Modelling conditional correlations in the volatility of Asian rubber spot and futures returns

Asia is presently the most important market for the production and consumption of natural rubber. World prices of rubber are subject to not only to changes in demand, but also speculation regarding future markets. Japan and Singapore are the major future markets for rubber, while Thailand is one of the world's largest producers of rubber. As rubber prices are influenced by external markets, it is important to analyse the relationship between the relevant markets in Thailand, Japan and Singapore. The analysis is conducted using several alternative multivariate GARCH models. The empirical results indicate that the constant conditional correlations arising from the CCC model lie in the low to medium range. The results from the VARMA-GARCH model and the VARMA-AGARCH model suggest the presence of volatility spillovers and asymmetric effects of positive and negative return shocks on conditional volatility. Finally, the DCC model suggests that the conditional correlations can vary dramatically over time. In general, the dynamic conditional correlations in rubber spot and futures returns shocks can be independent or interdependent.     

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.     

Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market

In this paper a number of alternative autoregressive conditional duration (ACD) models are compared using a sample of data for three major companies traded on the Australian Stock Exchange. The comparison is performed by employing the methodology for evaluating density and interval forecasts, developed by Diebold et al. [F. Diebold, A. Gunther, S. Tay, Evaluating density forecasts with applications to financial risk management, International Economic Review 39 (1998) 863–883] and Christoffersen [P. Christoffersen, Evaluating interval forecasts, International Economic Review 39 (1998) 841–862], respectively. Our main finding is that the generalized gamma and log-normal distributions for the error terms have similar performance and perform better that the exponential and Weibull distributions. Additionally, there seems to be no substantial difference between the standard ACD specification of Engle and Russel [R. Engle, J. Russell, Autoregressive conditional duration: a new model for irregularly-spaced transaction data, Econometrica 66 (1998) 1127–1162] and the log-ACD specification of Bauwens and Giot [L. Bauwens, P. Giot, The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks, Annales d’Economie et de Statistique 60 (2000) 117–150].     

Volatility dynamics of the US business cycle: A multivariate asymmetric GARCH approach

Most empirical investigations of the business cycles in the United States have excluded the dimension of asymmetric conditional volatility. This paper analyses the volatility dynamics of the US business cycle by comparing the performance of various multivariate generalised autoregressive conditional heteroskedasticity (GARCH) models. In particular, we propose two bivariate GARCH models to examine the evidence of volatility asymmetry and time-varying correlations concurrently, and then apply the proposed models to five sectors of Industrial Production of the United States. Our findings provide strong evidence of asymmetric conditional volatility in all sectors, and some support of time-varying correlations in various sectoral pairs. This has important policy implications for government to consider the effective countercyclical measures during recessions.     

Simplified specifications of a multivariate generalized autoregressive conditional heteroscedasticity model

Recent developments in multivariate volatility modeling suggest that the conditional correlation matrix can be described by a time series recursion, where the total number of parameters grows by the power-of-two of the dimension of financial returns. The power of two computational requirement makes high-dimensional multivariate volatility modeling very time consuming. In this paper, we propose two simplified specifications in a multivariate autoregressive conditional heteroscedasticity model. The first specification computes an unconditional correlation matrix from standardized residuals of the model. The second specification restricts the sum of the weights in a time-varying conditional correlation equation to be one. Applying a Bayesian sampling scheme allows the number of parameters to be reduced from the power of two of the dimension to the linear order of the dimension only and simultaneously provides us a framework for model comparison. We test our simplified specifications using simulated and real data from three sectoral indices in Hong Kong, three market indices and four exchange rates. The results suggest that our simplified specifications are more effective than the original formulation.     

A double-threshold GARCH model of stock market and currency shocks on stock returns

International integration of financial markets provides a channel for currency movements to affect stock prices. This paper applies a four-regime double-threshold GARCH (DTGARCH) model of stock market returns to investigate empirically the effects of daily currency movements on five stock market returns, namely in Taiwan, Singapore, South Korea, Japan and the USA. The asymmetric reactions of the mean and volatility stock returns in five markets to stock market and foreign exchange news are investigated using linear and nonlinear models. We discuss a four-regime DTGARCH model, which allows for asymmetry in both the conditional mean and conditional variance simultaneously by using two threshold variables to analyze stock market reactions to different types of information (that is, positive and negative news) that are generated from stock and foreign exchange markets. By applying the four-regime DTGARCH model, this paper finds that the interactions between the information of stock and foreign exchange markets lead to asymmetric reactions of stock returns and their associated variability. The empirical results show that international fund managers who invest in newly emerging stock markets need to evaluate the value and stability of domestic currencies as part of their stock market investment decisions.     

Block sampler and posterior mode estimation for asymmetric stochastic volatility models

A new efficient simulation smoother and disturbance smoother are introduced for asymmetric stochastic volatility models where there exists a correlation between today's return and tomorrow's volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their conditional posterior distribution. The algorithm is based on the multivariate normal approximation of the conditional posterior density and exploits a conventional simulation smoother for a linear and Gaussian state-space model. The performance of our method is illustrated using two examples: (1) simple asymmetric stochastic volatility model and (2) asymmetric stochastic volatility model with state-dependent variances. The popular single move sampler which samples a state variable at a time is also conducted for comparison in the first example. It is shown that our proposed sampler produces considerable improvement in the mixing property of the Markov chain Monte Carlo chain.     

Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis

Based on intraday 5-min high-frequency dataset, this paper empirically analyzes the intraday dynamic relationships between China’s CSI 300 index futures and spot markets with vector autoregression (VAR) and multivariate GARCH (MGARCH) models. By comparing four VAR–MGARCH models (dynamic conditional correlation, constant conditional correlation, diagonal and BEKK), the VAR–DCC–MGARCH model is found to fit the data the best and be preferred over the other models. The results of this model show that although there are bidirectional price causal relationships between the CSI 300 index futures and spot markets, the index futures return shock affects the spot market more severely than the spot return shock affects the futures market, indicating that the index futures market dominates the price discovery process between the two markets. There are bidirectional volatility spillovers effects between the CSI 300 index futures and spot markets, and the spillovers effects from index futures to spot almost equal to that from index spot to futures. The time-varying conditional correlations between the CSI 300 index futures and spot markets change from 0.4787 to 0.9594 across time, showing there is a strong positive correlation and linkage effect between the two markets. These results indicate that after a period of time of development, the price discovery performance of the CSI 300 index futures market has begun to function well, and the impact of the CSI 300 index futures market on its underlying spot market has strengthened.     

Modeling asymmetric volatility in weekly Dutch temperature data

In addition to clear-cut seasonality in mean and variance, weekly Dutch temperature data appear to have a strong asymmetry in the impact of unexpectedly high or low temperatures on conditional volatility. Furthermore, this asymmetry also shows fairly pronounced seasonal variation. To describe these features, we propose a univariate seasonal time series model with asymmetric conditionally heteroskedastic errors. We fit this (and other, nested) model(s) to 25 years of weekly data. We evaluate its forecasting performance for 5 years of hold-out data and find that the imposed asymmetry leads to better out-of-sample forecasts of temperature volatility.     

GARCH,jumps and permanent and transitory components of volatility: the case of the Taiwan exchange rate

This paper investigates whether there are three distinctive features in financial asset prices, that is, time-varying conditional volatility, jumps and the component factors of volatility. It adopts a component-GARCH-Jump, which can efficiently capture the three features simultaneously. Our results demonstrate that the three features exist in the Taiwan exchange rate. Besides time-varying conditional volatility, our model identifies 172 jumps between 5 January 1988 and 21 March 2003. The empirical evidence shows that the permanent component of the conditional variance is a relatively smooth movement except for a fairly sharp shift which began in 1997. This means that the effect of the Asian crisis shock might very well have exerted not only a transitory jump effect, but also a permanent effect on Taiwan’s exchange rate.     

Modelling and testing for market volatility

Three types of nonlinear models of volatility of market returns based on the conditional variance models and the logistic and cubic versions of chaotic dynamics are critically reviewed here and empirically tested against three types of market indices: value weighted equally weighted and Standard and Poor's return index for the New York Stock Market. Econometric results provide valuable insights into the temporal variations of the conditional variances and skewness of market returns.     

Currency hedging strategies using dynamic multivariate GARCH

This paper examines the effectiveness of using futures contracts as hedging instruments of: (1) alternative models of volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts. For this purpose, daily data of futures and spot exchange rates of three major international currencies, Euro, British pound and Japanese yen, against the American dollar, are used to analyze hedge ratios and hedging effectiveness resulting from using two different maturity currency contracts, near-month and next-to-near-month contract. We estimate four multivariate volatility models (namely CCC, VARMA-AGARCH, DCC and BEKK), and calculate optimal portfolio weights and optimal hedge ratios to identify appropriate currency hedging strategies. The hedging effectiveness index suggests that the best results in terms of reducing the variance of the portfolio are for the USD/GBP exchange rate. The empirical results show that futures hedging strategies are slightly more effective when the near-month future contract is used for the USD/GBP and USD/JPY currencies. Moreover, the CCC and AGARCH models provide similar hedging effectiveness, which suggests that dynamic asymmetry may not be crucial empirically, although some differences appear when the DCC and BEKK models are used.     

A simple multivariate ARCH model specified by random coefficients

This paper provides an alternative formulation of the conditional correlation structure in fitting the multivariate GARCH model. A special case is the multivariate ARCH model with random coefficients. Its coherence structure is derived by the correlations between the random coefficients which play an important role in describing the interested heteroscedastic features. The parameter estimation problem can be solved by maximum likelihood estimation and model selection is via the likelihood ratio test. We consider three real applications: (1) the spot and forward rates of the Deutsche Mark against the US dollars; (2) exchange rates of Deutsche Mark and Japanese Yen against US dollars; (3) the Heng Sang index and SES index.     

On asymmetric generalised t stochastic volatility models

In stochastic volatility (SV) models, asset returns conditional on the latent volatility are usually assumed to have a normal, Student-t or exponential power (EP) distribution. An earlier study uses a generalised t (GT) distribution for the conditional returns and the results indicate that the GT distribution provides a better model fit to the Australian Dollar/Japanese Yen daily exchange rate than the Student-t distribution. In fact, the GT family nests a number of well-known distributions including the commonly used normal, Student-t and EP distributions. This paper extends the SV model with a GT distribution by incorporating general volatility asymmetry. We compare the empirical performance of nested distributions of the GT distribution as well as different volatility asymmetry specifications. The new asymmetric GT SV models are estimated using the Bayesian Markov chain Monte Carlo (MCMC) method to obtain parameter and log-volatility estimates. By using daily returns from the Standard and Poors (S&P) 500 index, we investigate the effects of the specification of error distributions as well as volatility asymmetry on parameter and volatility estimates. Results show that the choice of error distributions has a major influence on volatility estimation only when volatility asymmetry is not accounted for.     

Joint forecasts of Dow Jones stocks under general multivariate loss function

When forecasts are assessed by a general loss (cost-of-error) function, the optimal point forecast is, in general, not the conditional mean, and depends on the conditional volatility—which, for stock returns, is time-varying. In order to provide forecasts of daily returns of 30 DJIA stocks under a general multivariate loss function, the following issues are addressed. We discuss what conditions define a multivariate loss function, and a simple class of such functions is proposed. Based on suitable combinations of univariate losses, the suggested multivariate functions are convenient for practical applications with many variables. To keep the computational aspect tractable, a flexible multivariate GARCH model is employed in estimating the conditional forecast distributions. The model easily copes with large number of series while allowing for skewness, fat tails, non-ellipticity, and tail dependence. Based on Engle’s DCC GARCH, it uses multivariate affine generalized hyperbolic distributions as conditional probability law, and the number of parameters to be estimated simultaneously does not depend on the number of series. The model is fitted using daily data from 2002 to 2007 (keeping data from 2008 for out-of-sample forecasts), and a bootstrap procedure is used to derive point forecasts under several multivariate loss functions of the proposed type.     

A risk map of international tourist regions in Spain

This paper presents a different approach to tourism research at the regional level. Financial econometric techniques are applied to international tourist arrivals, as well as their volatilities, in the five main tourist regions in Spain, using monthly international tourist arrivals during 1997–2007. Univariate time series models are estimated for the conditional means of monthly international tourist arrivals and their volatilities. The estimated conditional volatility models are GARCH(1,1), GJR(1,1) and EGARCH(1,1). Both the second moment and log-moment conditions are calculated to provide diagnostic checks of the estimated models. The conditional mean estimates are generally statistically adequate, and the inferences are valid.     

Several multi-criteria programming methods for classification

In this paper, we propose a general optimization-based model for classification. Then we show that some well-known optimization-based methods for classification, which were developed by Shi et al. [Data mining in credit card portfolio management: a multiple criteria decision making approac. In: Koksalan M, Zionts S, editors. Multiple criteria decision making in the new millennium. Berlin: Springer; 2001. p. 427–36] and Freed and Glover [A linear programming approach to the discriminant problem. Decision Sciences 1981; 12: 68–79; Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 1981; 7: 44–60], are special cases of our model. Moreover, three new models, MCQP (multi-criteria indefinite quadratic programming), MCCQP (multi-criteria concave quadratic programming) and MCVQP (multi-criteria convex programming), are developed based on the general model. We also propose algorithms for MCQP and MCCQP, respectively. Then we apply these models to three real-life problems: credit card accounts, VIP mail-box and social endowment insurance classification. Extensive experiments are done to compare the efficiency of these methods.     

A multivariate threshold stochastic volatility model

We introduce in this paper a multivariate threshold stochastic volatility model for multiple financial return time series. This model allows the dynamic structure of return and volatility to change according to a threshold model while accounting for the interdependence of financial returns. Through the threshold volatility modeling, we can understand the impact of market news on volatility asymmetry. Estimation of unknown parameters are carried out using Markov chain Monte Carlo techniques. Simulations show that our estimators are reliable in moderately large sample sizes. We apply the model to three market indice data and estimate time-varying correlations among the indice returns.     

Cyclic coding algorithms under MorphoSys reconfigurable computing system

This paper introduces reconfigurable computing (RC) and specifically chooses one of the prototypes in this field, MorphoSys (M1) [1], [2], [3], [4], [5]. The paper addresses the results obtained when using RC in mapping algorithms pertaining to digital coding in relation to previous research [6], [7], [8], [9], [10]. The chosen algorithms relate to cyclic coding techniques, namely the CCITT CRC-16 and the CRC-16. A performance analysis study of the M1 RC system is also presented to evaluate the efficiency of the algorithm execution on the M1 system. For comparison purposes, three other systems were used to map the same algorithms showing the advantages and disadvantages of each compared with the M1 system. The algorithms were run on the 8×8 RC (reconfigurable) array of the M1 (MorphoSys) system; numerical examples were simulated to validate our results, using the MorphoSys mULATE program, which simulates MorphoSys operations.