基于互联网金融的网贷利率特征研究

P2P网贷利率是研究中国互联网金融特征的重要切入点。本文在探究中国P2P网贷利率典型化事实和分析P2P网贷利率与传统金融市场利率之间波动溢出机理的基础上,通过运用单元和多元GARCH类模型对P2P网贷利率的典型特征以及与传统金融市场利率的互动关系进行研究。研究结果表明:第一,网贷利率波动具有集聚性、风险累积效应,同时不具备杠杆效应,对利好、利空信息反应大体一致,这意味着网贷市场风险性强而市场参与者风险意识不强。第二,验证了Shibor的基准利率地位,其对网贷利率、中债国债利率都有波动溢出效应,中债国债利率对网贷利率没有波动溢出效应。第三,网贷利率尚处于发展初期,对其他利率的影响作用有限,对Shibor和中债国债利率都没有波动溢出效应。最后根据实证研究结果提出系列针对性建议。     

Multivariate Process Monitoring Using the Dynamic Biplot

In this article, we present a method for monitoring multivariate process data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use reduction to two dimensions for displaying the state of the process but all the data for determining whether it is in a state of statistical control. This approach allows us to detect changes in location, variation, and correlational structure accurately yet display a large amount of information concisely. We illustrate the use of the biplot on an example of industrial data and also discuss some of the issues related to a practical implementation of the method.     

LARGE DEVIATIONS IN MULTIFACTOR PORTFOLIO CREDIT RISK

The measurement of portfolio credit risk focuses on rare but significant large-loss events. This paper investigates rare event asymptotics for the loss distribution in the widely used Gaussian copula model of portfolio credit risk. We establish logarithmic limits for the tail of the loss distribution in two limiting regimes. The first limit examines the tail of the loss distribution at increasingly high loss thresholds; the second limiting regime is based on letting the individual loss probabilities decrease toward zero. Both limits are also based on letting the size of the portfolio increase. Our analysis reveals a qualitative distinction between the two cases: in the rare-default regime, the tail of the loss distribution decreases exponentially, but in the large-threshold regime the decay is consistent with a power law. This indicates that the dependence between defaults imposed by the Gaussian copula is qualitatively different for portfolios of high-quality and lower-quality credits.     

Asymmetric effect of advertising on the Chinese stock market

In this paper, we investigate whether investor attention to advertising has an asymmetric effect on Chinese stock returns by using a multivariate Markov switching model with time-varying regime transition probabilities. Using the Chinese stock market as a setting, we obtain lagged conditional volatility from generalized autoregressive conditional heteroskedasticity (GARCH) for modelling the time-varying transition probabilities of the regime-switching process to capture changes in the market regime. Our evidence documents that the high advertising portfolio does earn higher abnormal return than the low advertising portfolio in low-volatility periods. In high-volatility periods, however, the abnormal return is insignificant when the firm increases advertising spending. Our results support the behavioural model argument that in high-volatility period, advertising information diffuses slowly due to cognitive dissonance. Thus, the effect of advertising on stock returns is asymmetric, and it shows statistical significance in low-volatility periods.     

A change in the time-varying correlation between oil prices and the stock market

We investigate the time-varying correlation between oil prices and stock prices. Estimation results from a multivariate DCC-GARCH model reveal that the correlation has changed since the financial crisis. Historically, the correlation has been close to zero or slightly negative. However, the correlation changed to positive during the Great Recession and continued to be positive through the first half of 2017. We investigate the role quantitative easing played in this change in correlation using a threshold model.     

Evolución de la capacidad tecnológica en México. Aplicación del análisis estadístico multivariante de cluster

This paper conducts an analysis on the existence of state clusters related with technological capabilities in Mexico. An empirical study was conducted using the technique of multivariate statistical cluster analysis, based on the set of indicators proposed by Cepal (2007), collecting data from various public sources country for 2006 and 2012 in order to study the time evolution of such clusters, trying to see what states have been moving to a cluster located in positions more which have advanced and retreated over the period. The results show the existence of 7 groups of states characterized by different technological capabilities, plus states are identified in decline and progress, both in terms of absorptive capacity and innovation, and in relation to the technological infrastructure capabilities.     

Automatic Interpretable Retail forecasting with promotional scenarios

Budgeting and planning processes require medium-term sales forecasts with marketing scenarios. The complexity in modern retailing necessitates consistent, automatic forecasting and insight generation. Remedies to the high dimensionality problem have drawbacks; black box machine learning methods require voluminous data and lack insights, while regularization may bias causal estimates in interpretable models.The proposed FAIR (Fully Automatic Interpretable Retail forecasting) method supports the retail planning process with multi-step-ahead category-store level forecasts, scenario evaluations, and insights. It considers category-store specific seasonality, focal- and cross-category marketing, and adaptive base sales while dealing with regularization-induced confounding.We show, with three chains from the IRI dataset involving 30 categories, that regularization-induced confounding decreases forecast accuracy. By including focal- and cross-category marketing, as well as random disturbances, forecast accuracy is increased. FAIR is more accurate than the black box machine learning method Boosted Trees and other benchmarks while also providing insights that are in line with the marketing literature.     

A Short Introduction to Correlation Markets

This short note gives a short overview of correlation marketsand was prepared for the "Roundtable Discussion on Default RiskCorrelation Models" given at the inaugural SoFiE-Conferencein June 2008.     

Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests

This paper investigates the relationship between stock market volatility and the business cycle in four major economies, namely the US, Canada, Japan and the UK. We employ both linear and nonlinear bivariate causality tests and we further conduct a multivariate analysis to explore possible spillover effects across countries. Our results suggest that there is a bidirectional causal relationship between stock market volatility and the business cycle within each country and additionally reveal that the recent financial crisis plays an important role in this context. Finally, we identify a significant impact of the US on the remaining markets.     

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.