Applying game theory to mortgage framework

To some, the retail banking market is considered an oligopoly. This is not healthy for a country's economy and its people because it is important for competition to thrive in the banking industry. Any form of market failure or anti-competitive behaviour affects productive efficiency, consumer welfare and economic growth. With the introduction of the Base Rate (BR) to replace the Base Lending Rate (BLR), Bank Negara Malaysia (BNM) seeks to inject transparency into the mortgage framework. Under this system, banks will have to disclose their margins and are not allowed to loan below the BR just to attract customers and boost loan growth. This new framework will allow customers to make better financial decisions whilst having little impact on borrowers. Even though this only applies to personal home loans, it still poses a challenge to competing banks. Those with a seemingly strong niche in consumer financing will be able to offer more attractive and competitive BR leading to better Effective Lending Rates (ELR). This paper proposes using Game Theory (GT) as a method to predict probable outcomes in strategic interactions for BR revision. Monte Carlo Simulation (MCS) will then provide a means of weighing the probabilities of the contributing factors. With the ability to anticipate the competition's next moves in a quantifiable manner, strategists will be better prepared in understanding and navigating the complex banking system

Generalized von Neumann–Kakutani transformation and random-start scrambled Halton sequences

AbstractIt is a well-known fact that the Halton sequence exhibits poor uniformity in high dimensions. Starting with Braaten and Weller in 1979, several researchers introduced permutations to scramble the digits of the van der Corput sequences that make up the Halton sequence, in order to improve the uniformity of the Halton sequence. These sequences are called scrambled Halton, or generalized Halton sequences. Another significant result on the Halton sequence was the fact that it could be represented as the orbit of the von Neumann–Kakutani transformation, as observed by Lambert in 1982. In this paper, I will show that a scrambled Halton sequence can be represented as the orbit of an appropriately generalized von Neumann–Kakutani transformation. A practical implication of this result is that it gives a new family of randomized quasi-Monte Carlo sequences: random-start scrambled Halton sequences. This work generalizes random-start Halton sequences of Wang and Hickernell. Numerical results show that random-start scrambled Halton sequences can improve on the sample variance of random-start Halton sequences by factors as high as 7000

Comparisons of Returns Between Randomly Chosen Portfolios from Indexes and Returns of Best Performing Equity Funds; Determining Whether Investors Should Pay Management Fees to Equity Funds’ Managers

2014 dissertation for MSc in Finance and Risk. Selected by academic staff as a good example of a masters level dissertation. In the extremely unpredictable stock market, especially in the period following the financial crisis, it has become very common that investors are willing to pay fund managers to make investment decisions for them and to build certain portfolios that will bring investors best possible returns. On the other hand, investors have options of building their own portfolios or investing in some existing indexes and getting the benchmark return. This paper examines and evaluates the possible returns of randomly chosen portfolios from different countries’ indexes and compares them with the best equity funds’ returns and with the benchmark return. In other words, this paper is answering the question: Should investors pay the fund managers’ fee to make investments for them? Is this management fee going to bring them enough of extra profit so that it will pay off? Key steps in these evaluations and comparisons will include gathering data for most popular Indexes’ returns of two different countries including United Kingdom and United States of America. This information will be used in building the random portfolios by using Monte Carlo Simulation method. Final results will show the mean as well as the minimum return and they will be compared with returns of best equity funds from these countries