nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒10‒07
nine papers chosen by

  1. Equity Premium Puzzle or Faulty Economic Modelling? By Abootaleb Shirvani; Stoyan V. Stoyanov; Frank J. Fabozzi; Svetlozar T. Rachev
  2. Machine Learning Optimization Algorithms & Portfolio Allocation By Sarah Perrin; Thierry Roncalli
  3. Stationarity of the detrended time series of S&P500 By Karina Arias-Calluari; Morteza. N. Najafi; Michael S. Harr\'e; Fernando Alonso-Marroquin
  4. Looking through systemic credit risk: determinants, stress testing and market value By Álvaro Chamizo; Alfonso Novales
  5. Ratings matter: announcements in times of crisis and the dynamics of stock markets By Rosati, Nicoletta; Bellia, Mario; Matos, Pedro Verga; Oliviera, Vasco
  6. The BoC-BoE Sovereign Default Database: What’s New in 2019? By David Beers; Patrisha de Leon-Manlagnit
  7. Market risk when hedging a global credit portfolio By Álvaro Chamizo; Alfonso Novales
  8. The Role of an Aligned Investor Sentiment Index in Predicting Bond Risk Premia of the United States By Oguzhan Cepni; I. Ethem Guney; Rangan Gupta; Mark E. Wohar
  9. Consistent and Efficient Pricing of SPX and VIX Options under Multiscale Stochastic Volatility By Jaegi Jeon; Geonwoo Kim; Jeonggyu Huh

  1. By: Abootaleb Shirvani; Stoyan V. Stoyanov; Frank J. Fabozzi; Svetlozar T. Rachev
    Abstract: In this paper, we revisit the equity premium puzzle reported in 1985 by Mehra and Prescott. We show that the large equity premium that they report can be explained by choosing a more appropriate distribution for the return data. We demonstrate that the high-risk aversion value observed by Mehra and Prescott may be attributable to the problem of fitting a proper distribution to the historical returns and partly caused by poorly fitting the tail of the return distribution. We describe a new distribution that better fits the return distribution and when used to describe historical returns can explain the large equity risk premium and thereby explains the puzzle.
    Date: 2019–09
  2. By: Sarah Perrin; Thierry Roncalli
    Abstract: Portfolio optimization emerged with the seminal paper of Markowitz (1952). The original mean-variance framework is appealing because it is very efficient from a computational point of view. However, it also has one well-established failing since it can lead to portfolios that are not optimal from a financial point of view. Nevertheless, very few models have succeeded in providing a real alternative solution to the Markowitz model. The main reason lies in the fact that most academic portfolio optimization models are intractable in real life although they present solid theoretical properties. By intractable we mean that they can be implemented for an investment universe with a small number of assets using a lot of computational resources and skills, but they are unable to manage a universe with dozens or hundreds of assets. However, the emergence and the rapid development of robo-advisors means that we need to rethink portfolio optimization and go beyond the traditional mean-variance optimization approach. Another industry has faced similar issues concerning large-scale optimization problems. Machine learning has long been associated with linear and logistic regression models. Again, the reason was the inability of optimization algorithms to solve high-dimensional industrial problems. Nevertheless, the end of the 1990s marked an important turning point with the development and the rediscovery of several methods that have since produced impressive results. The goal of this paper is to show how portfolio allocation can benefit from the development of these large-scale optimization algorithms. Not all of these algorithms are useful in our case, but four of them are essential when solving complex portfolio optimization problems. These four algorithms are the coordinate descent, the alternating direction method of multipliers, the proximal gradient method and the Dykstra's algorithm.
    Date: 2019–09
  3. By: Karina Arias-Calluari; Morteza. N. Najafi; Michael S. Harr\'e; Fernando Alonso-Marroquin
    Abstract: Our study presents the analysis of stock market data of S&P500 before and after been detrended. The analysis is based on two types of returns, simple return and log-return respectively. Both of them are non-stationary time series. This means that their statistical distribution change over time. Consequently a detrended process is made to neutralize the non-stationary effects. The detrended process is obtained by decomposing the financial time series into a deterministic trend and random fluctuations. We present an alternative method on detrending time series based on the classical moving average (MA) models, where Kurtosis is used to determine the windows size. Then, the dentrending fluctuation analysis (DFA) is use to show that the detrended part is stationary. This is done by considering the autocorrelation of detrended price return and the power spectrum analysis of detrended price.
    Date: 2019–10
  4. By: Álvaro Chamizo (BBVA.); Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.)
    Abstract: We provide a methodology to estimate a Global Credit Risk Factor (GCRF) from CDS spreads using the information provided by the default-related component of observed spreads. These are previ- ously estimated using Pan and Singleton (2008) methodology. The estimated factor contains higher explanatory power on CDS spread fluctuations across sectors than standard credit indices like iTraxx or CDX. We find a positive association between GCRF and implied volatility variables, and a negative association with MSCI stock market sector indices as well as with interest rates and with the slope and the curvature of the term structure. Such correlations provide useful insights for risk management as well as for the hedging of credit portfolios. Indeed, we present a synthetic factor regression model for GCRF that we apply in a stress testing methodology for credit portfolios as well as to evaluate future credit risk scenarios. Finally, we show evidence suggesting that the exposure to systemic credit risk was priced in the market during the 2006-2015 period.
    Keywords: Credit Risk; Systemic Risk; Idiosyncratic Risk; Stress Tests; Factor Models; Market Pricing.
    JEL: E44 F34 G01 G11 G23 G32
    Date: 2019–09
  5. By: Rosati, Nicoletta (European Commission -- JRC); Bellia, Mario (European Commission -- JRC); Matos, Pedro Verga (University of Lisbon); Oliviera, Vasco (University of Lisbon)
    Abstract: In this paper we propose a novel approach in analysing the impact of changes in sovereign credit ratings on stock markets. We study the evolution of a segmented form of the stock market index for several crisis-hit countries, including both European and Asian markets. Such evolution is modelled by a homogeneous Markov chain, where the transition probabilities from one starting level of the index to a new (lower or higher) level in the next period depend on some explanatory variables, namely the country’s rating, GDP and interest rate, through a generalised ordered probit model. The credit ratings turn out to be determinant in the dynamics of the stock markets for all three European countries considered - Portugal, Spain and Greece, while not all considered Asian countries show evidence of correlation of market indices with the ratings.
    Keywords: Credit ratings; financial crisis; Europe; Markov chains; generalized ordered probit models
    JEL: C25 C58 E44 G01 G15 G24
    Date: 2019–09
  6. By: David Beers; Patrisha de Leon-Manlagnit
    Abstract: Until recently, few efforts have been made to systematically measure and aggregate the nominal value of the different types of sovereign government debt in default. To help fill this gap, the Bank of Canada (BoC) developed a comprehensive database of sovereign defaults that is posted on its website and updated in partnership with the Bank of England (BoE). Our database draws on previously published datasets compiled by various public and private sector sources. It combines elements of these, together with new information, to develop comprehensive estimates of stocks of government obligations in default. These include bonds and other marketable securities, bank loans and official loans, valued in US dollars, for the years 1960 to 2018 on both a country-by-country and a global basis. This update of the BoC-BoE database, and future updates, will be useful to researchers analyzing the economic and financial effects of individual sovereign defaults and, importantly, the impact on global financial stability of episodes involving multiple sovereign defaults.
    Keywords: Debt Management; Development economics; Financial stability; International financial markets
    JEL: F34 G10 G14 G15
    Date: 2019–09
  7. By: Álvaro Chamizo (BBVA.); Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.)
    Abstract: Hedging a credit portfolio using single name CDS is affected by high spread volatility that induces continuous changes in a portfolio mark to market, which is a nuisance. Often, the problem is that CDS on firms in the portfolio are not being traded. To get around that, a derivative portfolio can be hedged by taking a contrary position in a credit index, and we examine in this paper the efficiency of such an imperfect hedge. We find over the 2007-2012 period an 80% hedging efficiency for a European portfolio, 60% for North American and Japanese portfolios, and around 70% for a global portfolio, as measured by the reduction in mark-to-market variance. We also consider sectorial credit portfolios for Europe and North America, for which hedging efficiency is not as high, due to their more import- ant idiosyncratic component. Taking into account the quality of the credit counterpart improves the effectiveness of the hedge, although it requires using less liquid credit indices, with higher transaction costs. Standard conditional volatility models provide similar results to the least squares hedge, except for extreme market movements. An efficient hedge for a credit portfolio made up of the most idiosyn- cratic firms would seem to require more than 50 firms, while the hedge for portfolios made up of the less idiosyncratic firms achieves high efficiency even for a small number of firms. The efficiency of the hedge is higher when portfolio volatility is high and also when short term interest rates or exchange rate volatility are high. Increases in VIX, in the 10-year swap rate or in liquidity risk tend to decrease hedging efficiency. Credit indices offer a moderately efficient hedge for corporate bond portfolios, which we have examined with a reduced sample of firms over 2006-2018. This analysis also shows that the current efficiency of a credit index hedge has recovered at pre-crisis levels.
    Keywords: Market Risk; CDS; Credit Indices; Credit Hedge; Asset Allocation; Systemic Risk.
    JEL: G01 G12 G13 G14 G15
    Date: 2019–09
  8. By: Oguzhan Cepni (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara,Turkey); I. Ethem Guney (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara,Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA; School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU, UK)
    Abstract: In this paper, we develop a new investor sentiment index that is aligned with the purpose of predicting the excess returns on government bonds of the United States (US) of maturities of 2-, 3-, 4-, 5-year. By eliminating a common noise component in underlying sentiment proxies using the partial least squares (PLS) approach, the new index is shown to have much greater predictive power than the original principal component analysis (PCA)-based sentiment index both in- and out-of-sample, with the predictability being statistically significant, especially for bond premia with shorter maturities, even after controlling for a large number of financial and macro factors, as well as investor attention and manager sentiment indexes. Given the role of Treasury securities in forecasting of output and inflation, and portfolio allocation decisions, our findings have significant implications for investors, policymakers and researchers interested in accurately forecasting return dynamics for these assets.
    Keywords: Bond premia, Investor attention, Investor sentiment, Predictability, Out-of-sample forecasts
    JEL: C22 C53 G12 G17
    Date: 2019–09
  9. By: Jaegi Jeon; Geonwoo Kim; Jeonggyu Huh
    Abstract: This study provides a consistent and efficient pricing method for both Standard & Poor's 500 Index (SPX) options and the Chicago Board Options Exchange's Volatility Index (VIX) options under a multiscale stochastic volatility model. To capture the multiscale volatility of the financial market, our model adds a fast scale factor to the well-known Heston volatility and we derive approximate analytic pricing formulas for the options under the model. The analytic tractability can greatly improve the efficiency of calibration compared to fitting procedures with the finite difference method or Monte Carlo simulation. Our experiment using options data from 2016 to 2018 shows that the model reduces the errors on the training sets of the SPX and VIX options by 9.9% and 13.2%, respectively, and decreases the errors on the test sets of the SPX and VIX options by 13.0\% and 16.5\%, respectively, compared to the single-scale model of Heston. The error reduction is possible because the additional factor reflects short-term impacts on the market, which is difficult to achieve with only one factor. It highlights the necessity of modeling multiscale volatility.
    Date: 2019–09

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NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.