Financial Modeling

A COMPARATIVE STUDY OF DOLLAR COST AVERAGING VS. VALUE AVERAGING

by Pawel Stefan Benedykcinski

AUTHOR: PAWEL STEFAN BENEDYKCINSKI and ADVISOR: St. Olaf College ECONOMICS PROF. RICHARD GOEDDE

A COMPARATIVE STUDY OF DOLLAR COST AVERAGING VS. VALUE AVERAGING
Pawel S. Benedykcinski and Prof. Rick Goedde... more A COMPARATIVE STUDY OF DOLLAR COST AVERAGING VS. VALUE AVERAGING
Pawel S. Benedykcinski and Prof. Rick Goedde (Advisor)
Economics Department
St. Olaf College
Northfield, MN

My research compares three investment techniques, fixed and variable dollar cost averaging and value averaging to determine if any of the methods yield superior investment returns in the long run. Mutual funds, stocks, and exchange-traded funds were used to test the methods. Value averaging is a formula-based investment technique using a mathematical formula to guide the investment of money into a portfolio over time. With this method investors contribute to their portfolios in such a way that the portfolio balance increases by a set amount, regardless of market fluctuations. Dollar cost averaging invests equal amounts regularly and periodically over specific time periods in a particular investment or portfolio. By doing so more shares are purchased when prices are low, and fewer shares are purchased when prices are high.

After testing many mutual funds, ETFs, and individual stocks, I concluded that Value Averaging yields better Internal Rates of Return than fixed and variable Dollar Cost Averaging. The results also indicate that the three methods provide superior investment returns over extended investment time periods with little increase in risk, even if prices are volatile. One important difference between these three formula investment techniques is that value averaging requires larger sums of money to be invested at regular time intervals than fixed or variable dollar cost averaging do.

The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows

by Josep Perello

Evaluating currency crisis: A multivariate Markov regime switching approach

by Dimitris Kenourgios

K. Mouratidis, D. Kenourgios, A. Samitas and D. Vougas, “Evaluating Currency Crises: A Multivariate Markov Regime Switching Approach”, The Manchester School Journal, forthcoming.

Sheffield Economic Research Paper Series SERP Number: 2010018

by Dimitris Kenourgios

Modeling Macroeconomic Effects in Central Eastern Economies Stock Returns

by Dimitris Kenourgios

Samitas, A. and D. Kenourgios “Modeling Macroeconomic Effects in Central Eastern Economies Stock Returns”, in Advances in Financial Forecasting, Lecture Series on Computer and Computational Sciences, Vol. 4, pp.1345-1348, VSP/Brill Academic Publishers, 2005 (ISSN: 1573-4196).

Modelling return and volatility in emerging stock markets: A Markov switching approach

by Dimitris Kenourgios

D. Kenourgios and A. Samitas (2009) “Modelling return and volatility in emerging stock markets: A Markov switching approach”, International Journal of Economic Research, Vol. 6, No. 1, pp. 61-72.

Ruin Probability: A Flexible Approach for Measuring Portfolio Risk

by Andrei Gonçalves

Co-authored with Aureliano Bressan

We provide a simple and flexible numerical based approach to measure portfolio risk. It measures the chance of one or... more We provide a simple and flexible numerical based approach to measure portfolio risk. It measures the chance of one or more specific undesirable financial events, defined as Ruins, for a predetermined investment horizon. The method is based on the Ruin Theory and is flexible enough to provide information about several financial events of concern at the same time and still relate them. We also conduct a simulation analysis using Bootstrap and Monte Carlo methods in order to exemplify how this approach is able to solve some practical risk management problems.

Actuarial Injustice Model: Statistical Arbitrage Opportunities in the European Option Market

by Andrei Gonçalves

Co-authored with Aureliano Bressan

This study presents a model for statistical arbitrage opportunities (SAO) detection in the European option market,... more This study presents a model for statistical arbitrage opportunities (SAO) detection in the European option market, named Actuarial Injustice Model (AIM). The hypotheses assumed for the model’s creation are the same as those proposed by Black and Scholes (1973). The AIM is based on the creation of a zero net investment portfolio that presents positive future expected payoff and is composed by positions in two alternative option series related to the same underlying asset. A simple strategy for portfolio creation based on the AIM was developed and empirically verified. The empirical evidences suggested that AIM based strategies created zero net investment portfolios that presented positive and statistically significant future expected payoffs in the Brazilian option market during the period ranging from January 2006 to December 2010. To summarize, theoretical and empirical evidence were presented in order to support the validation of the AIM as a model for SAO detection in the European option market.

Lambert W Random Variables - A New Family of Generalized Skewed Distributions with Applications to Risk Estimation

by Georg Goerg

Published in the Annals of Applied Statistics
http://arxiv.org/abs/0912.4554

Originating from a system theory and an input/output point of view, I introduce a new class of generalized... more Originating from a system theory and an input/output point of view, I introduce a new class of generalized distributions. A parametric nonlinear transformation converts a random variable X into a so-called Lambert W random variable Y, which allows a very flexible approach to model skewed data. Its shape depends on the shape of X and a skewness parameter γ. In particular, for symmetric X and nonzero γ the output Y is skewed. Its distribution and density function are particular variants of their input counterparts. Maximum likelihood and method of moments estimators are presented, and simulations show that in the symmetric case additional estimation of γ does not affect the quality of other parameter estimates. Applications in finance and biomedicine show the relevance of this class of distributions, which is particularly useful for slightly skewed data. A practical by-result of the Lambert W framework: data can be “unskewed.”

The R package LambertW developed by the author is publicly available (CRAN).