Factor estimation using MCMC-based Kalman filter methods

An exact MCMC-based solution for the Kalman filter with Markov switching and GARCH components is proposed. To motivate the solution, an international equity market model incorporating common Markovian regimes and GARCH residuals in a persistent factor environment is considered. Given the intractable and approximate nature of the model’s likelihood function, a Metropolis-in-Gibbs sampler with Bayesian features is constructed for estimation purposes. To accelerate the drawing procedure, approximations to the conditional density of the common component are also considered. The model is applied to equity data for 18 developed markets to derive global, European, and country-specific equity market factors.     

A fuzzy GARCH model applied to stock market scenario using a genetic algorithm

In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model.The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method.     

A generalized Dynamic Conditional Correlation model for portfolio risk evaluation

We propose a generalization of the Dynamic Conditional Correlation multivariate GARCH model of Engle [R.F. Engle, Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics 20 (2002) 339–350] and of the Asymmetric Dynamic Conditional Correlation model of Cappiello et al.[L. Cappiello, R.F. Engle, K. Sheppard, Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Econometrics 25 (2006) 537–572]. The model we propose introduces a block structure in parameter matrices that allows for interdependence with a reduced number of parameters. Our model nests the Flexible Dynamic Conditional Correlation model of Billio et al. [M. Billio, M. Caporin, M. Gobbo, Flexible dynamic conditional correlation multivariate GARCH for asset allocation, Applied Financial Economics Letters 2 (2006) 123–130] and is named Quadratic Flexible Dynamic Conditional Correlation Multivariate GARCH. In the paper, we provide conditions for positive definiteness of the conditional correlations. We also present an empirical application to the Italian stock market comparing alternative correlation models for portfolio risk evaluation.     

A Fuzzy Asymmetric GARCH model applied to stock markets

In this paper, we derive a new class of flexible threshold asymmetric Generalized Autoregression Conditional Heteroskedasticity (GARCH) models. We use this tool for analysis and modeling of the properties that are apparent in many financial time series. In general, the transmission of volatility in the stock market is time-varying, nonlinear, and asymmetric with respect to both positive and negative results. Given this fact, we adopt the method of fuzzy logic systems to modify the threshold values for an asymmetric GARCH model. Our simulations use stock market data from the Taiwan weighted index (Taiwan), the Nikkei 225 index (Japan), and the Hang Seng index (Hong Kong) to illustrate the performance of our proposed method. From the simulation results, we have determined that the forecasting of volatility performance is significantly improved if the leverage effect of clustering is considered along with the use of expert knowledge enabled by the GARCH model.     

Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization

Fluctuations in the stock market follow the principle of volatility clustering in which changes are cataloged by similarity; as such, large changes tend to follow large changes, and small changes tend to follow small changes. This clustering is one of the major reasons why many generalized autoregression conditional heteroscedasticity (GARCH) models do not forecast the stock market well. In this paper, an adaptive Fuzzy-GARCH model with particle swarm optimization (PSO) is proposed to solve this problem.The adaptive Fuzzy-GARCH model refers to both GARCH models and the parameters of membership functions, which are determined by the characteristics of market itself. Here, we present an iterative algorithm based on PSO to estimate the parameters of the membership functions. The PSO method aims to achieve a global optimal solution with a rapid convergence rate. The three stock markets of Taiwan, Japan, and Germany were analyzed to illustrate the performance of the proposed method.     

Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market

The problem of the identification of dependencies between time series of equity returns is analyzed. Marginal distribution functions are assumed to be known, and a bivariate chi-square test of fit is applied in a fully parametric copula approach. Several marginal models and families of copulas are fitted and compared with Spanish stock market data. The results show the difficulty in adjusting the bivariate distribution of raw returns, and highlight the effect of a GARCH filtering in the selection of the best fitting copula.     

A Student-<Emphasis Type="Italic">t</Emphasis> Full Factor Multivariate GARCH Model

We extend the full-factor multivariate GARCH model of Vrontos et al. (Econom J 6:312–334, 2003a) to account for fat tails in the conditional distribution of financial returns, using a multivariate Student-t error distribution. For the new class of Student-t full factor multivariate GARCH models, we derive analytical expressions for the score, the Hessian matrix and the Information matrix. These expressions can be used within classical inferential procedures in order to obtain maximum likelihood estimates for the model parameters. This fact, combined with the parsimonious parameterization of the covariance matrix under the full factor multivariate GARCH models, enables us to apply the models in high dimensional problems. We provide implementation details and illustrations using financial time series on eight stocks of the US market.     

The financial-economic crisis and value of equity capital: A case study of Slovenian public limited companies 2006–2011

The purpose of this paper is to discuss the effect of financial-economic crisis on the equity value of companies, as well as present the importance of fair and honest company valuations. The fundamental value of equity capital of a company is important for both management and external shareholders. The wide disparity between market and fundamental values can lead to high value adjustments, which reduces investors confidence in the capital market. This has had a negative impact on the operations of financial institutions, and individual as well as company investment; especially on developing financial markets during a financial-economic crisis. This research was designed to assess the equity value of Slovenian public limited companies based on the discounted free cash flows to equity and comparing it with market value of equity capital of companies before and during the financial-economic crisis. The fundamental value of equity capital of the selected companies (sample of 25) is calculated using a two-tiered model. The paired-sample t-tests method rejected the hypothesis that the fundamental value of equity capital of Slovenian public limited companies better reflects the market value of equity capital in today’s times of financial-economic crisis (2011) than before the crisis (2006). However, we found that the market value of equity capital in relation to the fundamental value of equity capital of the selected companies was lower in 2011 than in 2006. Various models of the basic calculations are used in the model evaluation. This study shows the problem of company valuation on small and emerging capital markets which have a short history of data.     

BP算法和对称ARCH类模型对股市波动性预测的实证比较

利用我国深圳股票市场的实际数据,建立了相应的BP算法网络预测模型和ARCH(1),GARCH(1,1)预测模型,分别用来对深成指数每个周末收盘价的波动性进行预测．研究表明,BP算法对样本外观测值的上凸曲线拟合得较好,对下凸曲线的拟合效果较差;ARCH(1)和GARCH(1,1)则反之,其预测曲线对样本外观测值的下凸曲线拟合效果都较好,但对上凸曲线的拟合效果都较差．通过采用6种常用的预测误差统计量:平均误差、平均绝对误差、均方根误差、平均绝对比率误差、Akaike信息准则、Baves信息准则对样本外数据的预测结果进行检验,BP算法的预测效果最好,ARCH(1)模型次之,GARcH(1,1)模型偏差．     

基于GARCH模型的网络新闻与舆情的波动性分析

网络新闻产生的舆情波动一般具有异方差特征,难以用普通模型拟合。由诺贝尔经济学奖获得者恩格尔教授提出的条件异方差(GARCH)模型在分析证券价格波动性方面获得极大成功。本文利用GARCH模型分析网络新闻与舆情的波动性,通过典型事件的舆情采集,分析数据的特征。研究表明,网络新闻与舆情的波动性符合GARCH模型的特征,通过参数的调整和检验,可以实现模型与数据的良好拟合。     

Intra-daily information of range-based volatility for MEM-GARCH

Conventional GARCH modeling formulates an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance, without much consideration on the effects of intra-daily data. Using Engle’s multiplicative-error model (MEM) formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The performances of these different approaches for two 8-year market data sets: the S&P 500 and the NASDAQ composite index, are studied and compared. The impact of significant changes in intraday data has been found to reflect in the MEM-GARCH volatility. For some frameworks it is also possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance estimation.     

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

This paper proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process.     

Locating a low-level waste disposal site

A model is developed to locate a waste disposal site for low-level (domestic and nontoxic industrial) waste. Account is taken of nuisance caused to population along routes and of equity considerations. Not only is equity as regards effects on different population centres considered, but also between carriers of waste from different towns giving rise to the concept of routing fairness. The resulting model problem is computationally complex and heuristic solution procedures are developed. These are applied to real data relating to the district of Algarve, Portugal, and results of numerical experiments are reported.     

Modeling spot price dependence in Australian electricity markets with applications to risk management

We examine the dependence structure of electricity spot prices across regional markets in Australia. One of the major objectives in establishing a national electricity market was to provide a nationally integrated and efficient electricity market, limiting market power of generators in the separate regional markets. Our analysis is based on a GARCH approach to model the marginal price series in the considered regions in combination with copulae to capture the dependence structure between the marginals. We apply different copula models including Archimedean, elliptical and copula mixture models. We find a positive dependence structure between the prices for all considered markets, while the strongest dependence is exhibited between markets that are connected via interconnector transmission lines. Regarding the nature of dependence, the Student-t copula provides a good fit to the data, while the overall best results are obtained using copula mixture models due to their ability to also capture asymmetric dependence in the tails of the distribution. Interestingly, our results also suggest that for the four major markets, NSW, QLD, SA and VIC, the degree of dependence has decreased starting from the year 2008 towards the end of the sample period in 2010. Examining the Value-at-Risk of stylized portfolios constructed from electricity spot contracts in different markets, we find that the Student-t and mixture copula models outperform the Gaussian copula in a backtesting study. Our results are important for risk management and hedging decisions of market participants, in particular for those operating in several regional markets simultaneously.     

绿色供应链的投贷联动融资决策均衡

以一个零售商和一个资金约束的制造商组成的绿色供应链为研究对象,构建了制造商资金约束情况下银行借贷融资、股权融资和组合融资时的融资模型,探讨制造商的资金水平和消费者的绿色偏好对定价、绿色投入水平、产品的市场需求和融资方式选择策略的影响。最后,通过数值分析进行验证。研究发现：消费者绿色偏好与零售价格、批发价格和绿色投入努力水平、产品的市场需求和股权融资时双方可接受的股权转让比例正相关;在制造商资金约束而不融资时,制造商初始资金水平与制造商的批发价格、零售商的零售价格负相关,而与绿色投入努力水平正相关;银行借贷利率越大,制造商的可接受的股权转让比例增大。当制造商进行融资时, 若初始资金水平极低, 选择股权融资的方式进行融资, 随着初始资金水平的上升, 零售商应该选择银行贷款的方式进行融资;在股权融资比例达到一定值时,组合融资比银行借贷更加有利于制造商;而单纯股权融资比组合融资更有利。     

Multiple criterion function goal programming applied to managerial compensation planning

In this paper a goal programming model is applied to the problem in wage and salary administration of balancing internal concerns for equity against eternal market prices in the design of managerial compensation structures. Instead of solving the goal program in a lexicographic fashion, each pre-emptive level is used to define a separate criterion function. Then an ideal point/Tchebycheff metric approach is used to explore the space of tradeoffs among the different goal levels until a final solution is obtained.     

Comparison of nonnested asymmetric heteroskedastic models

The GJR-GARCH model is a popular choice among nonlinear models of the well-known asymmetric volatility phenomenon in financial market data. However, recent work employs double threshold nonlinear models to capture both mean and volatility asymmetry. A Bayesian model comparison procedure is proposed to compare the GJR-GARCH with various double threshold GARCH specifications, by designing a reversible jump Markov chain Monte Carlo algorithm. A simulation experiment illustrates good performance in estimation and model selection over reasonable sample sizes. In a study of seven markets strong evidence is found that the DTGARCH, with US market news as threshold variable, outperforms the GJR-GARCH and traditional self-exciting DTGARCH models. This result was consistent across six markets, excluding Canada.     

Risk modelling and management: An overview

The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on “Risk Modelling and Management” (RMM2011). The papers cover the following topics: currency hedging strategies using dynamic multivariate GARCH, risk management of risk under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes via Gaussian mixture models, GFC-robust risk management under the Basel Accord using extreme value methodologies, volatility spillovers from the Chinese stock market to economic neighbours, a detailed comparison of Value-at-Risk estimates, the dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value-at-risk with a duration-based POT method, and extreme market risk and extreme value theory.     

An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models

This article studies monthly volatility forecasting for the copper market, which is of practical interest for various participants such as producers, consumers, governments, and investors.Using data from 1990 to 2016, we propose a framework composed of a set of time series models such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), non-parametric models from soft computing, e.g. Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS), and hybrid specifications of both. The adaptability characteristic of these models in exogenous variables, their configuration parameters and window size, simultaneously, are provided by a Genetic Algorithm in pursuit of achieving the best possible forecasts. Also, recognized drivers of this specific market are considered.We examine out-of-sample performance based on Heteroskedasticity-adjusted Mean Squared Error (HMSE), and we test model superiority using the Model Confidence Set (MCS). The results show that making forecasts using an adaptive technique is crucial to obtaining robust and improved performance. The Adaptive-GARCH–FIS specification yielded the best forecasting power.