Non-linear exchange rate forecasting : the role of market microstructure variables
Notice bibliographique
Résumé
In this dissertation, we conduct a study of exchange rate models for the Canada/U.S. exchange rate. More specifically, we focus on their intra-day (high-frequency) and, subsequently, weekly forecast performances. All attempts to explain equilibrium exchange rates suffer from various problems: structural (macroeconomic) models used for out-of-sample forecasting produce poor forecasts. Given that different market participants trade based on private as well as public information sets, it is natural to assume that equilibrium exchange rate expectations are formed from a combination of macroeconomic fundamentals and market microstructure variables. Chapter 1 motivates research in the area of non-linear microstructure exchange rate modeling, reviews the recent literature and introduces the general ideas behind this thesis. Chapter 2 outlines Artificial Neural Networks (ANNs) and other non-linear modeling approaches used in this research. Chapter 3 introduces a non-linear Canada/U.S. exchange rate microstructure model and provides a strong evidence for the microstructure effects. Our horse race for forecast performance results in a non-linear ANN model as the winner. ANN models outperform random walk and linear models in a number of recursive out-of-sample forecasts. The daily forecasts produced by ANN models are statistically significant according to Diebold and Mariano (1995) statistics. Apart from the nearest neighbours model, other linear and non-linear models are unable to generate significant predictions. The inclusion of a microstructure variable, order flow, substantially improves the predictive power of both the linear and non-linear models. Our findings also indicate the necessity of embodying (in a non-linear sense) information not only from interbank order flows, but also from commercial client and foreign institution transactions. No matter which non-linear model is used, there is always a slight forecast gain when dealer's private order flows are included into a set of explanatory variables. Chapter 4 describes fuzzy logic technology in the form of approximate reasoning as a method that can be used in economics when dealing with continuous and imprecise economic variables, insufficient data for analysis and when a mathematical model of the process is unknown. Chapter 5 develops an original and novel approach to generating trading strategies in the foreign exchange (FX) market based on forecasts from the ANN. Neurofuzzy (NF) decision-making technology is designed and implemented to obtain the optimal daily currency trading rule. We find that a non-linear ANN exchange rate microstructure model combined with a fuzzy logic controller (FLC) generates a set of trading strategies that, on average, earn a higher rate of return compared to the simple buy-and-hold strategy. We also find that after including transaction costs, the gains from the NF technology do not decline and increase on some periods. Finally, we successfully apply the NF model to the problem of determining the FX market's sentiment as reflected by the chartists' trading signals during periods of strong depreciation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».