In: Science
...Emerging Market Mutual Fund Performance and the State of the Economy∗ Ayelen Banegas November 2010 Abstract Following the ﬁnancial liberalization of many Asian, European, and Latin American countries emerging markets have become a central player in the global economy. As a result the universe of equity funds investing in these developing economies has been in continuous expansion. In this paper we propose a set of asset class speciﬁc predictive variables for emerging markets and exploit them in order to identify those funds that outperform the market in diﬀerent phases of the economic cycle. We employ a comprehensive survivorship-bias free universe of global and regional emerging market funds and use a Bayesian framework that incorporates predictability in manager skills (stock selection and benchmark timing skills), fund risk loadings and benchmark returns by exploiting ex-ante business cycle related state variables. Our results provide empirical evidence of return predictability and the economic value of active management in emerging markets. ∗ I would like to thank Allan Timmermann for his guidance and support. I am also grateful to James Hamilton, Bruce N. Lehmann, Ross Valkanov and Debbie Watkins for their helpful comments. I also beneﬁted from discussions with Ben Gillen. Finally, I want to thank Russ Wermers for providing me with the mutual fund dataset. 1 1 Introduction During the last decades the mutual fund industry has been......
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...In researching the needs for Abbon Laboratories in regards to their email server, I researched Naïve Bayesian Filters. Bayesian spam filtering; is a statistical technique of e-mail filtering. It makes use of a naive Bayes classifier to identify spam e-mail. The Bayesian classifiers work by associating the use of tokens (typically words, phrases, etc), with spam and non-spam e-mails and then using Bayesian inference to calculate a probability that an email is or is not spam. Bayesian spam filtering is considered to be a powerful procedure for dealing with spam, that can be tailored to the email needs of individual users, and gives low false positive spam detection rates that are generally acceptable to users. The process of Bayesian spam filtering works in the way of distinguishing particular words which have a higher probability of occurring in spam email. This filter however, doesn’t know these probabilities in advance, and must be first trained so it can build them up. In order to train the filter, the user must first manually indicate whether a new email is spam or not. For all the words in each training email, the filter will adjust the probabilities that each word will appear in spam or legitimate email in its database. After training the system, the word probabilities are used to compute the probability that an email with a particular set of words in it belongs to a particular category. Each word in the email contributes to the email’s spam probability, or......
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...2013 JEL classification: C11, C15, C53, E17, G17. Keywords: Commodity prices, equity prices, density forecasting, correlation, Bayesian DCC. BIS Working Papers are written by members of the Monetary and Economic Department of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in character. The views expressed in them are those of their authors and not necessarily the views of the BIS. This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2013. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISSN 1020-0959 (print) ISBN 1682-7678 (online) On the correlation between commodity and equity returns: implications for portfolio allocation∗ Marco J. Lombardi† Francesco Ravazzolo‡ July 11, 2013 Abstract In the recent years several commentators hinted at an increase of the correlation between equity and commodity prices, and blamed investment in commodity-related products for this. First, this paper investigates such claims by looking at various measures of correlation. Next, we assess what are the implications of higher correlations between oil and equity prices for asset allocation. We develop a time-varying Bayesian Dynamic Conditional Correlation model for volatilities and correlations and ﬁnd that joint modelling commodity and equity......
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... |2 | | |6.5 |Bayesian Probability Revision, EVSI |4 | | |6.6 |Expected Value and Expected Utility Criteria |4 | | |6.7 |Utility, Expected Utility Criterion |4 | | |6.8 |Decision Tree Analysis |5 | | |6.9 |Constructing Payoff Tables, Minimax Regret and Expected Value|6 | | | |Criterion | | | |6.10 |Game Theory |4 | | |6.11 |Decision Making Under Uncertainty-- Maximin and Principle of |1 | | | |Insufficient Reason Criteria | | | |6.12 |Bayesian Probability Revision, EVSI, Efficiency |4 | ...
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...probabilistic model for which a graph denotes the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. An example of a graphical model. Each arrow indicates a dependency. In this example: D depends on A, D depends on B, D depends on C, C depends on B, and C depends on D. Contents [hide] 1 Types of graphical models 1.1 Bayesian network 1.2 Markov random field 1.3 Other types 2 Applications 3 See also 4 Notes 5 References and further reading 5.1 Books and book chapters 5.2 Journal articles 5.3 Other Types of graphical models[edit] Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a complete distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov networks. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.[1] Bayesian network[edit] Main article: Bayesian network If the network structure of the model is a directed acyclic graph, the model represents a factorization of the joint probability of all random variables. More......
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...support this analysis consists of 669 radiographic scans measured by a SAIC VACIS (Vehicle and Cargo Imaging System) at the Port of Oakland. The machine learning methods that will be investigated include: particle filters (PF), support vector machines (SVM’s) and Variational Bayesian factor analysis (VBFA). Features that will be studied include those generated via Sobel Edge operators and those based upon both local and global statistical characteristics of the images. We will apply each of these techniques to the entire image dataset, as well as to a duplicate set of images that have a simple surrogate threat overlaid on each image. This allows us to characterize the separability and variability of both individual and combinations of features due to the presence of a threat and provide threat and non-threat training data for machine learning algorithms that could be used to analyze these features in combination with the features from other sources. This effort supports both directorate technology and infrastructure by providing an environment facilitating the rapid prototyping and development of feature extraction and machine learning algorithms. Technical Approach and Results Variational Bayesian Factor Analysis (VBFA) methods have been utilized to extract features from VACIS-type radiographic scans that could in the future be used with the RIID, RPM, PRD and context features to improve the detection, classification and identification of potential nuclear threats. ......
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...Disadvantages * 4.3 Recommendations * 5 Types of bootstrap scheme * 5.1 Case resampling * 5.1.1 Estimating the distribution of sample mean * 5.1.2 Regression * 5.2 Bayesian bootstrap * 5.3 Smooth bootstrap * 5.4 Parametric bootstrap * 5.5 Resampling residuals * 5.6 Gaussian process regression bootstrap * 5.7 Wild bootstrap * 5.8 Block bootstrap * 5.8.1 Time series: Simple block bootstrap * 5.8.2 Time series: Moving block bootstrap * 5.8.3 Cluster data: block bootstrap * 6 Choice of statistic * 7 Deriving confidence intervals from the bootstrap distribution * 7.1 Bias, asymmetry, and confidence intervals * 7.2 Methods for bootstrap confidence intervals * 8 Example applications * 8.1 Smoothed bootstrap * 9 Relation to other approaches to inference * 9.1 Relationship to other resampling methods * 9.2 U-statistics * 10 History * 11 See also * 12 Notes * 13 Further reading * 14 External links * 14.1 Software History The bootstrap was introduced in 1979 by Bradley Efron at Stanford University.[5] It was inspired by earlier work on the jackknife.[6][7][8] Improved estimates of the variance were developed later.[9][10] A Bayesian extension was developed in 1981.[11] The bias-corrected and accelerated (BCa) bootstrap was developed by Efron in 1987,[12] and the ABC procedure in 1992.[13] Approach The basic idea of......
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... being ignored. By contrast, P3 will probably offer a “middle-‐choice” which will bring a promising and more stable return. 1 Portfolio Modeling and Evaluation: Beating the Market Platinum Fund TABLE CONTENT 1.INTRODUCTION 2.DATA 2.1.BASIC INFORMATION 2.2.DATA LIMITATIONS 3 3 3 4 3.METHODOLOGY 3.1. METHODS ON PORTFOLIO MODELING CONSTRUCTION 3.1.1. MARKET PORTFOLIO AND BLACK-‐LITTERMAN MODEL 3.1.2. SHRINKAGE MIXED WITH BAYESIAN 4 4 4 8 3.2. PORTFOLIO CONSTRUCTION LIMITATIONS 3.3. METHODS ON EVALUATION 12 13 4. RESULTS 4.1. PERFORMANCE EVALUATION 4.2. SENSITIVITY TESTS 15 15 19 5.CONCLUSION REFERENCES 23 24 2 Portfolio Modeling and Evaluation: Beating the Market Platinum Fund 1.Introduction Platinum Fund (PF) is an actively managed investment portfolio composed by 10 large US stocks. This ......
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...tradeoﬀ Jeﬀ Borowitz Introduction to Game Theory 15 / 18 Game Theory Background An Illustrative Example Course Structure Plan Content Classes of Games and Equilibrium Concepts Typically, making a game richer in some dimension makes another equilibrium concept natural First concept: Dominant Strategy Equilibrium Other concepts: Complete Info (2) Nash Eq. (3) Subgame-Perfect Eq. Incomplete Info (4) Bayesian Eq. (5) Perfect Bayesian Eq, Static Dynamic Jeﬀ Borowitz Introduction to Game Theory 16 / 18 Game Theory Background An Illustrative Example Course Structure Plan Content Solution Techniques In order to solve these types of games, we will learn (and be tested on) solution techniques 1 2 3 Elimination of dominated strategies (Dominant Strategy Equilibrium) Finding simultaneous best responses (Nash Equilibrium) Finding simultaneous probabalistic best responses (Nash Equilibrium with mixed strategies) Backwards induction (Subgame Perfect Equilibrium) Construction of equilibria (Bayesian Equilibrium and Perfect Bayesian Equilibrium) 4 5 * I will use “Solution Concept” to mean “Equilibrium Concept” - the important thing is that a solution technique is used to ﬁnd strategies that satisfy equilibrium concepts Jeﬀ Borowitz Introduction to Game Theory 17 / 18 Game Theory Background An Illustrative Example Course Structure Plan Content Playing Games Game theory is just one set of things to think about in strategic......
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...C Ike Antkare’s publications [10] Ike Antkare. Analysis of reinforcement learning. In Proceedings of the Conference on Real-Time Communication, February 2009. [11] Ike Antkare. Analysis of the Internet. Journal of Bayesian, Event-Driven Communication, 258:20–24, July 2009. [12] Ike Antkare. Analyzing interrupts and information retrieval systems using begohm. In Proceedings of FOCS, March 2009. [13] Ike Antkare. Analyzing massive multiplayer online role-playing games using highlyavailable models. In Proceedings of the Workshop on Cacheable Epistemologies, March 2009. [14] Ike Antkare. Analyzing scatter/gather I/O and Boolean logic with SillyLeap. In Proceedings of the Symposium on Large-Scale, Multimodal Communication, October 2009. [15] Ike Antkare. Architecting E-Business Using Psychoacoustic Modalities. PhD thesis, United Saints of Earth, 2009. [16] Ike Antkare. Bayesian, pseudorandom algorithms. In Proceedings of ASPLOS, August 2009. [17] Ike Antkare. BritishLanthorn: Ubiquitous, homogeneous, cooperative symmetries. In Proceedings of MICRO, December 2009. [18] Ike Antkare. A case for cache coherence. Journal of Scalable Epistemologies, 51:41–56, June 2009. [19] Ike Antkare. A case for cache coherence. In Proceedings of NSDI, April 2009. [20] Ike Antkare. A case for lambda calculus. Technical Report 906-8169-9894, UCSD, October 2009. [21] Ike Antkare. Comparing von Neumann machines and cache coherence. Technical Report 7379, IIT, November......
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...augmenting lags (p) is determined by minimizing the Schwartz Bayesian information criterion or minimizing the Akaike information criterion or lags are dropped until the last lag is statistically significant. EVIEWS allows all of these options for you to choose from. Notice that this test equation does not have an intercept term or a time trend. What you want to use for your test is the t-statistic associated with the Ordinary least squares estimate of θ . This is called the Dickey-Fuller tstatistic. Unfortunately, the Dickey-Fuller t-statistic does not follow a standard t-distribution as the sampling distribution of this test statistic is skewed to the left with a long, left-hand-tail. EVIEWS will give you the correct critical values for the test, however. Notice that the test is left-tailed. The null hypothesis of the Augmented Dickey-Fuller t-test is H0 :θ = 0 (i.e. the data needs to be differenced to make it stationary) versus the alternative hypothesis of H1 : θ < 0 b. (i.e. the data is stationary and doesn’t need to be differenced) When the time series is flat and potentially slow-turning around a non-zero value, use the following test equation: Δz t = α 0 + θz t −1 + α 1 Δz t −1 + α 2 Δz t − 2 + L + α p Δz t − p + a t . Notice that this equation has an intercept term in it but no time trend. Again, the number of augmenting lags (p) is determined by minimizing the Schwartz Bayesian information criterion or minimizing the......
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...goal of this dissertation is to investigate the Bayesian modelling performance for football data. An extensive study of Markov processes and the Bayesian statistical approach is carried out. In particular, special reference is made to the radical Markov Chain Monte Carlo sampling technique. Using real data from the Italian serie A championship, a Bayesian modelling application (as according to the rationale of professors Gianluca Baio and Martha A. Blangiardo) is considered and confronted with the performance of a comparable generalised linear model. i Acknowledgements It is my pleasure to thank the several people who have made this dissertation possible with their precious support. First and foremost I am heartily thankful to my supervisor, Professor Lino Sant (Head of Statistics & Operations Research Department, University of Malta), whose continuous encouragement and professional guidance motivated me to develop a thorough understanding of the subject. I wish to express my deep gratitude towards two more individuals for their time and disponibility. The first is Vincent Marmara (from Betfair Group, Malta), who helped me develop a better picture of the sports betting industry. And the other is professor Gianluca Baio (from the Department of Statistical Science, University College London), with whom I have frequently corresponded via email regarding any queries I had about the implementation of his Bayesian mixture model. Lastly I would also like to......
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...The article, "Bayesian Inference and Contractualist Justification on Interstate 95", written by Arthur Isak Applbaum sheds light on philosophic controversial issue of racial profiling. (1) Applbaum argues that the use of racial generalizations can be justified if the statistical inference is accurate. He describes three types of cases that police might use group-based selection criteria: group-based patrol, group-based enforcement, and group-based identification. The later one states that police will select unidentified people who fit the group-based description for a known violation from public for scrutiny. For example, if a thief is a tall redhead woman, then police try to stop all the tall redhead women to find that thief. (2) He argues that, under this type, accurate profiling can make police more effective and people who are selected only bear a minor cost. The author claims that if the statistical generalization is accurate, then the net gain from searching strategies would be huge from profiling. For the benefits side, profiling helps police largely reduce their searching work and makes the efforts more effective. For the costs side, the selected people may only bear few minutes questioning and that is not a big deal for rational persons except particular circumstances. Thus, the net gain from profiling, compared to search all the people, is largely positive. Hence, the use of racial profiling can be justified in this case. (3) Although the profiling can help police......
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...| | | | Education | 2007 to 2011 | Bachelor of Engineering in Computer Science and Engineering (September 2007) Annamalai University, Chidambaram, Tamilnadu SEMESTER | GPA | SEMESTER | GPA | First yr | 7.84 | ------------------- | -------- | Third | 7.54 | Fourth | 7.79 | Fifth | 8.11 | Sixth | 8.86 | Seventh | 9.25 | Eighth | 8.21 | CGPA:8.31 | 2006 | 12th Grade/Class Percentage or Grade:71.4 | 2004 | 10th Grade/ClassPercentage or Grade:80.6 | | | Industrial Exposure / Internships & Projects | Company Name:1) BSNL PATNABSNL(Bharat Sanchar Nigam Limited) 2) MAJOR ROJECTSparse Bayesian Learning of Filters for Efficient Image Expansion. This project aims for acquiring compact yet high performance image expansion filter based on Sparse Bayesian estimation and derive an efficient learning procedure for its parameters on the basis of variation approximation. This project provides high quality of image with low cost. The language used was Visual C# .Net. The Front End was Microsoft Visual Studio .Net2008 and the Backend was SQL Server 2005. | 1)MAY 2010 to JUNE 2010 2) DEC 2010 to APRIL 2010 | | | | | | | | Activities and Interests (Mention Extra-Curricular Activities if any) | * Organizing many events in the college. * Article published in Annamalai University year book. The topic was Mobile Health......
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...tests, we use the f +1 rule of thumb. Since quarterly data was used, an initial lag length of 5 was employed for the ADF test of all the variables. Therefore the ADF regression is given as: "∆" "Y" _"t" "= " "C" _"1" "+ " "C" _"2" "+ " "C" _"3" "Y" _"t-1" "+ " ∑_"i=1" ^"5" ▒"δ" _"1" 〖"∆Y" 〗_"t-i" "+ " "μ" _"t" Ho: C3 = 0 : Non Stationary (Yt has unit root) H1: C3 ≠ 0 : Stationary (Yt has no unit root ) Where C1 is the constant term, C2 and C3 are the coefficients of a time trend and the first lag of Yt respectively. µt is the stochastic error term. Yt is used here as the general term which refers to the variables, savings rate, real per capital income and deposit rate. The Akaike Information Criterion (AIC), Schwarz and Bayesian Criterion (SBC), and Hannan-Quinn Criterion (HQC) will be used to determine the appropriate lag length to be used. The corresponding t statistic is used for testing the presence of unit root. SAV: Testing for unit root in SAV and assuming no linear trend, the AIC, SBC and HQC all support the use of model with no lags which has a corresponding t-statistic of -1.7634. With an ADF critical value of -2.9414, the ADF test suggests presence of unit root in SAV since the ADF critical value is greater than the t-statistic in absolute terms. Therefore SAV is observed to be integrated of order 1. If we assumed a linear trend, all three (AIC, SBC, HQC) again select a model without lags and the corresponding t-statistic (-1.2964) is......
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