Forecasting dynamic return distributions based on ordered binary choice

We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.     

Conditional risk measures in a bipartite market structure

In this paper, we study the effect of network structure between agents and objects on measures for systemic risk. We model the influence of sharing large exogeneous losses to the financial or (re)insurance market by a bipartite graph. Using Pareto-tailed losses and multivariate regular variation, we obtain asymptotic results for conditional risk measures based on the Value-at-Risk and the Conditional Tail Expectation. These results allow us to assess the influence of an individual institution on the systemic or market risk and vice versa through a collection of conditional risk measures. For large markets, Poisson approximations of the relevant constants are provided. Differences of the conditional risk measures for an underlying homogeneous and inhomogeneous random graph are illustrated by simulations.     

Portfolio optimization based on GARCH-EVT-Copula forecasting models

This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-$t$ and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-$t$ copulas, are best at reducing the portfolio risk.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Cooperative game with nondeterministic returns

We study a non-traditional cooperative game where returns from coalitions are nondeterministic. The long-standing concept of core can be generalized to reflect players’ contentment with their allocations. It is now imperative to formalize the restrictions, such as those pertaining to information, on allocations. The latter are also at times more conducive to fractional representations. With probabilistic structures added, nondeterministic returns become random variables, utility functions attain risk-attitude connotations, and the timing of players’ allocation resolutions gains significance. Under various conditions for utility functions, we show how various core concepts of the general game can be related to its traditionally defined auxiliaries. These developments help pave the way for our illustrations, within two distinct settings, that players’ increased risk aversion would promote the formation of the grand coalition.     

Do differences in national cultures affect cross-country conditional conservatism behavior under IFRS?

The purpose of this paper is to explore whether international differences in cultural dimensions of individualism and uncertainty avoidance affect how managers from different countries implement International Financial Reporting Standards (IFRS) and influence cross-country conditional conservatism behavior. We analyze the conditional conservatism behavior of publicly listed firms in 14-member countries of the European Union (EU) during the period 2006-2016. The results confirm the relationship between the individualism and uncertainty avoidance dimensions of national culture and conditional conservatism in the post-IFRS period. Particularly, conditional conservatism is higher in countries where individualism is lower and where uncertainty avoidance is higher.     

中国农村减贫成效测度、分解及影响因素研究——基于分布动态学框架的CFPS数据分析

测算农村减贫成效并考察减贫成效的影响因素对我国打赢"脱贫攻坚战"具有重要的现实意义。本文基于覆盖我国东、中、西和东北四大地区的CFPS调查数据,在Quah(1997)的分布动态学(Distribution dynamics)基础上构建了完整的减贫成效分析框架。首先测算出各地区农村家庭贫困内部状态的Markov转移概率矩阵,并在此基础上构建了可分解的绝对、相对减贫指数,以综合考察各地区的农村减贫成效,随后本文通过首次构造的"条件Markov模型"进一步考察了减贫成效的影响因素。研究结果发现:(1)样本考察期内,我国各地区的农村贫困状况随着时间的积累逐渐变好,贫困发生率均有所下降;(2)减贫过程中仍存在着脱贫又返贫的现象,农村家庭的深度贫困固化问题比轻度贫困问题更加严重,意味着扶贫开发工作正式进入攻坚时期,扶贫难度加大;(3)家庭成年成员特征、家庭特征以及收入类型对农村减贫成效的影响显著性情况存在着时段和区域的异质性。其中,家庭成年成员受教育水平、家庭中是否有成员从事有薪酬的农业劳动、工资性和营业性收入对家庭的贫困状态变动具有更显著的影响。基于此,政府应贯彻落实精准扶贫,将扶贫重点放在中西部地区和处于深度贫困的家庭,另外应再调整和倾斜扶贫资源与扶贫政策,将提高农民受教育水平、增加非农业就业机会作为主要减贫途径,将促进农村工资性和营业性收入增长作为主要的政策倾斜方向。     

Intraday pairs trading strategies on high frequency data: the case of oil companies

This paper introduces novel ‘doubly mean-reverting’ processes based on conditional modelling of model spreads between pairs of stocks. Intraday trading strategies using high frequency data are proposed based on the model. This model framework and the strategies are designed to capture ‘local’ market inefficiencies that are elusive for traditional pairs trading strategies with daily data. Results from real data back-testing for two periods show remarkable returns, even accounting for transaction costs, with annualized Sharpe ratios of 3.9 and 7.2 over the periods June 2013–April 2015 and 2008, respectively. By choosing the particular sector of oil companies, we also confirm the observation that the commodity price is the main driver of the share prices of commodity-producing companies at times of spikes in the related commodity market.     

Islamic equity market integration and volatility spillover between emerging and US stock markets

The purpose of this paper is to study the conditional correlations across the US market and a sample of five Islamic emerging markets, namely Turkey, Indonesia, Pakistan, Qatar, and Malaysia. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic equity index since it applies stringent restrictions to include companies. Indeed, two main restrictions must be met: (i) the business activity must be compliant with Shari’ah (i.e., Islamic law) guidelines and (ii) interest-bearing investments and leverage ratios should not exceed upper limits. Three models are used: multivariate GARCH BEKK, CCC, and DCC. The estimation results of the three models show that the US and Islamic emerging equity markets are weakly correlated over time. No sheer evidence supports that the US market spills over into the Islamic emerging equity markets. Besides interpreting the results in terms of weak market integration, the peculiar specificities of the Islamic finance industry and the admittance conditions to the MSCI Islamic equity index contribute to explaining them. Indeed, Islamic finance bans interest-bearing investments and imposes some rules, such as asset-backing, which has sizeable impacts on volatility spillover and shocks transmissions, alongside with the close linkage between real and financial sectors. These findings suggest that investors should take caution when investing in the Islamic emerging equity markets and diversifying their portfolios in order to minimize risk.