Hededge funds Essay Example | Topics and Well Written Essays - 1750 words

Hededge gold - Essay Example compact of Data The data used for this exercise consists of periodical returns and Assets chthonian commission (AUM) everywhere a period of ten years (September 1999 August 2009) for over 28 table funds. The randomness on these sample distribution hedge funds was obtained from the EurekaHedge database which stores information on over 21,000 hedge funds. The sample hedge funds come through one of the following strategies typical of financial institutions operating in this domain gigantic/Short Equities CTA/Managed Futures Multi-Strategy merchandise The table below details results of the results from the three-card monte Carlo and the Historical simulation methods using the sample data. Historical four-card monte Carlo prob Not Losing prob Number of tend 50% Not losing Not Losing prob Number of run 50% Drift SD Mean 98.90% 9814 13.05% 1,354 98.95% 9865 13.10% 1319 0.45% 1.59% 0.46% Arbitrage 86.56% 8600 53.01% 5,363 86.02% 8532 53.20% 5 371 0.76% 5.63% 0.92% CTA/Managed Futures 87.53% 8729 58.98% 5,944 85.10% 8434 56.77% 5779 0.86% 6.65% 1.08% Multi-Strategy 86.98% 8611 55.45% 5,595 87.10% 8661 52.53% 5331 0.74% 5.19% 0.88% Long/Short Equities The above results show that the mean monthly returns (lowest to highest) for each fund class are 0.46% (Arbitrage), 0.88% (Long-Short Equities), 0.92% (CTA-Managed Futures) and 1.08% (Multi-Strategy). The dispersion (Standard Deviation - SD) of returns among these fund classes follows the same order suggesting that Arbitrage funds have the lowest mean and the lowest SD while Multi-Strategy funds exhibit the highest mean and highest SD. Summary of Approaches used The analysis uses both the three-card monte Carlo and the Historical simulation methods for answering the key questions listed previously. The Monte Carlo pretence method depends on the formulation of an appropriate model that can suitably explain and analyze the monthly returns used as input for this analysis. To m odel the behaviour of these monthly returns, the concept of geometric Brownian Motion (BM) was undertake (Rubinstein, 2008). The BM used in this context is a Markov Process which simply means that the monthly returns follow a random walk and exhibit behaviour consistent with the weak form of the EMH (Efficient Market Hypothesis) (Robert, 2004). This implies that the Monte Carlo method in this case utilizes the fact that movements in monthly returns are conditionally independent from much(prenominal) movements during previous periods. Under the Monte Carlo Method, a number of iterations for each test case was conducted to analyze the settled model configured using a sequence of random numbers generated as inputs. This simulation technique is especially useful when modelling non-linear, uncertain and complex parameters (Hammersley, 2005). On an average, the current simulations utilize between 5500 and 9000 iterations under any given test case. The Historical Simulation method, als o known as back simulation, is reference of the Value at Risk (VaR) approach which also utilizes a large number of iterations like the Monte Carlo method. As the name suggests, the Historical method depends on past information on monthly returns (unlike the Monte Carlo method that depends on random input) and simulates useful results through the construction of a CDF (Cumulative Distribution Function) of these monthly returns over time) (Dowd, 2009). Key Findings Monte Carlo Method On the question of the chances of