Quantitative, Mathematical and Computational Finance Course

Why select this training course?

The use of quantitative finance, mathematics, and computational finance has become increasingly important in the financial sector due to the growing complexity of financial markets and the increasing need for accurate and efficient financial decision-making. Individuals working in these fields typically have advanced degrees in mathematics, finance, economics, or computer science and strong analytical and computational skills. Studying the Quantitative, Mathematical, And Computational Finance Courses at Rcademy will help you become a good problem solver, develop a keen interest in financial markets, have good math skills, and help you succeed in mathematical and computational finance.

What are quantitative, mathematics, and computational finance?

Using mathematical models, numerical approaches, and computer tools, quantitative finance, mathematics, and computational finance work together to assess and solve financial issues. Financial decision-making is aided by quantitative finance using mathematical and statistical tools. Specifically, it is the application of mathematical models to the tasks of evaluating risk and making sound decisions. Simply put, computational finance involves running financial simulations and problem-solving computations on a computer. e investment possibilities and oversee financial portfolios. The field of quantitative finance relies heavily on mathematics. It serves as the basis for all quantitative finance models and procedures. Computational finance refers to using computational methods and computer algorithms to analyse financial data and conduct financial modelling and simulations.

What is the need to study Quantitative, Mathematical, And Computational Finance?

Numerous opportunities await those with a passion for finance and related subjects who dedicate themselves to studying quantitative finance, mathematics, and computational finance. As financial decision-making and risk management increasingly rely on mathematical modelling, numerical approaches, and computational techniques, jobs in these areas are becoming more in demand. Individuals may improve their analytical, mathematical, and computational abilities while expanding their knowledge of financial markets, financial decision-making, and risk management via education in these areas. Risk management, portfolio management, quantitative trading, and financial engineering are just a few industries where such skills are in high demand. Learning about these disciplines may give students the tools they need to help shape the future of financial technology and tackle difficult financial challenges.

With the help of the Quantitative, Mathematical and Computational Finance Course by Rcademy, you’ll have several chances to put your mathematical prowess to use in finance. It will provide you with the information, understanding, and disposition necessary to use quantitative, mathematical, and computational finance abilities in the banking industry.

Who should attend?

Quantitative, Mathematical and Computational Finance Course by Rcademy is ideal for:

• Financial managers
• Risk and return analyst
• Corporate decision-makers
• Market trend specialist
• Managers and auditors’ accountants
• Professionals acting upon the financial decision of others
• Analysts whose primary responsibility is to identify and evaluate competitive and market trends
• Acquisition and merger specialists
• Senior managers
• Business managers

What are the course objectives?

The objectives of the Quantitative, Mathematical and Computational Finance Course by Rcademy are to enable professionals to:

• Develop a strategic and analytical eye for managing the danger associated with complex financial transactions
• Study to develop, structure, price, trade, and hedge innovative monetary merchandise
• Study the modelling methods and computational models used to price the tradable assets, for example, securities, bonds, energy, loans, and associated derivatives
• Know stochastic analysis
• Ito method and Ito integration) and the Black-Scholes technique
• Apply the primary approach to option costing in discrete time in the context of simple financial techniques
• Understand the no-arbitrage pricing concept
• Understand fundamental concepts and financial derivative tools

How will this course be presented?

The quantitative, mathematical, and computational finance course at Rcademy gets structured with scope for modification according to each batch’s professional or academic background. This Rcademy program gets presented by thoroughly analysing the training materials before the commencement of every teaching session. Therefore, it does alter according to the stages and understanding of the participants. Tutors training this program are Rcademy’s trained and skilled professionals from the course-associated field who deliver the teaching using audio-visual presentations and provide a recorded session after the class to assist the training audience in referring to the videos in case of anything. The tutor also motivates the trainees for a two-way presentation technique by using projects, group activities, and questions. Case studies, role-lays and experimental studying are among the models used in the treasure management course. The kind of model used in teaching is the do-review-study-apply method.

What are the topics covered in this course?

Module 1: Introduction

• Fixed-income securities
  – Valuation
  – Interest rate sensitivity
  – Portfolio management
• Portfolio optimisation
  – Basic mean-variance portfolio optimisation

Module 2: Option Pricing and Binomial Techniques

• Options
  – The no-arbitrage principle
• The binomial model
  – Pricing option by use of binomial methods
• An alternative binomial model

Module 3: Stochastic Differential Equations

• Introduction to the stochastic Ito process
• Introduction to stochastic Ito integral
  – Properties and definition of the Ito integral
• Into Lemma
• Application in the stock market

Module 4: The Black-Scholes Expression

• Black -Scholes expression derivatives
• Black Scholes expression solution
  – Changes to the heat equation
  – Closed-form result of put and call choices
• The Greeks: Hedging portfolio
• Expected volatility

Module 5: Monte Carlo Simulation and Random Numbers

• Pseudorandom numbers
• Transformation of random variables
  – Inverse transformation technique
  – Acceptance-rejection technique
• Generation of normal variates
  – Box-muller method
  – The polar method of Marsa glia
  – Multivariate normal variables
• Monte Carlo integration
• Option costing by Monte Carlo simulation
  – Correlated assets
• Variance reduction techniques
  – Antithetic variates
  – Control variates
• Quasi-monte Carlo simulation
  – Halton sequences

Module 6: Option Costing by Partial Differential Expression

• Allocation of PDEs
• Finite variation techniques for parabolic expression
  – An explicit model
  – An implicit method
  – Crank-Nicolson method
• Option costing by the Heat expression
• Option costing by using black Scholes expression
  – Pricing by an exact method
  – Pricing by an implicit model
  – Costing by the Crank-Nicolson technique
• Pricing options
  – Projected SQR method
• Tree methods and finite differences
  – A trinomial tree
  – A binomial tree

Module 7: The Basics of Financial Reporting

• Introduction to financial reporting concepts
• The financial position statements
• The cash flow statement
• The income statements
• Analytical review and ratio analysis
• Company governance

Module 8: Fixed Income Derivatives and Securities

• Bond market technique
  – Overview and notations
  – Notations
  – Swaps and caps
  – Valuation of primary tools: vanilla choices and zero coupons on zero-coupon
  – Black model
  – Short-rate models
  – Term structure consistent models
  – Inverting the yield curve
  – Affine term structure
  – Problems
• Credit default swap (CDS), securitisation, and exchange-traded funds (ETF)
  – Overview
  – Bond ETFs
  – Commodity ETFs
  – Currency ETFs
  – Inverse ETFs
  – Exchange-traded funds (ETFs)
  – Index ETFs
  – Stock ETFs
  – Leveraged ETFs
  – Credit default swap (CDS)
  – Example of credit default swap
  – Valuation
  – Recovery rate calculation
  – Binary credit basic swaps
  – Basket credit basic swaps
  – Collateralised loan obligations (CLO)=
  – Collateralised bond obligations (CBO)
  – Mortgage-backed securities ( MBS)
  – Collateralised mortgage obligations (CMO)
  – Collateralised debt obligation (CDO)
  – Examples

Module 9: Advanced Methods for Underlying Assets

• Stochastic volatility technique
  – Overview
  – Stochastic volatility
  – Types of progressive-time SV technique
  – Hull-white technique
  – Moment evaluation for CIR-type activity
  – Scott model
  – Stein and stein technique
  – Heston method
  – Formulation of formulae applied: mean-reverting activity
  – The stochastic alpha beta Rho (SABR) method
  – Problems
• Jump diffusion models
  – Introduction
  – The Poisson process (jumps)
  – Suitability of the stochastic method postulated
  – Regime switching jump-diffusion technique
  – The compound Poisson activity
  – The black Scholes technique with jumps
  – Solutions to partial-integral differential structure
  – The option pricing challenge
  – The general PIDE structure
  – Examples
• Generalised levy activity. Long-range correlations and memory impacts
  – Introduction
  – Stable issuing
  – Kurtosis
  – The levy flight technique
  – Self-comparison
  – The H- infinity association for levy flight
  – Sum of stochastic levy variables with various factors
  – Rescaled range analysis and Detrended fluctuation analysis (DFA) applied to economic indices
  – Total of levy random variables with multiple factors
  – Examples and applications
  – Truncated levy models applied to financial indices
  – Total of exponential random variables with other parameters
  – Problems
• Factor and copulas model
  – Introduction
  – Cross-sectional regression
  – Factor technique
  – Expected return
  – Basic factor method
  – Copula technique
  – Families of copulas
  – Macroeconomic factor method
  – problems