Nachhilfe Neuronale

Mit individueller Nachhilfe für Studenten und Abiturienten konnte ich schon einigen zum Bestehen ihrer kritischen Klausuren in den Angstfächern verhelfen. Weiterhin kann ich statistische Auswertungen mit R und Python durchführen und entsprechend interpretieren. Geduld ist selbstverständlich.

Ich gebe seit über 10 Jahren Nachhilfe. Falls Interesse besteht, bitte eine genaue Anfrage stellen: Sprich: Ziel, Genaue Beschreibung des Stoffs und Zeit.
Statistik ist für viele der Endgegner :D

I am very patient tutor.i love to teach.it is my passion

Die Unterrichten können bei mir stattfinden. Falls ich zu Dir fahren soll, sind die Fahrkosten zu bezahlen. Ich kann Mathe auch auf Englisch unterrichten. :)

Vorbereitung auf Bewerbungsgespräche, Behavioural & Tech.
Ich habe etwa mit 300 verschiedenen Bewerbern gearbeitet.
Ich kenne mich sehr gut aus. CV-Check.

I have an experience of 4 years in Engineering Colleges.The main thrust of my career development is to excel in the field of Education, with an emphasis on integrating resources into a cohesive environment that will allow the corporation to react quickly to changing technology.

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