The forward market for foreign exchange :

The findings of this study support others which examine financial markets. The time series distributions are not normal; they are stable Paretian. The forward market for foreign exchange is not random; but neither is it inefficient based on the weak form model.The study also shows that there were patterns in the time series of rates of return on forward contracts. These were low level autocorrelations and were not stable through time. The patterns were short and changed throughout the time period. Using several testing techniques, the hypothesis that the forward is inefficient was rejected. Several nonparametric tests and Box-Jenkins time series analysis were used. These show that the time series of rates of return on forward contracts, while not a random walk, are not inefficient at the weak form level.Intertemporal speculation, the act of speculating between forward maturities, was defined in the paper. The existence of profitability using this technique was found to be significantly different from zero, but less than the return on short-term U. S. government securities. Therefore, even though the technique is available for forward market participants, the return is not commensurate with the level of risk incurred.The study employed daily spot and forward exchange rates from March 1973 to June 1976 for the following currencies: U.S. to U.K.; U.S. to Swiss Franc; U.S. to German Mark; U.S. to Canadian Dollar; U.K. to German Mark; and U.K. to Canadian Dollar. The rate of return on forward contracts was defined to be the difference between the forward rate at time t and the spot rate which exists upon maturation of that forward contract expressed as a percentage of the spot rate. The distribution of these rates of return was shown to more closely approximate the stable Paretian distribution than the normal distribution. This was true for the spot rate and forward rate distributions as well. Since stable Paretian distributions have no defined variance, an alternative measure of disbursion should be established to replace the sample standard deviation or variance.This study is devoted to an examination of the efficiency and characteristics of the forward market for foreign exchange. Here, efficiency implies that current market prices or rates incorporate any information embodied in the pattern of past prices or rates. The characteristics of the forward market examined include the distribution of rates of return, as well as the relationship of forward exchange rates of different maturities.The percentage premia of different forward rates, relative to the spot rate, were examined in the paper. It was found that relative premia decline as time to maturity increases. Furthermore, the premia decrease at a decreasing rate. The slope between 30 and 60 day premia is more steep than the slope between 60 and 90 day premia. For firms using the forward market to cover exchange rate risk, the implication is that the cost of forward cover is decreased as forward maturity is increased. Thus a reward exists for good forward planning

Fiscal adjustment and growth in Sub-Saharan Africa : overview and lessons from the current downturn

In light of the proliferation of exceptionally large fiscal stimuli to ward off the recession triggered by the 2008 global economic and financial crisis in most advanced economies, this paper revisits the fiscal adjustment and growth nexus in Sub-Saharan Africa. Using transfer functions, it quantifies expected losses in terms of aggregate output largely attributed to a systematic implementation of pro-cyclical expenditure switching and reducing policies to achieve low deficit targets throughout the decades ofadjustments. The results consistently highlight a much higher predicted aggregate output under the hypothesized counter-cyclical fiscal expansion option. This consistent outcome suggests that the output gap would have been significantly smaller in the region if countries had drawn on stop-and-go policies of fiscal expansion to sustainably raise the stock of capital investments.Debt Markets,Public Sector Expenditure Policy,Fiscal Adjustment,Economic Stabilization,Economic Theory&Research

Wind power forecasting by nonparametric and parametric time series models

This report represents the documentation of the project entitled "Wind Power Forecasting by Nonparametric and Parametric Time Series Models". The project was prepared between March 2012 and the 10th of October 2012, at Universitat Politècnica de Catalunya. The main focus of the project was to study methods for forecasting wind speed and wind power for a short term horizon. In order to find the estimates, parametric and nonparametric models were used. The accuracy of the predictions was tested by using the RMSE indicator. Results show that nonlinear models are a better fit for the wind dat

Conceptualization of Machine Learning in Economic Forecasting

Economic forecasting is a very important aspect that policymakers in the financial and corporate organization rely on because helps them to determine future events that might infringe some hardship on the economy and the citizens at large. However, the principal statistical pointers that are available to the public domain provide numerous reservations and doubts for their economics estimates as it is later released with frequent issues to major revisions and also it shows a great lag in decision making for an incoming event. To this effect, the expansion of the latest forecasting patterns was important to address the gaps. Hence, this paper examines the conceptualization of Machine learning in economic forecasting. To achieve this, the Italian economy was used as the dataset, and machine learning controlled tools were used as the method of analysis. The result obtained from this study shows that machine learning is a better model to use in economic forecasting for quick and reliable data to avert future events

Machine Learning-Based Data and Model Driven Bayesian Uncertanity Quantification of Inverse Problems for Suspended Non-structural System

Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and control strategies based on simulation or prediction results. However, in the surrogate model, preventing overfitting and incorporating reasonable prior knowledge of embedded physics and models is a challenge. Suspended Nonstructural Systems (SNS) pose a significant challenge in the inverse problem. Research on their seismic performance and mechanical models, particularly in the inverse problem and uncertainty quantification, is still lacking. To address this, the author conducts full-scale shaking table dynamic experiments and monotonic & cyclic tests, and simulations of different types of SNS to investigate mechanical behaviors. To quantify the uncertainty of the inverse problem, the author proposes a new framework that adopts machine learning-based data and model driven stochastic Gaussian process model calibration to quantify the uncertainty via a new black box variational inference that accounts for geometric complexity measure, Minimum Description length (MDL), through Bayesian inference. It is validated in the SNS and yields optimal generalizability and computational scalability

Authentication of reprocessing plant safeguards data through correlation analysis

This report investigates the feasibility and benefits of two new approaches to the analysis of safeguards data from reprocessing plants. Both approaches involve some level of plant modeling. All models involve some form of mass balance, either applied in the usual way that leads to material balances for individual process vessels at discrete times or applied by accounting for pipe flow rates that leads to material balances for individual process vessels at continuous times. In the first case, material balances are computed after each tank-to-tank transfer. In the second case, material balances can be computed at any desired time. The two approaches can be described as follows. The first approach considers the application of a new multivariate sequential test. The test statistic is a scalar, but the monitored residual is a vector. The second approach considers the application of recent nonlinear time series methods for the purpose of empirically building a model for the expected magnitude of a material balance or other scalar variable. Although the report restricts attention to monitoring scalar time series, the methodology can be extended to vector time series

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)

A Bayesian approach to robust identification: application to fault detection

In the Control Engineering field, the so-called Robust Identification techniques deal with the problem of obtaining not only a nominal model of the plant, but also an estimate of the uncertainty associated to the nominal model. Such model of uncertainty is typically characterized as a region in the parameter space or as an uncertainty band around the frequency response of the nominal model. Uncertainty models have been widely used in the design of robust controllers and, recently, their use in model-based fault detection procedures is increasing. In this later case, consistency between new measurements and the uncertainty region is checked. When an inconsistency is found, the existence of a fault is decided. There exist two main approaches to the modeling of model uncertainty: the deterministic/worst case methods and the stochastic/probabilistic methods. At present, there are a number of different methods, e.g., model error modeling, set-membership identification and non-stationary stochastic embedding. In this dissertation we summarize the main procedures and illustrate their results by means of several examples of the literature. As contribution we propose a Bayesian methodology to solve the robust identification problem. The approach is highly unifying since many robust identification techniques can be interpreted as particular cases of the Bayesian framework. Also, the methodology can deal with non-linear structures such as the ones derived from the use of observers. The obtained Bayesian uncertainty models are used to detect faults in a quadruple-tank process and in a three-bladed wind turbine

Chaotic price dynamics of agricultural commodities

Traditionally, commodity prices have been analyzed and modeled in the context of linear generating processes. The purpose of this dissertation is to address the adequacy of this work through examination of the critical assumption of independence in the residual process of linearly specified models. As an alternative, a test procedure is developed and utilized to demonstrate the appropriateness of applying generalized conditional heteroscedastic time series models (GARCH) to agricultural commodity prices. In addition, a distinction is made between testing for independence and testing for chaos in commodity prices. The price series of interest derive from the major international agricultural commodity markets, sampled monthly over the period 1960--1994. The results of the present analysis suggest that for bananas, beef, coffee, soybeans, wool and wheat seasonally adjusted growth rates, ARCH-GARCH models account for some of the non-linear dependence in these commodity price series. As an alternative to the ARCH-GARCH models, several neural network models were estimated and in some cases outperformed the ARCH family of models in terms of forecast ability. This further demonstrated the nonlinearity present in these time series. Although, further examination is needed, all prices were found to be non-linearly dependent. It was determined by use of different statistical measures for testing for deterministic chaos that wheat prices may be an example of such behavior. Therefore, their may be something to be gained in terms of short-run forecast accuracy by using semi-parametric modeling approaches as applied to wheat prices