Asset Management in Volatile Markets

The 27th SUERF Colloquium in Munich in June 2008: New Trends in Asset Management: Exploring the Implications was already topical in the Summer of 2008. The subsequent dramatic events in the Autumn of 2008 made the presentations in Munich even more relevant to investors and bankers that want to understand what happens in their investment universe. In the present SUERF Study, we have collected a sample of outstanding colloquium contributions under the fitting headline: Asset Management in Volatile Markets.derivatives, financial innovation, asset management, finance-growth-nexus; Relative Value Strategy, Pair Trading, Slippage, Implementation Shortfall, Asset Management, Fin4Cast

FORECASTING CRUDE OIL MARKET VOLATILITY: TEST OF SYMMETRIC AND ASYMMETRIC GARCH–TYPE MODELS

The purpose of this thesis is to compare the predictive power of three different volatility forecasting models on Brent Crude Oil Index data under two different market conditions. The models included are GARCH, TARCH, and EGARCH. The data covers the period from January 1990 to October 2005. From this overall data two periods of data is extracted both individually representing unique era in the market. First data set measures models functionality during mid 1990’s tranquil times and second measures model performance at the era of higher uncertainty in the early 2000’s. Four hypotheses were formed in this study based on the findings in earlier studies. The first hypothesis suggests that the more complex model should generate most accurate forecasts. Second hypothesis inspected if the asymmetric volatility model results more accurate forecasts than the symmetric model. The third hypothesis stated that more volatile period results inferior volatility forecasts. The final hypothesis suggested that the volatility forecasting capability is linked to forecasting horizons length and is decreasing over time. The empirical tests were concluded by estimating models after two different periods and performing then the forecasting experiment. Each estimation sample was around 4 years and forecasts were constructed for 1–, 3–, and 5–day periods. Forecasting performance of different models is evaluated with five widely used error statistics: the root mean square error (RMSE), mean absolute percentage error (MAPE), the adjusted mean absolute percentage error (AMAPE), logarithmic error (LE), and heteroskedasticity adjusted mean square error (HMSE). Three of four hypotheses were discarded, only third hypothesis was confirmed.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

EVALUATION OF BUSINESS EFFECTS OF MACHINE-TO-MACHINE SYSTEM

The tightening competition and pressure in the project schedules often leave no time or space for the assessment of business impacts of different investments and projects. In addition, in many cases the assessment may be challenging and there is no experience available to undertake it. Therefore, companies often commit to different projects and investments without careful planning and vision of the costs it may cause. The goal in this thesis is to present and clarify the possible applications for the designed platform. The different benefits and its scope of use are also evaluated. Its potential market size is also assessed and its payback period calculated. Moreover, the investment eligibility from customer point of view is evaluated using several investment decision methods. In order to enable the practical business impact assessment, the designed platform is applied to fleet management business. In order to facilitate and increase the assessment of business impacts, a decision support system is also created. It is built on the understanding gained from the cost-benefit analysis conducted in the fleet management case and three other cases from the machine-to-machine business. As a background for the thesis, an overview of the existing solutions is presented and few well-known service models are described. Also an introduction to three sales forecasting methods is given. In order to build a basis for the decision support system, few investment decision methods are presented. As a result, a good understanding of different applications of the platform was gained. It was found to be suitable for any business in which vehicles are involved as they share several common properties such as location information, fuel consumption, speed, and status information. Its potential market size was assessed very promising despite low market share assumption. The payback period was found as very appealing and the investment strongly eligible. The created decision support system was found to be successful. It can be seen as a reliable tool as it consists of several investment decision methods. However, experience from the business area is still needed because any system cannot provide thorough means to identify all the crucial cost factors involved in an investment.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

OFTER: An Online Pipeline for Time Series Forecasting

We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of dimensionality, we employ a weighted norm based on a modified version of the maximal correlation coefficient. The pipeline we introduce is specifically designed for online tasks, has an interpretable output, and is able to outperform several state-of-the art baselines. The computational efficacy of the algorithm, its online nature, and its ability to operate in low signal-to-noise regimes, render OFTER an ideal approach for financial multivariate time series problems, such as daily equity forecasting. Our work demonstrates that while deep learning models hold significant promise for time series forecasting, traditional methods carefully integrating mainstream tools remain very competitive alternatives with the added benefits of scalability and interpretability.Comment: 26 pages, 12 figure

Ensemble prediction model with expert selection for electricity price forecasting

Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets

Benefit-Cost Analysis for Transportation Planning and Public Policy: Towards Multimodal Demand Modeling

This report examines existing methods of benefit-cost analysis (BCA) in two areas, transportation policy and transportation planning, and suggests ways of modifying these methods to account for travel within a multimodal system. Although the planning and policy contexts differ substantially, this report shows how important multimodal impacts can be incorporated into both by using basic econometric techniques and even simpler rule-of-thumb methods. Case studies in transportation planning focus on the California Department of Transportation (Caltrans), but benchmark California’s competencies by exploring methods used by other states and local governments. The report concludes with a list and discussion of recommendations for improving transportation planning models and methods. These will have immediate use to decision makers at Caltrans and other state DOTs as they consider directions for developing new planning capabilities. This project also identifies areas, and lays groundwork, for future research. Finally, by fitting the planning models into the broader context of transportation policy, this report will serve as a resource for students and others who wish to better understand BCA and its use in practice