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Econometrics & Statistics (R, SPSS, Eviews, Gretl, Stata) for master thesis and assignments
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University. Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading. Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl
Technical Skills (application and often implementation from scratch): 1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity 2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution 3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment 4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup 5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
Technical Skills (application and often implementation from scratch): 1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity 2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution 3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment 4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup 5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
Location
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General info
Age: | Adults (18-64 years old) Seniors (65+ years old) |
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Student level: | Beginner Intermediate Advanced |
Duration: | 60 minutes |
The class is taught in: | English, Italian |
About Me
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.
Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science
I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.
Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...
Education
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.
Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science
I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.
Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...
Experience / Qualifications
Technical Skills (application and often implementation from scratch):1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity
2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution
3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment
4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup
5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.
Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.
Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science
I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.
Covering university courses all around Europe: Italy, United Kingdom, France, Germany, Spain, Portugal,...
Technical Skills (application and often implementation from scratch):
1) Econometrics: Multivariate Regression, Discrete variable models (i.e. Logit), Time series models (i.e. AR/MA, ARCH/GARCH), Vector AutoRegressive model (VAR), Cointegration (Engle-Granger, VECM), Long-memory process (Fractional Integration), Regime switching models (Hamilton Filter), Kalman Filter, Unobserved Components ARIMA model, Beveridge-Nelson decomposition (Hansen's approach), Copula methods, Metropolis-Hastings algorithm, Black-Litterman model (Meucci's approach), Hierarchical Risk Parity
2) Quantitative Trading (Mid-High Frequency Trading): Stat Arb & Pairs Trading models, Order Imbalance & Order Replenishment effects on intraday returns, Optimal Setup of Entry-Exit Trading Triggers for Quant Trading Strategies, Stat Arb Bertram Model, Data sampling rules for non equally-spaced data (time vs. volume clock for high freq data), Bid-Ask Bounce Bias & Sahalia Method for Microstructure Noise Estimation & Test, Hayashi-Yoshida Lead-Lag Index, D'Aspremont Method for Mean Rev Portfolios, Market Fragmentation in Financial Markets, High-Low prices & Pivot Points trading rule, Trend Following Strategy, Avellaneda-Stoikov Model for Optimal Trading Execution
3) Risk Management: P&L production & analysis for energy trading, VaR & Profit at Risk for energy trading, Merton approach for Credit VaR with/without credit rating migrations, EVT & Copula-based VaR, Stress Test models, Structured Credit Models for Regulatory Risk-Transfer, Additional Value Adjustments for Balance Sheet, Risk Aggregation, Model Risk, Interpolation Methods for multi-year PD Term Structure, Methods for Semidefinite-Positive Corr Matrix Adjustment
4) Financial Mathematics: Longstaff-Schwartz, HJM model (Glasserman's scheme), Greeks with Finite Difference Method, CPPI Products & Cushion Multiplier Setup
5) Machine Learning: Support Vector Machine, Decision Tree, Principal Component Analysis & Regression, XGBoost, Random Forest
Expert offers private lessons in statistics and econometrics for master thesis and assignments. Regression ARIMA ARMA GARCH ARCH TOBIT LOGIT PROBIT and many other econometrics models. I have a strong experience that can help you to find your solution
Expert offers private lessons in statistics and econometrics using R Stata spss eviews gretl and many other softwares. Lessons online. I can support you for master thesis, assignments, exam preparation. I have such an experience that I can already give you a proper guidance in few minutes!
MsC in Engineering with top marks and research assistant of Econometrics for Italian top University.
Business Expert in Risk Management. Academic Research in Quantitative Finance and Algorithmic Trading.
Common discipline covered: Econometrics (with applications in R, Stata, SPSS, Eviews, Gretl), Statistics, Financial Mathematics, Quantitative Support for Master Degree Thesis (from Regressions to all statistical applications), Risk Management, Mathematics, Computer Science
I help with assignments, exams, presentations, advanced research, dissertations, big programming projects and general skill enhancement. Proficient in all major statistical packages: R, SPSS, Stata, Matlab, EViews, Gretl.
Ask a question
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from 69.71FrOnline via webcam
100% Satisfaction Guarantee
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
Online reputation
- Instructor since December 2017
- Phone number verified
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from 69.71FrOnline via webcam
Good-fit Instructor Guarantee
If you are not satisfied after your first lesson, Apprentus will find you another instructor or will refund your first lesson.
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🚀 NSI Booster – 📚 Intensive Preparation to 🌟 Shine in Your Exams!
Are you preparing for your Digital and Computer Science (NSI) exams and want to maximize your chances of success? "NSI Booster: Intensive Preparation to Shine in Your Exams!" is the course you need to revise effectively, fill in your gaps, and excel on the big day! This comprehensive and intensive program is specially designed for students in Première and Terminale to master all the key concepts of the NSI program and pass with flying colors.
Why choose this NSI preparation course?
Optimize revision time: Focus on the essential concepts to save time and revise efficiently.
Deep understanding of concepts: Don't just memorize, really understand the basics of computing to succeed in the most complex exercises.
Intensive practice: Practice with corrected exercises, exam questions and past papers to be fully prepared.
Exam Methodology: Learn how to manage your time, structure your answers, and avoid common NSI question pitfalls.
Confidence and serenity: Work in a progressive and structured manner to arrive calm on the day of the exam.
What you will learn:
This course covers the entire NSI program for Première and Terminale, focusing on the concepts most often covered in exams:
1. Algorithms and Programming:
Advanced algorithms: Algorithmic complexity, recursion, sorting and searching (quick sort, dichotomous sort).
Programming in Python:
Advanced syntax: list comprehension, lambda, decorators.
Data structures: lists, tuples, dictionaries, sets.
Functions and modularity: Decomposition of the problem, reuse of code.
File Management: Reading and writing files to process real data.
Object-oriented programming: Classes, objects, inheritance, polymorphism in Python.
Practical projects: Development of complete programs to apply the concepts covered.
2. Data Structures and Algorithms:
Lists, Stacks, Queues, and Trees: Implementation, Manipulation, and Use in Problem Solving.
Graphs: Representation (adjacency matrix, neighbor list), breadth-first (BFS) and depth-first (DFS) traversal.
Sorting and searching algorithms: Merge sort, quick sort, binary search.
Algorithmic complexity: Big-O notation, analysis of algorithm performance.
3. Databases and SQL:
Data modeling: Relational model, database design with the Entity-Relationship model.
SQL:
Select, insert, update and delete queries.
Joins (INNER JOIN, LEFT JOIN, RIGHT JOIN).
Aggregate functions and subqueries.
Optimizing queries for better performance.
Practical projects: Creation and management of databases for concrete applications.
4. Computer and Network Architecture:
Hardware architecture: Processor, memory (RAM, cache), input/output devices.
Computer operation: Instruction cycle, execution of programs in memory.
Computer networks:
OSI model and TCP/IP.
IP addressing, routing, protocols (HTTP, FTP, SMTP).
Network security: firewall, encryption, VPN.
5. Web and Front-End Development:
HTML/CSS: Structure and formatting of web pages.
JavaScript: Dynamics of web pages, manipulation of the DOM, events.
Practical projects: Creation of interactive websites to apply the skills acquired.
Methodology and educational approach:
Targeted and structured revisions: Each module is designed to cover the key concepts of the NSI program while adapting to your level and your needs.
Practical exercises and exam questions: Practice with targeted exercises and past exam questions to familiarize yourself with the exam format.
Detailed correction and personalized feedback: Each exercise is corrected in detail with a thorough explanation of the solutions.
Exam methodology: Advice on how to read and understand the statements, structure your answers and manage your time on the day of the test.
Interactive Online Classes: Classes are conducted online with the use of audio and screen sharing, ensuring seamless communication and interactive learning.
For who ?
This course is aimed at:
NSI students in their first and final year of high school who want to pass their exams with intensive and targeted preparation.
Computer science students wanting to strengthen their foundations in programming and algorithms.
Self-taught people passionate about programming and computer science looking to deepen their knowledge.
Parents who wish to support their children in their academic success in computer science.
Why choose this course?
An experienced teacher: The course is led by a computer engineer with a perfect command of the NSI program, with a clear and motivating teaching approach.
An intensive and comprehensive program: Each concept is covered in depth, with varied exercises to practice all types of questions.
Personalized support: The content and pace of the course are adapted to your level and your objectives.
Flexibility and accessibility: Learn from home at your own pace, with additional resources to deepen your knowledge.
How does it work?
Initial assessment: A personalized assessment to identify your objectives and your starting level.
Interactive online courses: Learn from home with live explanations, demonstrations and practical exercises.
Intensive training: Series of exercises and past papers to prepare you for written and practical tests.
Personalized monitoring: Receive regular feedback on your progress and advice to continuously improve.
Register now !
Don't leave anything to chance when it comes to your NSI exams. Join "NSI Booster" for intensive and targeted preparation, and give yourself every chance of excelling in your exams!
Math and Computer Science at Your Fingertips – For Young and Old!
Math and Computer Science Courses for Everyone 🌍
Do you want to improve your math skills or discover (or delve deeper into) the exciting world of computer science? Whether you're a student, a pupil, an adult looking to retrain, or simply curious, these courses are for you!
🔢Mathematics:
Academic support, refresher courses, exam preparation or simply wanting to improve...
➡️ Arithmetic, geometry, fractions, algebra, problem solving...
➡️ A method adapted to the level and pace of each child.
💻 Computer science:
Discover or master the essential digital tools today:
➡️ Office software (Word, Excel, PowerPoint, etc.).
➡️ Programming (Nodejs, Python, JavaScript, HTML, CSS, etc.).
There is no such thing as a weak person, nor is there a subject too difficult: everyone has their own method for learning, and each subject has its own method for being taught. Find your method, discover your own pace, and progress in your own way 🎯