporate customers – demonstrating the existence of bank-induced increases in firms’ probabilities of default. However, it fails to consider the indirect effects through the interfirm transmission of default risk along supply chains. Supply chain relationships have been shown to be a powerful channel for default risk contagion. Therefore, the literature might severely underestimate the overall impact of bank shocks on default risk in the business economy. Our paper fills this gap by analyzing the direct as well as the indirect impact of banks’ default risk on firms’ default risk in the U.K. Relying on Input-Output tables, we devise methods that enable us to examine this question in the absence of microeconomic data on supply chain links. To capture all potential propagation channels, we account for horizontal linkages between the firm and its competitors in the same industry, and for vertical linkages, both between the firm and its suppliers in upstream industries and between the firm and its customers in downstream industries. In addition, we identify trade credit and contract specificity as significant characteristics of supply chains, which can either amplify or dampen the propagation of default risk. Our results show that the banking crisis of 2007-2008 affected the non-financial business sector well beyond the direct impact of banks’ default risk on their corporate clients.
    Keywords: default risk, propagation of banking crises, supply chains
    JEL: G21 G34 O16 O30
    Date: 2020–06–26
  11. By: Raphael A Espinoza; Miguel A. Segoviano; Ji Yan
    Abstract: We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.
    Keywords: Financial crises;Bank credit;Financial markets;Financial institutions;Macroprudential policies and financial stability;Systemic risk,Minsky effect,CIMDO,Default,WP,interbank,repayment rate,expected shortfall,time t,Minsky
    Date: 2020–03–13
  12. By: Contessi, Silvio (Monash Business School); De Pace, Pierangelo
    Abstract: We identify periods of mildly explosive dynamics and collapses in the stock markets of 18 major countries during the COVID-19 pandemic of 2020. We find statistical evidence of instability transmission from the Chinese stock market to all other markets. The recovery is heterogeneous and not explosive in all markets.
    Keywords: Mildly Explosive Behavior, Stock Market Crashes, COVID-19
    Date: 2020–06–20
  13. By: Hammitt, James K.
    Abstract: In evaluating the appropriate response to the covid-19 pandemic, a key parameter is the rate of substitution between mortality risk and wealth or income, conventionally summarized as the value per statistical life (VSL). For the United States, VSL is estimated as approximately $10 million, which implies the value of preventing 100,000 covid-19 deaths is $1 trillion. Is this value too large? There are reasons to think so. First, VSL is a marginal rate of substitution and the potential risk reductions are non-marginal. The standard VSL model implies the rate of substitution of wealth for risk reduction is smaller when the risk reduction is larger, but the implied value of non-marginal risk reductions decreases slowly until the value accounts for a substantial share of income, after which it decreases sharply; average individuals are predicted to be willing to spend more than half their income to reduce one-year mortality risk by only 1 in 100. Second, mortality risk is concentrated among the elderly, for whom VSL may be smaller and who would benefit from a persistent risk reduction for a shorter period because of their shorter life expectancy. Third, the pandemic and responses to it have caused substantial losses in income that should decrease VSL. In contrast, VSL is plausibly larger for risks (like covid-19) that are dreaded, uncertain, catastrophic, and ambiguous. These arguments are evaluated and key issues for improving estimates are highlighted.
    Keywords: value per statistical life; pandemic; age-dependence; ambiguity aversion; risk perception
    Date: 2020–06
  14. By: David R. Ba\~nos; Marc Lagunas-Merino; Salvador Ortiz-Latorre
    Abstract: One of the risks derived from selling long term policies that any insurance company has, arises from interest rates. In this paper we consider a general class of stochastic volatility models written in forward variance form. We also deal with stochastic interest rates to obtain the risk-free price for unit-linked life insurance contracts, as well as providing a perfect hedging strategy by completing the market. We conclude with a simulation experiment, where we price unit-linked policies using Norwegian mortality rates. In addition we compare prices for the classical Black-Scholes model against the Heston stochastic volatility model with a Vasicek interest rate model.
    Date: 2020–06
  15. By: Ralph Chami; Thomas F. Cosimano; Celine Rochon; Julieta Yung
    Abstract: Investors seek to hedge against interest rate risk by taking long or short positions on bonds of different maturities. We study changes in risk taking behavior in a low interest rate environment by estimating a market stochastic discount factor that is non-linear and therefore consistent with the empirical properties of cashflow valuations identified in the literature. We provide evidence that non-linearities arise from hedging strategies of investors exposed to interest rate risk. Capital losses are amplified when interest rates increase and risk averse investors have taken positions on instruments with longer maturity, expecting instead interest rates to revert back to their historical average.
    Keywords: Interest rate increases;Discount rates;Risk premium;Financial markets;Financial instruments;Interest rate risk,non-linear stochastic discount factor,investment portfolio,term structure model,risk aversion distribution,low interest rate environment,WP,yield curve,interest-rate,risk aversion,conditional mean,treasury security
    Date: 2020–03–13
  16. By: Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo
    Abstract: This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber attacks. Significant dynamic linkages (interdependence) among the three cryptocurrencies under investigation are found in most cases when cyber attacks are taken into account, Bitcoin appearing to be the dominant one. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
    Keywords: mean and volatility spillovers, contagion, cryptocurrencies, cyber attacks
    JEL: C32 F30 G15
    Date: 2020
  17. By: Gambacorta, Leonardo; Huang, Yiping; Qiu, Han; Wang, Jingyi
    Abstract: This paper compares the predictive power of credit scoring models based on machine learning techniques with that of traditional loss and default models. Using proprietary transaction-level data from a leading fintech company in China for the period between May and September 2017, we test the performance of different models to predict losses and defaults both in normal times and when the economy is subject to a shock. In particular, we analyse the case of an (exogenous) change in regulation policy on shadow banking in China that caused lending to decline and credit conditions to deteriorate. We find that the model based on machine learning and non-traditional data is better able to predict losses and defaults than traditional models in the presence of a negative shock to the aggregate credit supply. One possible reason for this is that machine learning can better mine the non-linear relationship between variables in a period of stress. Finally, the comparative advantage of the model that uses the fintech credit scoring technique based on machine learning and big data tends to decline for borrowers with a longer credit history.
    Keywords: credit risk; credit scoring; Fintech; Machine Learning; non-traditional information
    JEL: G17 G18 G23 G32
    Date: 2019–12
  18. By: Soheil Ghili (Cowles Foundation, Yale University); Ben Handel (Department of Economics, UC Berkeley); Igal Hendel (Department of Economics, Northwestern University); Michael D. Whinston (Department of Economics and Sloan School of Management, M.I.T)
    Abstract: Reclassiï¬ cation risk is a major concern in health insurance where contracts are typically one year in length but health shocks often persist for much longer. We theoretically characterize optimal long-term insurance contracts with one-sided commitment, and use our characterization to provide a simple computation algorithm for computing optimal contracts from primitives. We apply this method to derive empirically-based optimal long-term health insurance contracts using all-payers claims data from Utah, and then evaluate the potential welfare performance of these contracts. We ï¬ nd that optimal long-term health insurance contracts that start at age 25 can eliminate over 94% of the welfare loss from reclassiï¬ cation risk for individuals who arrive on the market in good health, but are of little beneï¬ t to the worst age-25 health risks. As a result, their ex ante value depends signiï¬ cantly on whether pre-age-25 health risk is otherwise insured. Their value also depends on individuals’ expected income growth.
    JEL: L5 I1 D0
    Date: 2019–12
  19. By: Qingyin Ge; Yunuo Ma; Yuezhi Liao; Rongyu Li; Tianle Zhu
    Abstract: With the good development in the financial industry, the market starts to catch people's eyes, not only by the diversified investing choices ranging from bonds and stocks to futures and options but also by the general "high-risk, high-reward" mindset prompting people to put money in the financial market. People are interested in reducing risk at a given level of return since there is no way of having both high returns and low risk. Many researchers have been studying this issue, and the most pioneering one is Harry Markowitz's Modern Portfolio Theory developed in 1952, which is the cornerstone of investment portfolio management and aims at "maximum the return at the given risk". In contrast to that, fifty years later, E. Robert Fernholz's Stochastic Portfolio Theory, as opposed to the normative assumption served as the basis of earlier modern portfolio theory, is consistent with the observable characteristics of actual portfolios and markets. In this paper, after introducing some basic theories of Markowitz's MPT and Fernholz's SPT, then we step across to the application side, trying to figure out under four basic models based on Markowitz Efficient Frontier, including Markowitz Model, Constant Correlation Model, Single Index Model, and Multi-Factor Model, which portfolios will be selected and how do these portfolios perform in the real world. Here we also involve universal Portfolio Algorithmby Thomas M. Cover to select portfolios as a comparison. Besides, each portfolio value at Risk, Expected Shortfall, and corresponding bootstrap confidence interval for risk management will be evaluated. Finally, by utilizing factor analysis and time series models, we could predict the future performance of our four models.
    Date: 2020–06
  20. By: Laurent Ferrara; Joseph Yapi
    Abstract: In this paper, we empirically look at the effects of uncertainty on risk measures for exchange rates, by focusing on two recent specific periods: the Brexit and the outbreak of the Covid-19. Based on a Fama regression extended with uncertainty measures, we forecast exchange rate in the short run through a quantile regression approach. By fitting a Skewed-Student distribution to the quantile forecasts, we put forward measures of risks for appreciation and depreciation of the expected exchange rates. We point out two interesting results. First, we show that the increase in Brexit-related uncertainty is strongly associated to higher future depreciation risks of the British Pound vs the Euro, as a mistrust towards the British economy. Second, we get that the Covid-related uncertainty is perceived as a global risk, leading to a flight-to-safety move towards the US Dollar and associated high depreciation risks for emerging currencies.
    Keywords: Exchange rate, Risk measures, Fama regression, Uncertainty, Covid-19 crisis, Brexit
    JEL: C22 C53 F31
    Date: 2020–06
  21. By: Nicolas Ettlin (University of Basel, Actuarial Science, Department of Mathematics and Computer Science); Walter Farkas (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; ETH Zürich); Andreas Kull (University of Basel, Actuarial Science, Department of Mathematics and Computer Science; BerninaRe Ltd.); Alexander Smirnow (University of Zurich - Department of Banking and Finance)
    Abstract: Risk transfer is a key risk and capital management tool for insurance companies. Transferring risk between insurers is used to mitigate risk and manage capital re- quirements. We investigate risk transfer in the context of a network environment of insurers and consider capital costs and capital constraints at the level of individual insurance companies. We demonstrate that the optimisation of profitability across the network can be achieved through risk transfer. Considering only individual in- surance companies, there is no unique optimal solution and, a priori, it is not clear which solutions are fair. However, from a network perspective, we derive a unique fair solution in the sense of cooperative game theory. Implications for systemic risk are briefly discussed.
    Keywords: risk transfer, risk-based capital, reinsurance, return optimisation, conditional expected shortfall
    JEL: G13 G22 D85 C57 C71
    Date: 2020–06
  22. By: Timothy C Irwin; Samah Mazraani; Sandeep Saxena
    Abstract: This note discusses what finance ministries can do to ensure that public-private partnerships (PPPs) are used wisely. By inviting private participation in infrastructure development and service provision, PPPs can help improve public services. Yet, strong governance institutions are needed to manage risks and avoid unexpected costs from PPPs. While in the short term, PPPs may appear cheaper than traditional public investment, over time they can turn out to be more expensive and undermine fiscal sustainability, particularly when governments ignore or are unaware of their deferred costs and associated fiscal risks. To use PPPs wisely governments should (1) develop and implement clear rules for their use; (2) identify, quantify, and disclose PPP risks and expected costs; and (3) reform budget and government accounting frameworks to capture all fiscal costs comprehensively.
    Keywords: Fiscal risk;Fiscal sustainability;Fiscal policy;Fiscal management;Risk management;Fiscal rules;Accounting for Public-Private Partnerships (PPPs);Public services;Public-private partnership;Fiscal rules and institutions;public-private partnerships, finance ministers, fiscal costs
    Date: 2018–10–16
  23. By: Donggyu Kim; Xinyu Song; Yazhen Wang
    Abstract: This paper introduces unified models for high-dimensional factor-based Ito process, which can accommodate both continuous-time Ito diffusion and discrete-time stochastic volatility (SV) models by embedding the discrete SV model in the continuous instantaneous factor volatility process. We call it the SV-Ito model. Based on the series of daily integrated factor volatility matrix estimators, we propose quasi-maximum likelihood and least squares estimation methods. Their asymptotic properties are established. We apply the proposed method to predict future vast volatility matrix whose asymptotic behaviors are studied. A simulation study is conducted to check the finite sample performance of the proposed estimation and prediction method. An empirical analysis is carried out to demonstrate the advantage of the SV-Ito model in volatility prediction and portfolio allocation problems.
    Date: 2020–06
  24. By: Massimo Guidolin; Manuela Pedio
    Abstract: Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.
    Keywords: Attention, Sentiment, Text Mining, Forecasting, Conditional Variance, GARCH model, Brexit
    JEL: C53 C58 G17
    Date: 2020
  25. By: Young Shin Kim; Kum-Hwan Roh; Raphael Douady
    Abstract: In this paper, we introduce a new time series model having the stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time varying parameter. The model captures the stochastic exponential tail which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S\&P 500 index return data. We present Monte-Carlo simulation technique for a parameter calibration of the model for the S\&P 500 option prices. By the calibration, we can see that the stochastic exponential tail makes the model better to analyze the market option prices.
    Date: 2020–06
  26. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari); Massimiliano Caporin (Department of Statistical Sciences, University Of Padua); Lorenzo Frattarolo (Department of Economics, University Of Venice Cà Foscari); Loriana Pelizzon (SAFE-Goethe University Frankfurt (Germany); Department of Economics, University Of Venice Cà Foscari)
    Abstract: We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral Multivariate GARCH speci_cation. We study covariance stationarity and identification of the model, develop the quasi-maximum-likelihood estimator and analyze its consistency and asymptotic normality. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euroarea bond data shows that Italy and Ireland are key players in spreading risk, France and Portugal are major risk receivers, and we uncover Spain's non-trivial role as risk middleman.
    Keywords: Spatial GARCH, network, risk spillover, financial spillover
    JEL: C58 G10
    Date: 2020
  27. By: Ngai, Liwa Rachel; Sheedy, Kevin D.
    Abstract: This paper documents the cyclical properties of housing-market variables, which are shown to be volatile, persistent, and highly correlated with each other. Is the observed volatility in these variables due to changes in the speed at which houses are sold or changes in the number of houses that are put up for sale? An inflow-outflow decomposition shows that the inflow rate accounts for almost all of the fluctuations in sales volume. The paper then shows that a search-and-matching model with endogenous moving subject to housing demand shocks performs well in explaining fluctuations in housing-market variables. A housing demand shock induces more moving and increases the supply of houses on the market, thus one housing demand shock replicates three correlated reduced-form shocks that would be needed to match the relative volatilities and correlations among key housing-market variables.
    Keywords: Endogenous moving; Housing market volatility; inflow-outflow decomposition; search frictions
    Date: 2020–01

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