Research Methods in Quantitative Finance

19 Pages Posted: 14 Jun 2016

See all articles by Paul Cottrell

Paul Cottrell

affiliation not provided to SSRN

Date Written: June 12, 2016


In this Breadth component, an analysis of research methods in quantitative finance pertaining to experimental, cross-sectional, quasi-experimental, pre-experimental, sample design, econometric models, and stochastic simulations is put forward. A compare and contrast will be performed for each of the above research methods in quantitative finance. This component will include a discussion of how these methods contribute to better understanding on hedging and pricing of financial assets. A discussion of the strengths and limitations of each research design method investigated pertaining to quantitative finance will be put forth.

Experimental design is an important research method due to the power of inference. Frankfort-Nachmias and Nachmias (2008) proposed four major types of research design with differing degrees of inference and safeguards. The four major types of research design proposed by Frankfort-Nachmias and Nachmias are: (1) experimental, (2) cross-sectional, (3) quasi-experimental, and (4) pre-experimental (p. 103). Experimental designs involve assigning random samples to the experimental and control groups with independent variables applied to the experimental group. Cross-sectional designs usually are associated with survey research, whereby manipulation to determine inference is diminished compared to experimental design. Quasi-experimental research involves random sampling from a population but not random assignment within testing groups. Pre-experimental are the weakest form of research methods due to the lack of random selection and assignment.

In sample designs there are probability samples and non-probability samples (Frankfort-Nachmias and Nachmias, 2008, p. 167). Frankfort-Nachmias and Nachmias (2008) considered three types of non-probability sample designs: convenience, purposive, and quota samples (p. 168). Frankfort-Nachmias and Nachmias also considered four probability sample designs: simple random, systematic, and stratified (pp. 169-173).

Another important set of tools in research design, especially in the field of finance is econometrics. Econometrics is utilizing regression analysis to determine relationships between dependent and independent variables. This breadth component will also provide an investigation by Wooldridge (2009) in three broad categories in econometrics: introduction to econometrics, simultaneous equation models, and advanced time series topics. Simultaneous equation models are a set of system of equations that describe financial and economic systems. A review of time series analysis for the field of finance and economics is important to understand the temporal dynamics of the investigated variables. For example, a time series analysis on price of the S&P 500 might reveal certain volatility dynamics.

In the stochastic modeling section a review of some random sampling methods and how to improve on the random sampling homogeneity are investigated. In complex financial problems Monte Carlo simulations are frequently utilized with stochastic processes built in the simulation. This type of research method can help in making a more realistic and statistically sound research design. Stochastic models can also help in forecasting price curves when the simulation parameterizes a Levy process, whereby the Levy process defines the drift, volatility, and jump-diffusion components of the historic time series. By taking the Levy process parameters the Monte Carlo simulation can generate probability paths of the price curve of interest.

In the conclusion section an important question was examined. How to best construction a research study that is related to quantitative finance research questions, especially relating to time series data from asset markets? It’s within the intent of this Breadth component to answer this important question by using different research methods in quantitative finance pertaining to the categories of experimental, cross-sectional, quasi-experimental, pre-experimental, sample design, econometric models, and stochastic simulations.

Keywords: Research Methods, Quantitative Finance, Statisitics

JEL Classification: C10, C49, C5, C53, C63, E17, E30, F47

Suggested Citation

Cottrell, Paul, Research Methods in Quantitative Finance (June 12, 2016). Available at SSRN: or

Paul Cottrell (Contact Author)

affiliation not provided to SSRN

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