LINEAR RELATIONSHIP BETWEEN THE AUD/USD EXCHANGE RATE AND THE RESPECTIVE STOCK MARKET INDICES: A COMPUTATIONAL FINANCE PERSPECTIVE

In the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May 2011 is considered in this research. Utilizing a set of statistical and computational intelligence techniques, the research establishes that the AUD/USD exchange rate is best estimated by a linear forecast model compared with the nonlinear and ensemble‐based intelligent system models. This research further highlights that, among the competing linear models, the model with both the stock market indices and historical exchange rate values as the predictors is the best forecaster. Parameters of the linear model are deduced through a Monte Carlo stochastic approach. Relative importance of the predictors is also studied, and the influence of historical exchange rates, the immediate impact of AOI and the lagged effect of DJI are noted. Copyright © 2012 John Wiley & Sons, Ltd.     

Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

Prediction of outstanding payments in a Poisson cluster model

We consider a simple Poisson cluster model for the payment numbers and the corresponding total payments for insurance claims arriving in a given year. Due to the Poisson structure one can give reasonably explicit expressions for the prediction of the payment numbers and total payments in future periods given the past observations of the payment numbers. One can also derive reasonably explicit expressions for the corresponding prediction errors. In the (a, b) class of Panjer's claim size distributions, these expressions can be evaluated by simple recursive algorithms. We study the conditions under which the predictions are asymptotically linear as the number of past payments becomes large. We also demonstrate that, in other regimes, the prediction may be far from linear. For example, a staircase-like pattern may arise as well. We illustrate how the theory works on real-life data, also in comparison with the chain ladder method.     

Information and volatility links in the foreign exchange market

We apply the trading model of Fleming et al (1998 ). to a number of currency markets. The model posits that two markets can have common volatility structures as a result of receiving common information and from cross‐hedging activity where a position in one currency is used to hedge risk in a position taken in another. Our results imply that the model is effective in identifying common information flows and volatility spillovers in the currency markets and that some of these effects are lost when simply examining raw correlations. A series of specification tests of the 21 bivariate systems that are examined provides support for the trading model in the foreign exchange context.     

Testing PPP Hypotheses between Japan and the Six G7 Countries

The paper examines the purchasing power parity(PPP) theory of the foreign exchange rate of the yenagainst the currencies of the six G7 countries. We usethe error-corrected five-dimensional vectorautoregressive (VAR) model with structural changes inthe trend function. The data cover the period of thepost-Breton–Woods floating exchange rate system. Theresults reveal that the PPP relation alone determinesthe exchange rates for the USA, France, Germany, andItaly, while a linear combination of PPP and uncoveredinterest rate parity (UIP) relations determines that for Canada. Ina model without trend breaks, the PPP relations holdonly for Germany, which indicates that a correctspecification of the sampling distribution of data isimportant. The one-step prediction based on the errorcorrection model (ECM) outperforms the random walkmodel. The ECM is useful to predict the out-of-samplebehaviors of the exchange rates.     

An Intertemporal International Asset Pricing Model: Theory and Empirical Evidence

We extend Campbell's (1993) model to develop an intertemporal international asset pricing model (IAPM). We show that the expected international asset return is determined by a weighted average of market risk, market hedging risk, exchange rate risk and exchange rate hedging risk. These weights sum up to one. Our model explicitly separates hedging against changes in the investment opportunity set from hedging against exchange rate changes as well as exchange rate risk from intertemporal hedging risk. A test of the conditional version of our intertemporal IAPM using a multivariate GARCH process supports the asset pricing model. We find that the exchange rate risk is important for pricing international equity returns and it is much more important than intertemporal hedging risk.     

Exchange rates and order flow in the long run

Several recent papers have underlined the importance of microstructure effects in understanding exchange rate behavior by documenting stable long-run relationships between cumulated order flows and spot exchange rates. This stands in contrast to the widely-studied failure of exchange rates to conform to the long-run behavior implied by “conventional” macroeconomic models and is consistent with the prediction of micro-structure models. We re-examine the evidence for stable long-run relationships. We find that such evidence exists only for a small number of the major currencies we examine and that it is statistically fragile. We conclude that this implication of microstructure models does not fit the data as well as previous studies suggest.     

Adaptive Learning in an Expectational Difference Equation with Several Lags: Selecting among Learnable REE

It is demonstrated that adaptive learning in the least squares sense may be incapable of satisfactorily reducing the number of attainable equilibria in a rational expectations model when focusing on the forward‐solutions to the model. The model examined, as an illustration, is a basic asset pricing model for exchange rate determination that is augmented with technical trading in the currency market in the form of moving averages since it is the most commonly used technique according to questionnaire surveys. The forward‐solutions to such a model are preferable to the backward‐solutions that are normally utilized since announcement effects is an important feature in currency trade. Because of technical trading in foreign exchange, the current exchange rate depends on j _max lags of the exchange rate, meaning that the model has j _max+1 rational expectations equilibria, where several of them are adaptively learnable in the least squares sense. However, since past exchange rates should not affect the current exchange rate when technical trading is absent, it is possible to single out a unique equilibrium among the adaptively learnable equilibria that is economically meaningful. It is worth noting that the model examined can also be viewed as a model for stock price determination in which the forward‐solutions to the model are preferable to the backward‐solutions since the importance of announcement effects is a common characteristic for currency and stock markets.     

Valuing foreign exchange rate derivatives with a bounded exchange process

Foreign exchange rates have been subjected to periods of tighter or looser controls as various political and economic forces have waxed and waned. When currencies were backed by gold there were fixed exchange rates. In 1973 floating exchange rates were adopted though many countries did try to keep their currency values within certain ranges. More recently the European Economic Community formalized this practice. Free-floating exchange rates might be well characterized by the lognormal distribution which is standard in option pricing. However, this is probably a poor approximation for exchange rates which are kept within some range by the actions of one or both governments or central banks. This paper develops a model which can be used to value options and other derivative contracts when the underlying exchange rate is bounded in a fixed range (a, b). Methods for pricing both European and American style options are developed.The author would like to thank Ken French and Geert Rouwenhorst for their comments and suggestions.     

A novel fuzzy neural approach to data reconstruction and failure prediction

Bank failure prediction is of great importance to a bank's clients, policy-makers and regulators. Various traditional models have been employed to study bank failures. Unfortunately, their performances are unsatisfactory. In this paper, the pseudo-outer product fuzzy neural network using the compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S))-based bank failure prediction model is proposed. It employs computational bank failure analysis techniques coupled with reconstruction of missing financial data in financial covariates that are available from publicly available financial statements as inputs. The performance of the proposed model is assessed through the classification rate of 3636 US banks observed over a 21-year period. The effects of missing data reconstruction are investigated. Copyright © 2009 John Wiley & Sons, Ltd.     

The Application of Corporate Governance Indicators With XBRL Technology to Financial Crisis Prediction

ABSTRACT

The widespread adoption of eXtensible Business Reporting Language (XBRL) suggests that intelligent software agents can now use financial information disseminated on the Web with high accuracy. Financial data have been widely used by researchers to predict financial crises; however, few studies have considered corporate governance indicators in building prediction models. This article presents a financial crisis prediction model that involves using a genetic algorithm for determining the optimal feature set and support vector machines (SVMs) to be used with XBRL. The experimental results show that the proposed model outperforms models based on only one type of information, either financial or corporate governance. Compared with conventional statistical methods, the proposed SVM model forecasts financial crises more accurately.     

Forecasting forward defaults: a simple hazard model with competing risks

A forward default prediction method based on the discrete-time competing risk hazard model (DCRHM) is proposed. The proposed model is developed from the discrete-time hazard model (DHM) by replacing the binary response data in DHM with the multinomial response data, and thus allowing the firms exiting public markets for different causes to have different effects on forward default prediction. We show that DCRHM is a reliable and efficient model for forward default prediction through maximum likelihood analysis. We use actual panel data-sets to illustrate the proposed methodology. Using an expanding rolling window approach, our empirical results statistically confirm that DCRHM has better and more robust out-of-sample performance than DHM, in the sense of yielding more accurate predicted number of forward defaults. Thus, DCRHM is a useful alternative for studying forward default losses on portfolios.     

European Corporate Governance: a Thematic Analysis of National Codes of Governance

Using Latent Semantic Analysis techniques to analyse the corporate governance codes of 23 EU nations, we obtain a number of new findings regarding their thematic content, variability, and convergence. We determine that these codes can be decomposed into five common themes, with substantial cross‐sectional variability in their relative importance. We also find that the themes contained in these codes cluster in ways that are not fully consistent with the legal regime classifications of La Porta et al. (1997) , leading us to construct two new country clusters. We further discover that the identity of the code issuer (e.g., government versus stock exchange) is important in explaining a code's primary theme as well as changes in theme prominence over time. Finally, we fail to find evidence of an unchecked thematic convergence towards an Anglo‐Saxon model of corporate governance, with some code themes converging to UK practices while others diverge.     

Quantitative modelling of the EUR/CHF exchange rate during the target zone regime of September 2011 to January 2015

Krugman (1991)'s target zone model for exchange rate dynamics has become the reference of a large part of this literature. Despite its simplicity and elegance, empirical evidence has been lacking, not least because it is difficult to capture the predicted non-linear relationship between the observable exchange rate and the non-observable fundamental value. This is why we propose a different approach. By inverting locally the relation between exchange rate and fundamental value, we derive analytical expressions for the conditional volatility and the probability density as a function of the exchange rate. This allows us to examine Krugman's prediction directly from historical data, and, furthermore, enables us to test the smooth pasting condition, which is intimately related to the no-arbitrage condition. Concretely, we study the performance of the euro/Swiss franc exchange rate in the extraordinary period from September 6, 2011 to January 15, 2015, when the Swiss National Bank enforced a minimum exchange rate of 1.20 Swiss francs per euro. We show that the data are well explained by the theory and conclude that Krugman's target zone model holds after all, but apparently only under extreme and sustained pressure that pushes continuously the exchange rate very close to the boundary of the target zone.     

The term structure of the forward premium

Most studies of the efficiency of the foreign exchange market focus on a single maturity — usually a one month forward exchange rate. However, one observes that forward contracts of many maturities are simultaneously traded in the foreign exchange market. The hypothesis that the foreign exchange market uses all available information has implications for the joint behavior of forward exchange rates of various maturities. This paper proposes an equilibrium theory of the term structure of the forward premium. The model is tested using data on the German and Canadian exchange rates; the results indicate that the data are consistent with the theory for Germany and inconsistent with the theory for Canada.     

Credibility Using Copulas

Abstract

Credibility is a form of insurance pricing that is widely used, particularly in North America. The theory of credibility has been called a “cornerstone” in the field of actuarial science. Students of the North American actuarial bodies also study loss distributions, the process of statistical inference of relating a set of data to a theoretical (loss) distribution. In this work, we develop a direct link between credibility and loss distributions through the notion of a copula, a tool for understanding relationships among multivariate outcomes.

This paper develops credibility using a longitudinal data framework. In a longitudinal data framework, one might encounter data from a cross section of risk classes (towns) with a history of insurance claims available for each risk class. For the marginal claims distributions, we use generalized linear models, an extension of linear regression that also encompasses Weibull and Gamma regressions. Copulas are used to model the dependencies over time; specifically, this paper is the first to propose using a t-copula in the context of generalized linear models. The t-copula is the copula associated with the multivariate t-distribution; like the univariate tdistributions, it seems especially suitable for empirical work. Moreover, we show that the t-copula gives rise to easily computable predictive distributions that we use to generate credibility predictors. Like Bayesian methods, our copula credibility prediction methods allow us to provide an entire distribution of predicted claims, not just a point prediction.

We present an illustrative example of Massachusetts automobile claims, and compare our new credibility estimates with those currently existing in the literature.     

Exchange rate dynamics under alternative optimal interest rate rules

We explore the role of interest rate policy in the exchange rate determination process. Specifically, we derive exchange rate equations from interest rate rules that are theoretically optimal under a few alternative settings. The exchange rate equation depends on its underlying interest rule and its performance could vary across evaluation criteria and sample periods. The exchange rate equation implied by the interest rate rule that allows for interest rate and inflation inertia under commitment offers some encouraging results — exchange rate changes “calibrated” from the equation have a positive and significant correlation with actual data, and offer good direction of change prediction. Our exercise also demonstrates the role of the foreign exchange risk premium in determining exchange rates and the difficulty of explaining exchange rate variability using only policy based fundamentals.     

Multimarket Trading and Liquidity: Theory and Evidence

We develop a new model of multimarket trading to explain the differences in the foreign share of trading volume of internationally cross‐listed stocks. The model predicts that the trading volume of a cross‐listed stock is proportionally higher on the exchange in which the cross‐listed asset returns have greater correlation with returns of other assets traded on that market. We find robust empirical support for this prediction using stock return and volume data on 251 non‐U.S. stocks cross‐listed on major U.S. exchanges.     

A multivariate stochastic volatility model with applications in the foreign exchange market

The main objective of this paper is to study the behavior of a daily calibration of a multivariate stochastic volatility model, namely the principal component stochastic volatility (PCSV) model, to market data of plain vanilla options on foreign exchange rates. To this end, a general setting describing a foreign exchange market is introduced. Two adequate models—PCSV and a simpler multivariate Heston model—are adjusted to suit the foreign exchange setting. For both models, characteristic functions are found which allow for an almost instantaneous calculation of option prices using Fourier techniques. After presenting the general calibration procedure, both the multivariate Heston and the PCSV models are calibrated to a time series of option data on three exchange rates—USD-SEK, EUR-SEK, and EUR-USD—spanning more than 11 years. Finally, the benefits of the PCSV model which we find to be superior to the multivariate extension of the Heston model in replicating the dynamics of these options are highlighted.