Nonlinear causality testing with stepwise multivariate filtering

This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total Forex trading worldwide. The data covers the period 3/20/1987-11/14/2007, including the Asian crisis, the dot-com bubble and the period just before the outbreak of the US subprime crisis. The objective of the paper is to test for the existence of both linear and nonlinear causal relationships among these currency markets. The modified Baek-Brock test for nonlinear non-causality is applied on the currency return time series as well as the linear Granger test. Further to the classical pairwise analysis causality testing is conducted in a multivariate formulation, to correct for the effects of the other variables. A new stepwise multivariate filtering approach is implemented. To check if any of the observed causality is strictly nonlinear, the nonlinear causal relationships of VAR/VECM filtered residuals are also examined. Finally, the hypothesis of nonlinear non-causality is investigated after controlling for conditional heteroskedasticity in the data using GARCH-BEKK, CCC-GARCH and DCC-GARCH models. Significant nonlinear causal linkages persisted even after multivariate GARCH filtering. This indicates that if nonlinear effects are accounted for, neither FX market leads or lags the other consistently and currency returns may exhibit statistically significant higher-order moments and asymmetries.nonparametric Granger causality; filtering; multivariate GARCH models; spillovers

Comparison of MSACD models

We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition, the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C22, C25, C41, G1

Duration and Order Type Clusters

This paper introduces a new bivariate autoregressive conditional framework (ACD×ACL) for modelling the arrival process of buy and sell orders in a limit order book. The model contains two dynamic components to describe the observed clustering of durations and order types: a duration process to capture the time structure, combined with a new "Autoregressive Conditional Logit" model in order to display the traders' order choice. Both processes are adapted to a common natural filtration and modelled simultaneously. It can be shown that the state of the order book as well as the success and the speed of the matching process have a significant influence on the traders' decisions when and on which side of the market to submit orders and, thus, affect the market's liquidityUltra high frequency transaction data, limit order book, market microstructure, ACD model, dynamic logit model, bivariate point process.

Relation between higher order comoments and dependence structure of equity portfolio

We study a relation between higher order comoments and dependence structure of equity portfolio in the US and UK by relying on a simple portfolio approach where equity portfolios are sorted on the higher order comoments. We find that beta and coskewness are positively related with a copula correlation, whereas cokurtosis is negatively related with it. We also find that beta positively associates with an asymmetric tail dependence whilst coskewness negatively associates with it. Furthermore, two extreme equity portfolios sorted on the higher order comoments are closely correlated and their dependence structure is strongly time varying and nonlinear. Backtesting results of value-at-risk and expected shortfall demonstrate the importance of dynamic modeling of asymmetric tail dependence in the risk management of extreme events

Duration and Order Type Clusters

This paper introduces a new bivariate autoregressive conditional framework (ACD×ACL) for modelling the arrival process of buy and sell orders in a limit order book. The model contains two dynamic components to describe the observed clustering of durations and order types: a duration process to capture the time structure, combined with a new "Autoregressive Conditional Logit" model in order to display the traders' order choice. Both processes are adapted to a common natural filtration and modelled simultaneously. It can be shown that the state of the order book as well as the success and the speed of the matching process have a significant influence on the traders' decisions when and on which side of the market to submit orders and, thus, affect the market's liquidityUltra high frequency, transaction data, limit order book, order aggressiveness, market microstructure, ACD model, dynamic logit model, bivariate point process, survival analysis.

VOLATILITY SPILLOVERS BETWEEN FOREIGN EXCHANGE, COMMODITY AND FREIGHT FUTURES PRICES: IMPLICATIONS FOR HEDGING STRATEGIES

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncertainty for international grain traders. A theoretical model is developed for a representative European importer to depict a realistic trading problem encountered by an international grain trading corporation exposed to more than one type of price risk. The traditional method of estimating hedge ratios by Ordinary Least Squares (OLS) is compared to the Seemingly Unrelated Regression (SUR) and the multivariate GARCH (MGARCH) methodology, which takes into account time-varying variances and covariances between the cash and futures markets. Explicit modeling of the time-variation in futures hedge ratios via the MGARCH methodology, using all derivatives and taking into account dependencies between markets results in a significant reduction in price risk for grain traders. The results also confirm that the unique, but underutilized, freight futures market is a potentially useful mechanism for reducing price uncertainty for international grain traders. The research undertaken in this study provides valuable information about reducing price uncertainty for international grain traders and gives a better understanding of the linkages between closely related markets.hedging, multivariate GARCH, foreign exchange, freight and commodity futures, Financial Economics, International Relations/Trade,

VOLATILITY SPILLOVERS BETWEEN FOREIGN EXCHANGE, COMMODITY AND FREIGHT FUTURES PRICES: IMPLICATIONS FOR HEDGING STRATEGIES

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncertainty for international grain traders. A theoretical model is developed for a representative European importer to depict a realistic trading problem encountered by an international grain trading corporation exposed to more than one type of price risk. The traditional method of estimating hedge ratios by Ordinary Least Squares (OLS) is compared to the Seemingly Unrelated Regression (SUR) and the multivariate GARCH (MGARCH) methodology, which takes into account time-varying variances and covariances between the cash and futures markets. Explicit modeling of the time-variation in futures hedge ratios via the MGARCH methodology, using all derivatives and taking into account dependencies between markets results in a significant reduction in price risk for grain traders. The results also confirm that the unique, but underutilized, freight futures market is a potentially useful mechanism for reducing price uncertainty for international grain traders. The research undertaken in this study provides valuable information about reducing price uncertainty for international grain traders and gives a better understanding of the linkages between closely related markets.hedging, multivariate GARCH, foreign exchange, freight and commodity futures, Marketing, F3, C3, G1,

INVESTIGATING RAPESEED PRICE VOLATILITIES IN THE COURSE OF THE FOOD CRISIS

Multivariate GARCH, MATIF, rapeseed, crude oil, volatilities, food crisis, Demand and Price Analysis, Research Methods/ Statistical Methods, C32, E44, G1, Q11, Q13, Q49,

Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Non linear dynamics in US macroeconomic time series

This paper investigates whether the inherent non stationarity of the US macroeconomic time series may be entirely explained by simple stochastic non linear models (like GARCH). Applying the numerical tools of the analysis of dynamical systems to long time series for the US, we reject the hypothesis that the uncorrelated and homoscedastic residuals of the estimated GARCH models contain no structure. Contrary to the theories that attribute the source of the irregular behaviour of the economic system to erratic factors, we are not able, using GARCH models, to obtain truly random residuals. Given this evidence we put forward the possibility that seemingly but not truly random residuals could be, in principle, better controlled and forecasted in the short run.economics of technology ;