Global ETD Search
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Showing 1 to 14 of 14 for “"log-returns"”.
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Analysis and Modelling of Financial Logarithmic Return Data using Multifractal and Agent-Based Techniques
… behaviour. In this thesis, I study two empirical log return time series for the stylised facts of financial data. I then use Multifractal Detrended Fluctuation Analysis to study the empirical log returns for multifractal scaling. I find that extreme events are inimical to the scaling in highly …
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Risk-return portfolio modelling
… on the efficient frontier when working with log returns. However when working with simple returns the ARMA shifts the efficient frontier to the left. We find that GARCH(l , 1) models capture most of the autocorrelation in the squared residuals for both simple returns and log returns and …
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An Investigation into the suitability of using GARCH process for pricing options on the SAFEX all share index futures contracts
… This involves an assessment of whether the log-returns of the ALSI futures (the instruments underling the ALSl option) follow an ARCH process. A secondary objective is to assess the potential for using an ARCH process to model the ALST spot log returns. This could have the following uses: • …
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Quantile Forecasting of Commodity Futures' Returns: Are Implied Volatility Factors Informative?
This study develops a multi-period log-return quantile forecasting procedure to evaluate the performance of eleven nearby commodity futures contracts (NCFC) using a sample of 897 daily price observations and at-the-money (ATM) put and call implied volatilities of the corresponding prices for the …
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Modelling long-term security returns
… spliced distribution is introduced to model log returns. The Geometric Brownian Motion (GBM) model is employed to predict and evaluate returns on common stocks using the Maximum Likelihood Estimator (MLE), assuming that daily log returns follow a normal distribution. Additionally, the Merton …
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Essays in Global Commodity Prices and Realised Volatility
… MIDAS models estimated recursively, and their analogous monthly version seem to capture some predictive information contained in the speculative variables described by the agricultural managed money spread positions. The most interesting finding – larger RMSE reductions during the crisis period - …
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The Distribution of Individual Stock Returns in a Modified Black-scholes Option Pricing Model
… including which probability distribution stock returns follow. In this paper, we test several distributions to see which best fit the log returns of 20 different companies over a period between November 1, 2006 to October 31, 2011. If a "best" distribution is found, a modified Black-Scholes …
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Market state discovery
… the underlying states (using only simulated log-returns as inputs); and measuring the algorithms' ability to recover the underlying states, using the Adjusted Rand Index (ARI) as a performance metric. Experiments revealed that ASPC is a more robust and better performing algorithm than ICC. …
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A Bi-variate Gamma Generalized Laplace Distribution
… market data, modeling the joint behavior of log returns and volatility for major stock indices. These empirical examples showcase the distribution's ability to capture complex relationships in financial data. This research expands the toolkit of bivariate distributions, with potential …
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Forecasting the S&P 500 index using time series analysis and simulation methods
… forecasts forl2 month levels, and by using log returns from the Great Depression, Tech Bubble, and Oil Crisis the simulation indicates an expected value -2%, valid up to 12 months.
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Asymptotic Likelihood Inference for Sharpe Ratio
… and developed a general tail probability methodology, based on the tangent exponential model. The objective of this paper is to use the third order asymptotic likelihood-based statistical method to obtain highly accurate inference on Sharpe ratio. Since the methodology is demonstrated to work …
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Three Essays Applying Dynamic Models in Economics, Finance, and Machine Learning
… over time. The framework is applied to the log returns of the exchange rates between the US Dollar and the European Euro, British Pound, and Japanese Yen. <strong></strong></p> <p><strong>Estimating Dynamic Time Warping in the Presence of Non-Stationary Time Series</strong></p> <p>This essay …
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The Consequences of Euronext integration on the French, Belgian and Dutch stock markets.
… and post-integration period. Two types of returns are computed: log- returns and excess returns. A dummy variable and a control variable, the German main index DAX, are included in the analysis to acc ount for the effect of the introduction of the Euro. Unit root and stationarity tests show …
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Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models
… variable of our SV models is transformed log return (based on log-square transformation), GARCH directly models the log return itself. This means that, theoretically speaking, we are able to predict stock returns using GARCH models while this is not feasible if we use SV model. Because SV …