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.     

Public news announcements and quoting activity in the Euro/Dollar foreign exchange market

The effect of public news announcements on dealers’ quoting activity is analyzed with the multivariate double autoregressive conditional Poisson model. Quoting activity is measured by the frequency of price revisions in the Euro/Dollar foreign exchange market. The multivariate double autoregressive conditional Poisson model is designed for time series of count data. It is based on the double Poisson distribution, which can be both over- and underdispersed. The main findings are first a significant interaction between dealers’ quoting activity, which confirms hot potato trading. Second, news announcements have a different impact on the quoting activity of different banks. Third, impulse-response functions to news announcements show the dynamic nature of the reaction to these news releases.     

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.     

Forecasting conditional correlations in stock,bond and foreign exchange markets

The paper forecasts conditional correlations between three classes of international financial assets, namely stock, bond and foreign exchange. Two countries are considered, namely Australia and New Zealand. Forecasting will be conducted using three multivariate GARCH models, namely the CCC model [T. Bollerslev, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model, Rev. Econ. Stat. 72 (1990) 498–505], VARMA-GARCH model [S. Ling, M. McAleer, Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory 19 (2003) 280–310], and VARMA-AGARCH model [M. McAleer, S. Hoti, F. Chan, Structure and asymptotic theory for multivariate asymmetric volatility, Econometric Rev., in press]. A rolling window technique is used to forecast 1-day ahead conditional correlations. To evaluate the impact of model specification on conditional correlations forecasts, this paper calculates and compares the correlations between conditional correlations forecasts resulted from the three models. The paper finds the evidence of volatility spillovers and asymmetric effect of negative and positive shock on the conditional variance in most pairs of series. However, it suggests that incorporating volatility spillovers and asymmetric do not contribute to better conditional correlations forecasts.     

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.     

The dynamic relations among return volatility,trading imbalance,and trading volume in futures markets

Trading imbalances reflect the quality of market information and may contain more information than the number of trades or trading volume. In order to better understand how trading imbalances play a role different from traditional variables (i.e., number of trades and trading volume) in explaining volatility, we use intraday data to examine the dynamic relations among return volatility, trading imbalances, and traditional variables for E-mini S&P 500 futures and Japanese Yen futures contracts, respectively. The Granger-causality tests indicate strong feedback effects between volatility and trading variables, confirming the information-based and hedging-based trading. We also compare the results of the traditional volumes and trading imbalances through variance decomposition and impulse responses analysis. It is shown that the sequential arrival of private information through trading imbalance is more important in explaining return volatility than the traditional variables, which are a proxy for the public information.     

A class of nonlinear stochastic volatility models and its implications for pricing currency options

A class of stochastic volatility (SV) models is proposed by applying the Box-Cox transformation to the volatility equation. This class of nonlinear SV (N-SV) models encompasses all standard SV models, including the well-known lognormal (LN) SV model. It allows to empirically compare and test all standard specifications in a very convenient way and provides a measure of the degree of departure from the classical models. A likelihood-based technique is developed for analyzing the model. Daily dollar/pound exchange rate data provide some evidence against LN model and strong evidence against all the other classical specifications. An efficient algorithm is proposed to study the economic importance of the proposed model on pricing currency options.     

基于消息的汇率趋势预测的数据挖掘方法

在目前,汇率预测的方法一般采用人工神经网络和贝叶斯估计方法,但这些方法一般都是基于汇率数据本身进行的。然而汇率的变动实际上是由政治、经济、心理等因素造成的。因此,利用这些新闻消息应该更具有可行性。该文讨论了如何根据新闻消息利用贝叶斯网络来进行汇率的趋势预测,分析了如何才能改进预测精度。同时还实现了一个寻找全局最优贝叶斯网络的选择算法。     

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.     

Bayes网络在汇率趋势预测中的应用

根据汇率变化特点,设计了一个用贝叶斯网络方法建立的基于消息的汇率预测模型和算法,在实际应用中得到了较高的预测准确度。     

What Drives Short Rate Dynamics? A Functional Gradient Descent Approach

Functional gradient descent (FGD), a recent technique coming from computational statistics, is applied to the estimation of the conditional moments of the short rate process with the goal of finding the main drivers of the drift and volatility dynamics. FGD can improve the accuracy of some reasonable starting estimates obtained using classical short rate models introduced in the literature. It exploits the predictive information of an enlarged set of variables, including yields at other maturities, time, and macroeconomic indicators. Fitting this methodology to the time series of monthly US 3-month Treasury bill rates, we find that the drift dynamics react mostly in a non-linear way to changes in macroeconomic variables, whereas volatility dynamics are subjected to time-dependent regime-switches. Finally we show the superior performance of the final predictions obtained by applying FGD in a forecasting exercise.     

Goodness-of-fit test for interest rate models: An approach based on empirical processes

A new test for the goodness of fit of parametric forms of the drift and volatility functions of interest rate models is proposed. The test is based on a marked empirical process of the residuals. More specifically, a marked empirical process is constructed using estimators of the integrated regression function and the integrated conditional variance function for the drift function and the volatility function, respectively. Distributions of these processes are approximated using bootstrap techniques. This test is then applied to simulated classical financial models and is illustrated in an empirical application to a EURIBOR data set.     

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.     

An ontology for causal relationships between news and financial instruments

In recent years, people have begun to pay more and more attention to the effect of news on financial instrument markets (i.e., the markets for trading financial instruments). Researchers in the financial domain have conducted many studies demonstrating the effect of different types of news on trade activities in financial instrument markets such as volatility in trade price, trade volume, trading frequency, and so on. In this paper, an ontology for knowledge about news regarding financial instruments is provided. The ontology contains two parts: the first part presents a hierarchy framework for the domain knowledge that primarily includes classes of news, classes of financial instrument markets participants, classes of financial instruments, and primary relations between these classes. In the second part, a causal map is used to demonstrate how classes of news are causally related with classes of financial instruments. Finally, a case concerning the “9/11 American terror attack” is analyzed. On the basis of the ontology, it is first comprehensive to understand the knowledge about news in financial instrument markets; second, it helps building trading models based on news in the financial instrument markets; third, systems (e.g., systems for prediction of stock price based on news, systems for supporting financial market participants to search relevant news) design and development in this domain are facilitated and supported by this ontology.     

International portfolio optimisation with integrated currency overlay costs and constraints

International financial portfolios can be exposed to substantial risk from variations of the exchange rates between the countries in which they hold investments. Nonetheless, foreign exchange can both generate extra return as well as loss to a portfolio, hence rather than just being avoided, there are potential advantages to well-managed international portfolios. This paper introduces an optimisation model that manages currency exposure of a portfolio through a combination of foreign exchange forward contracts, thereby creating a “currency overlay” on top of asset allocation. Crucially, the hedging and transaction costs associated with holding forward contracts are taken into account in the portfolio risk and return calculations. This novel extension of previous overlay models improves the accuracy of the risk and return calculations of portfolios. Consequently, more accurate investment decisions are obtained through optimal asset allocation and hedging positions. Our experimental results show that inclusion of such costs significantly changes the optimal decisions. Furthermore, effects of constraints related to currency hedging are examined. It is shown that tighter constraints weaken the benefit of a currency overlay and that forward positions vary significantly across return targets. A larger currency overlay is advantageous at low and high return targets, whereas small overlay positions are observed at medium return targets. The resulting system can hence enhance intelligent expert decision support for financial managers.     

A variance frontier model of market volatility: an empirical application

Two variance frontier models are proposed and empirically estimated here to test market volatility. By measuring market volatility by temporal variances, the impact of skewness and asymmetry is directly estimated over monthly return data on several market indices over two subperiods: January 1965–December 1974 and Auguast 1982–December 1991. The empirical applications tend to provide strong support to the asymmetrical impact of skewness on variance and also the persistence of market volatility.     

Clarifying the dynamics of the relationship between option and stock markets using the threshold vector error correction model

This work examines how the option and stock markets are related when using the threshold vector error correction model (hereinafter referred to as threshold VECM). Moreover, compared to previous studies in the literature of application of threshold models, this study not only investigates the impacts of price transmission mechanisms on stock return means but also the volatilities of returns. The model is tested using the U.S. S&P 500 stock market. The empirical findings of this investigation are consistent with the following notions. First, the equilibrium re-establishment process depends primarily on the option market and is triggered only when price deviations exceed a critical threshold. Second, arbitrage behaviors between the option and stock markets increase volatility in these two markets and reduce their correlation.     

跳跃扩散股价的最优投资组合选择

假定股票价格服从跳跃扩散过程.在传统均值-方差组合投资模型基础上,最大化最终收益的期望及最小化最终财富的方差.引进一个随机线性二次最优控制问题作为原问题的近似问题.证明了一个状态为跳跃扩散过程的一般最优控制问题的验证性定理.应用验证性定理求解HJB(Hamilton-Jacobi-Bellman)方程得到了原问题的最优策略.最后还给出了原问题有效前沿的表达式.     

A Dimension Reduction Shannon-Wavelet Based Method for Option Pricing

We present a robust and highly efficient dimension reduction Shannon-wavelet method for computing European option prices and hedging parameters under a general jump-diffusion model with square-root stochastic variance and multi-factor Gaussian interest rates. Within a dimension reduction framework, the option price can be expressed as a two-dimensional integral that involves only (i) the value of the variance at the terminal time, and (ii) the time-integrated variance process conditional on this value. A Shannon wavelet inverse Fourier technique is developed to approximate the conditional density of the time-integrated variance process. Furthermore, thanks to the excellent approximation properties of Shannon wavelets, the overall pricing procedure is reduced to the evaluation of just a single integral that involves only the density of the terminal variance value. This single integral can be accurately evaluated, since the density of the variance at the terminal time is known in closed-form. We develop sharp approximation error bounds for the option price and hedging parameters. Numerical experiments confirm the robustness and impressive efficiency of the method.     

汇率趋势预测数据挖掘的数据预处理方法

要用数据挖掘的方法得到有关汇率变动的趋势预测,其首要任务就是对这些汇市消息摘要进行一定的数据预处理,将其转换成具有一定结构的、有利于数据挖掘方法实现的目标语言。该文就是一个这样的对汇市消息摘要的预处理方法,它对有关汇市消息摘要的领域知识进行了详细的分析,得出了相应的领域规则知识,并对基于TRIE索引树的分析词典机制犤2犦加以改进,建立了一定的相关算法,从而实现了从汇市消息摘要到Bayes语言的数据挖掘的数据预处理。