The specification of conditional expectations

This paper explores different specifications of conditional expectations. The most common specification, linear least squares, is contrasted with nonparametric techniques that make no assumptions about the distribution of the data. Nonparametric regression is successful in capturing some nonlinearities in financial data, in particular, asymmetric responses of security returns to the direction and magnitude of market returns. The technique is ideally suited for empirically modeling returns of securities that have complicated embedded options. The conditional mean and variance of the NYSE market return are also examined. Forecasts of market returns are not improved with the nonparametric techniques which suggests that linear conditional expectations are a reasonable approximation in conditional asset pricing research. However, the linear model produces a disturbing number of negative expected excess returns. My results also indicate that the relation between the conditional mean and variance depends on the specification of the conditional variance. Furthermore, a linear model relating mean to variance is rejected and these tests are not sensitive to the expectation generating mechanism nor the conditioning information. Rejections are driven by the distinct countercyclical variation in the ratio of the conditional mean to variance.     

Unconditional distributions obtained from conditional specification models with applications in risk theory

Bivariate distributions, specified in terms of their conditional distributions, provide a powerful tool to obtain flexible distributions. These distributions play an important role in specifying the conjugate prior in certain multi-parameter Bayesian settings. In this paper, the conditional specification technique is applied to look for more flexible distributions than the traditional ones used in the actuarial literature, as the Poisson, negative binomial and others. The new specification draws inferences about parameters of interest in problems appearing in actuarial statistics. Two unconditional (discrete) distributions obtained are studied and used in the collective risk model to compute the right-tail probability of the aggregate claim size distribution. Comparisons with the compound Poisson and compound negative binomial are made.     

Estimation of conditional asset pricing models with integrated variables in the beta specification

We introduce a methodology which deals with possibly integrated variables in the specification of the betas of conditional asset pricing models. In such a case, any model which is directly derived by a polynomial approximation of the functional form of the conditional beta will inherit a nonstationary right hand side. Our approach uses the cointegrating relationships between the integrated variables in order to maintain the stationarity of the right hand side of the estimated model, thus, avoiding the issues that arise in the case of an unbalanced regression. We present an example where our methodology is applied to the returns of funds-of-funds which are based on the Morningstar mutual fund ranking system. The results provide evidence that the residuals of possible cointegrating relationships between integrated variables in the specification of the conditional betas may reveal significant information concerning the dynamics of the betas.     

Statistical tests of distributional scaling properties for financial return series

Existing empirical evidence of distributional scaling in financial returns has helped motivate the use of multifractal processes for modelling return processes. However, this evidence has relied on informal tests that may be unable to reliably distinguish multifractal processes from other related classes. The current paper develops a formal statistical testing procedure for determining which class of fractal process is most consistent with the distributional scaling properties in a given sample of data. Our testing methodology consists of a set of test statistics, together with a model-based bootstrap resampling scheme to obtain sample p-values. We demonstrate in Monte Carlo exercises that the proposed testing methodology performs well in a wide range of testing environments relevant for financial applications. Finally, the methodology is applied to study the scaling properties of a data-set of intraday equity index and exchange rate returns. The empirical results suggest that the scaling properties of these return series may be inconsistent with purely multifractal processes.     

Forecasting financial time-series using data mining models: A simulation study

In this paper, we examine the static and dynamic predictive ability of artificial neural networks and random forests for financial time series within a simulation context. Our simulation design, in which we generate data from an AR(1)-GARCH(1,1) model, allows for several degrees of persistence in the mean equation to mimic the behavior of short and long-horizon asset returns. While the true data generating process beats the data mining techniques in terms of static forecasting, the novelty in this paper is to demonstrate that the data mining techniques outperform the true model under a dynamic forecasting scheme for moderate to highly persistent time series. We provide an empirical application using one-day and long-horizon returns on two exchange rates. Our empirical findings corroborate our simulation results in that the data mining models exhibit superior predictive ability for highly persistent time series. We discuss the importance of our findings for asset allocation and portfolio management.     

Performance of information criteria for selection of Hawkes process models of financial data

We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike’s information criterion, the Bayesian information criterion and the Hannan–Quinn criterion. Since we work with simulated data, we are able to measure the performance of model selection by the success rate of the IC in selecting the model that was used to generate the data. In particular, we are interested in the relation between correct model selection and underlying sample size. The analysis includes realistic sample sizes and parameter sets from recent literature where parameters were estimated using empirical financial intra-day data. We compare our results to theoretical predictions and similar empirical findings on the asymptotic distribution of model selection for consistent and inconsistent IC.     

Testing conditional factor models: A nonparametric approach

Some recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.     

The day-of-the-week effect and conditional volatility: Sensitivity of error distributional assumptions

We test for reliable evidence of the day-of-the-week effect on both the mean and volatility for the S&P/TSX Canadian return index. Unlike previous studies, we permit several specifications for the error distribution — GARCH normal, Student's t, generalized error distribution, and double exponential distribution. Unlike other studies, we find that the day-of-the-week effect in both mean and conditional volatility is sensitive to the particular specification of the underlying distributions. We also find that using a regression analysis assuming a Student's t distribution is a better way to investigate this effect. Our evidence demonstrates the apparent fragility of previous empirical studies on calendar anomalies. Thus, our results serve as a warning that with financial data, the error distributional assumptions are critical to correctly identifying empirical regularities in the data.     

Multivariate extremes for models with constant conditional correlations

Models with constant conditional correlations are versatile tools for describing the behavior of multivariate time series of financial returns. Mathematically speaking, they are solutions of a special class of stochastic recurrence equations (SRE). The extremal behavior of general solutions of SRE has been studied in detail by Kesten [Kesten, H., 1973. Random difference equations and renewal theory for products of random matrices. Acta Mathematica 131, 207–248] and Perfekt [Perfekt, R., 1997. Extreme value theory for a class of Markov chains with values in ^d. Advances in Applied Probability 29, 138–164]. The central concept to understanding the joint extremal behavior of such multivariate time series is the multivariate regular variation spectral measure. In this paper, we propose an estimator for the spectral measure associated with solutions of SRE and prove its consistency. Our estimator is the tail empirical measure of the multivariate time series. Successful use of the estimator depends on a good choice of k, the number of upper order statistics contributing to the empirical measure. We introduce a new criteria for the choice of k based on a scaling property of the spectral measure. We investigate the performance of our estimation technique on exchange rate time series from HFDF96 data set. The estimated spectral measure is used to calculate probabilities of joint extreme returns and probabilities of large movements in an exchange rate conditional on the occurrence of extreme returns in another exchange rate. We find a high level of dependence between the extreme movements of most of the currencies in the EU. We also investigate the changes in the level of dependence between the extreme returns of pairs of currencies as the sampling frequency decreases. When at least one return is extreme, a strong dependence between the components is present already at the 4-hour level for most of the European currencies.     

XBRL and open data for global financial ecosystems: A linked data approach

Information professionals performing business activity related investigative analysis must routinely associate data from a diverse range of Web based general-interest business and financial information sources. XBRL has become an integral part of the financial data landscape. At the same time, Open Data initiatives have contributed relevant financial, economic, and business data to the pool of publicly available information on the Web but the use of XBRL in combination with Open Data remains at an early state of realisation. In this paper we argue that Linked Data technology, created for Web scale information integration, can accommodate XBRL data and make it easier to combine it with open datasets. This can provide the foundations for a global data ecosystem of interlinked and interoperable financial and business information with the potential to leverage XBRL beyond its current regulatory and disclosure role. We outline the uses of Linked Data technologies to facilitate XBRL consumption in conjunction with non-XBRL Open Data, report on current activities and highlight remaining challenges in terms of information consolidation faced by both XBRL and Web technologies.     

Persistency of financial distress amongst Italian households: Evidence from dynamic models for binary panel data

This paper builds on existing studies on households’ financial distress and provides new evidence on the determinants of financial hardship in Italy and its persistence over time. It suggests a quantitative definition of financial distress based on the distribution of net wealth, and tests whether the probability of experiencing financial difficulty is persistent over time, using (random and fixed effects) dynamic models for binary panel data. The analysis exploits the longitudinal component of the Bank of Italy Survey on Household Income and Wealth for the period 1998–2006. Its results show that, after accounting for unobserved heterogeneity, past values of the outcome variable play a large part in explaining the probability of experiencing financial distress. In addition, the probability of financial vulnerability decreases with income and greater sophistication of the household portfolio and, at least in one of the model specifications, increases in areas with higher unemployment rates.     

中国金融市场信用风险模型研究与应用

文章运用中国金融市场和原银行信贷登记系统数据及人民银行组织的信用评级等数据资源,在金融工程理论和技术方法创新的基础上,借鉴国际信用风险模型中违约模式代表——KMV模型原理,实证建立由判别函数和违约强度共同构成的中国金融市场违约预警模型;借鉴国际信用风险模型中盯市模式代表——CreditMetrics模型原理,使用蒙特卡罗模拟方法实证建立中国金融市场信用组合计量模型;探索这两类模型在中国信贷市场、外汇市场、货币市场和债券市场风险管理实务中的应用;并在此基础上提出了政策性建议。     

Multivariate tests of financial models: A new approach

A variety of financial models are cast as nonlinear parameter restrictions on multivariate regression models, and the framework seems well suited for empirical purposes. Aside from eliminating the errors-in-the-variables problem which has plagued a number of past studies, the suggested methodology increases the precision of estimated risk premiums by as much as 76%. In addition, the approach leads naturally to a likelihood ratio test of the parameter restrictions as a test for a financial model. This testing framework has considerable power over past test statistics. With no additional variable beyond β, the substantive content of the CAPM is rejected for the period 1926–1975 with a significance level less than 0.001.     

Bayesian estimation of financial models

This paper outlines a general methodology for estimating the parameters of financial models commonly employed in the literature. A numerical Bayesian technique is utilised to obtain the posterior density of model parameters and functions thereof. Unlike maximum likelihood estimation, where inference is only justified in large samples, the Bayesian densities are exact for any sample size. A series of simulation studies are conducted to compare the properties of point estimates, the distribution of option and bond prices, and the power of specification tests under maximum likelihood and Bayesian methods. Results suggest that maximum–likelihood–based asymptotic distributions have poor finite–sampleproperties.     

Big data tools for Islamic financial analysis

Behavioural science states that emotions, principles and the manner of thinking can affect the behaviour of individuals and even investors in their decision making on financial markets. In this paper, we have tried to measure the investor sentiment by three means of big data. The first is based on a search query of a list of words related to Islamic context. The second is inferred from the engagement degree on social media. The last measure of sentiment is built, based on the Twitter API classified into positive and negative directions by a machine learning algorithm based on the naive Bayes method. Then, we investigate whether these sensations and emotions have an impact on the market sentiment and the price fluctuations by means of a vector autoregression model and Granger causality analysis. In the final step, we apply the agent‐based simulation by means of the sequential Monte Carlo method with the control of our Twitter measure on Islamic index returns. We show, then, that the three social media sentiment measures present a remarkable impact on the contemporaneous and lagged returns of the different Islamic assets studied. We also give an estimation of the parameters of the latent variables relative to the agent model studied.     

A simple non-linear model with fractional integration for financial time series data

This paper provides several examples of simple non-linear time series models with fractionally integrated disturbances. Both types of models (non-linear and fractional integration) have been widely used in recent years when modeling financial data. We use a testing procedure that permits us to test the order of integration in raw time series in the context of non-linear models. The tests are applied to several financial time series, the results showing that when the non-linear sign structure is taken into account, the order of integration of the series is much higher than one, finding thus conclusive evidence against mean reversion in their behavior.     

Positivity constraints on the conditional variances in the family of conditional correlation GARCH models

In this article, we derive a set of necessary and sufficient conditions for positivity of the vector conditional variance equation in multivariate GARCH models with explicit modelling of conditional correlation. These models include the constant conditional correlation GARCH model of Bollerslev [1990. Review of Economics and Statistics 72, 498–505] and its extensions. Under the new conditions, it is possible to introduce negative volatility spillovers in the model. An empirical example illustrates usefulness of having such conditions in practice.     

Automated explanation of financial data

We describe a methodology for explanation generation in financial knowledge-based systems. This offers the possibility to generate explanations and diagnostics automatically to support business decision tasks. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from financial data and models. A multistep look-ahead algorithm is proposed that deals with so-called cancelling-out effects, which are a common phenomenon in financial data sets. Our method is an extension of the traditional variance decomposition in accounting. The method was tested on a case-study conducted for Statistics Netherlands involving the comparison of financial figures of firms in the Dutch retail branch. Copyright © 2009 John Wiley & Sons, Inc.     

A review of consumer decision-making models and development of a new model for financial services

It is recognised that existing theories of Consumer Decision Making (CDM) are not well suited for financial services and there have been calls for development of a new conceptual model. This article reviews prominent models of CDM and identifies strengths and limitations. A new conceptual model that is applicable to financial services is developed. An important element of the model is the recognition that the components interact rather than a consumer following a linear progression through a series of stages. The new model better reflects the iterative decision-making process relevant to financial services and enhances marketers’ understanding of the process and thus their ability to influence it to increase the likelihood of positive outcomes for all. The model has three main components: inputs, processes and outcomes. Inputs include the purchase situation (contextual and environmental variables), consumer characteristics (psychological and social influences) and information sources (marketing mix and interpersonal). Processes include need arousal, information utility, criteria development and evaluation of alternatives. Outcomes include the decision (that may be to abort the purchase), the purchase itself and post-decision evaluation. Further research is required to test the relationships between the variables in different contexts, and thus enable refinement and/or validation of the model.