Quantitative Finance

Finance is a professional field dedicated to the analysis, management, and planning of financial strategy, transactions, and records. Financial professionals are accredited fiduciary practice leaders qualified to oversee the financial strategy of an organization, partnership, estate or trust fund, and its portfolio of accounts. Demonstration of higher mathematics competency is required of students studying toward a degree in finance. Finance degrees programs typically demand students perform calculus of financial statements (i.e. ratios) and market activity. Students may study the regulation of financial practices and transactions, as well as rules for stock market exchange laid forth by national and intra-jurisdictional bodies responsible for the oversight of investment agreements and trade. 24HourAnswers is responsive to the needs of students training for a degree in Quantitative Finance. Our team of highly qualified tutors are subject matter experts with the knowledge to assist students in meeting their finance coursework and credential objectives in Science, Technology, Engineering, or Mathematics (STEM) subjects.

Here are some insights from the field of Finance on the topic of Quantitative Finance:

With the advent of FinTech, the global financial services industry underwent major transformation. Offering rapid transaction processing and increased security, the complexity of these newly introduced technologies also brought on new regulatory and structural reforms. FinTech expansion across global banking and finance institutional operations and services sectors has created the conditions for how companies perform transactions, and with it, business growth. Today, asset management firms, banks, exchanges, software providers, regulators and other industry segments engaged with FinTech operations demand highly skilled technical professionals to meet their financial market objectives.

An Interdisciplinary Field

Quantitative finance is an interdisciplinary field that involves training in higher mathematics (i.e. calculus and statistics), stochastic processes, data analysis, artificial intelligence and machine learning, coding, and technical writing. Quantitative financial professionals are trained in the creation of algorithmic trading strategies, as well as the mathematical risk calculus required to compute commodity, stock, and mortgage-backed securities pricing schemes, as well as develop and validate capital risk models for measurement of structural financial opportunities and regulatory compliance reporting.

The following is an inexhaustive list of a typical Quantitative Finance degree program course subject matter:

Statistical and econometric analysis

Statistical inference and hypothesis testing, including regression methods, and econometric analyses

Probability distributions in finance

The law of probability allows for understanding of common distributions of financial mathematics; characteristic functions; asymptotic; CLT; and LLN.

Time-series models: random walks, ARMA, and GARCH

Random walks and Bernoulli trials; basic properties of linear time series models (AR(p), GARCH(1,1); MA(q); recursive calculations for Markov processes; first-passage properties; applications to forecasting and trading strategies.

Continuous-time stochastic processes

Continuous-time limits of discrete processes; introduction to calculus; solving differential equations of finance; properties of Brownian motion applications to derivative pricing and risk management.

Numerical models

Monte Carlo simulation; quadratic programming.

Optimization

LaGrange multipliers and multivariate optimization; Markov decision processes and dynamic programming; inequality constraints and quadratic programming variational methods; applications for portfolio construction, algorithmic trading, and execution.

Linear algebra of asset pricing

Covariance and correlation matrices; Review of axioms and operations of linear spaces; applications to asset pricing.

Applied computational techniques

C, C++, Java, MATLAB, Mathematica, R, SAS, SQL, and Python.

Applied QF Professional Specialization

Like other finance specializations, quantitative finance professionals perform mathematical and statistical analysis of large-scale data sets to analyze the financial markets and for definition of liquid asset or pricing structures, including for use in digital high-frequency trading exchange platform environments. Derivatives pricing and risk management are commonly part of the job description of a quantitative financial professional responsible for algorithmic trading, electronic market making, statistical arbitrage, and quantitative investment management. Major firms invest in model validation (MV) of their quantitative libraries to determine their validity and correctness by way of risk measurement techniques (i.e. Monte Carlo analysis). Risk analysis is also applied to institutional structural capital stress tests and market volatility estimates.

Distinct from financial engineering, quantitative finance focuses primarily on mathematical risk models. Financial engineering applies those models in building FinTech tools for purposes of implementation of the mathematical theory of quantitative finance in computational trading, price, hedge, and other investment decision simulations. Depending on the stage of FinTech development, a quantitative financial strategy may employ both a quantitative financial analyst and financial engineer or developer.

Expert level knowledge of C, C++, Java, MATLAB, Mathematica, R, SAS, SQL, and Python programming is required of both analyst and engineering functions of quantitative finance, as well as mathematical calculation of algorithms, differential equations, linear algebra, multi-variable calculus, probability theories, and statistical analysis. Formal economics or econometric (i.e. game theory, time-series analysis) training furthers the potential of a quantitative finance specialist beyond a support role in trading and risk management operations.

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