The Effects of Non-Tariff Measures and Trade Facilitation - Assignment Example

Trading simulation report An asset, being a balance sheet item that has some economic value, is owned by a corporation and easily translatable to cash. An example of such is bonds and shares. In this case, I shall invest shares worthy £2000000 in bank and on the other hand, a bond of nominal value £1500000 that has 12 month $ Libor interest is 0.54670%. Therefore, the interest that will be earned is: (0.54670/100)× 1500000 = £8505 the total to be earned is 1500000+8505 = £1508505 Simulation of an optimized portfolio (engine portfolio results) The diagram above shows the results of 21 days, of how the variations cancel each other in a non- linear nature (Li et al., 2012). The portfolio engine results for non-optimized simulation From the above two parralell simulated portfolios, the portfolio that is optimised produced results that are unswerving to the market performance while the non-optimised portfolio produced inconsistent returns at some point in time hence in calculating I used the optimized portfolio results (Andersen et al., 2011). The simulation was carried out 21 times in three weeks. In calculating the value at risk and time, after analyzing the data variance at monthly confidential interval of 90% had –14.5% the daily variance = -14.5/(square root of 21) = - 3.16 At a daily confidential interval of 98% it is found to be -17.25% daily variance will be -17.25/(square root of 21) = -3.76. This, therefore, means that the volatility of money increases or reduces with square root of period of time. This happens because money has equal chances of increasing or decreasing hence the variation may compensate each other. In this case, if an investor invests £2000000 then the value at risk is (14.5% *2000000) = £290000. If the loss exceeds 30% of the portfolio then it is too risky. In our case (290000/2000000)* 100 = 14.5% The portfolio risk and return on this investment is not critical and hence one can invest in shares though the shares suffer from the following setbacks: The risk of market value: this risk involves down turn of an investment and chasing other good things in the market leaving the company behind. The market indices in Bloomberg is this a clear indication that the investment can be feasible because for a short period of time calculated, it has a positive net present value (Hua & Liu, 2010). This is so as most of the variances observe for the last 21 days cancels each other leaving some positive value. In the long-term, the profit index will be greater than one. When one invests in bonds as the alternative asset of the same class, it incurs inflations in the market. In our case, the inflation is 15% meaning invested amount will lose the value (15%*1500000) = £225000. The overall interest at the year-end will be £8505 while the value of money will have depreciated by (225000 – 8505) = £216495. This is the persistent rise in prices of shares hence reducing the time value of money at given point in time. It interferes with the investors as it dilutes their income value. From the exercise performed I have learned that trade simulation is a sophisticated method that enables one to analyze the feasibility of an investment without financial risk. The exercise has enabled me to know the time value of money in that the income loses value as days go by. I have comprehensively understood the effect of market forces to the value of shares over a period of time. In portfolio analysis the method that should be followed in calculating the financial risks is through the use of Monte Carlo simulation method. The method uses the numbers randomly, the Z score as standard deviation, price and percentage change to compute the returns and risks. The method is cost effective, reliable and fast in calculation of the financial risks. I have a negative attitude towards risks because risk is a predicament to my goal of investing and earning returns from any investment. Trading simulation report Trade simulation is the sophisticated method of testing a risk free trading in the current time using today’s market devoid of risking ones money. It uses trade simulant that enable one to explore new market opportunities and current market strategies without risking ones sterling. It is of vital importance that one can duplicate live market on his computer device to practice trading stocks short of financial jeopardies involved. This can be done by manipulation of fictional starling and the stock positions that perform in a way like of existent market. Portfolio engines are responsible in doing most of the calculations by use of calculators to evade the arithmetic errors. Investors use different investment strategies and compare the performance of each strategy and consequently choose the best strategy that is feasible according to their available resources. Forex alongside Bloomberg and portfolio engines are the advanced strategies. In trading simulation, there are two simulators namely financial and fantasy simulators respectively. Financial simulator is a stock market simulator that allows an investor to generate a portfolio based on real stock entries. This simulator is to let one practice with fantasy funds in real world so to determine whether they gain or they lose in the investments (Finke & Huston, 2003). Fantasy simulator on other hand facilitates the investor to trade shares of real world objects that habitually would not be scheduled on a commodity. The past performance of investments whether indicated historical strategies or the actual one is not a guarantee to betterment of future success of it. Investments have contingencies and therefore there is a likelihood of a loss or a gain of the investment irrespective of the type of asset one trades. Before one decides to invest in an investment, it is sagacious that one considers the market effect of market volatility, trading approaches, enactment graphs, the historic price indexes and price earnings ratios (Fouque, 2000). Therefore in general market simulators are tools of great importance as they give the investors to test and adopt feasible trading strategy based on their resources. The fundamental investment drive is to realize the financial goals with the welfare of the employees in an organization. The welfare includes apt remuneration and retirement plans. Therefore, to achieve the objective, an investment methodology has to be implemented. Professional management and other advices are relevant to investment. The investment process, requests to exploit the risk-adjusted yields of the portfolios by heightening asset type disclosures, performance of managers and other relevant physiognomies from available investments. Generate tailor-made, differentiated portfolios for every client choosing from amid existing investment choices. Effectively managing the portfolio uncertainty levels and investment apportionments on a continuing basis as flea market and individual situations do alter. The approach for portfolio building is to endorse an unswerving differentiated investment tailored to the wishes and time length of every client. To unequivocally evade any timing of the market, we use the market agreement to enable us estimate the expected returns from each type of asset. The axioms under this are that the costs of each type of asset are equitably valued by the market and that market accord prospects are the top forecaster over the forthcoming predictable returns. In this case we do not disclose the assets that are either under cast and/or overcast as it makes it difficult to predict. With the current market, allocations of portfolio use the risk assumptions that are consistent with the market. The forward looking risks assumptions are elastic to the portfolio allocations in that the market allocations changes the risk-premiums are rationalized. This technique will prevent the tendency of biasness when it comes to process of allocating the portfolio which might lead to the introduction of annoying unpredictability. The investment process includes accumulation of assets for building of the mean and the variance in portfolio optimization. In doing so, estimation of the current economic expectations like dividend, interest rates and inflation need to be considered. The correlations and class estimation of the asset’s predetermined returns are considered too. The trade simulation of investment strategies should not be outlawed. Estimation of core economic expectations of an organization In an event of investing, a pricing model “pricing kernel” is used to describe the improvement or scaling down of the financial economy by use current market conditions prevailing in the market. The model takes in account the three factors namely as the dividend growth of the company, inflation prevailing in the state and the interest rates. The additional parameters to be included in the model are the equity in long-term basis and the interest rate that a company estimates. The pricing kernel model is realistic one as it takes into account the returns of the assets unlike other models. It therefore offers a situation of how specific assets may perform dependently and how they can be simulated. The models input and output The inputs in the model describes the evolvement of the interest rates, the growth of dividends, the interest rates prevailing and the current market conditions of each variable. The inputs play a major role in defining the correlation existing between the variables. The outputs on other hand are helpful in determining the expected returns and connection of different assets. This can be illustrated in a diagram as below Core asset class Expected returns Volatilities and correlations In the diagram above, it is clearly shown that the three inputs were developed in ancient time using the historic information for analysis of the financial low-cost. In the research, the other method that frequently used was the statistical methods like the use of chi- square method to measure the correlation. If the investor for instance wants to measure the behavior of inflation in the economy, one would incorporate the seen inflation rates of future based on the current rates of inflation (Malevergne & Sornette, 2006). The inclination for inflation rates to return towards in long-term as well as the assets of alterations in inflation to become more unpredictable when rise rates are great. In this case, there is a haphazard innovation tenure that echoes the accurate possibility of unforeseen surprises. The outputs on other side form the foundation of general asset classification model. The predetermined volatilities, the returns and expected correlations of important assets are steady with the calibrated “pricing kernel” and the prevailing economic market conditions. The model can give a forecast of equally distributed expected returns over a certain period of time frame. The pricing kernel has got two key principles that make it more efficient than any other model that an investor might think of. The foremost principle is called “arbitrage free” pricing. Under the principle, it is theoretically and practically ineffectual to receive guaranteed payoff minus an original cash expense. Secondly, it generates returns to the assets that are inter-temporally and others cross sectional steady. This openly reveals that each variable replicates the applicable performance throughout the period and the relationships between the variables are applicably exhibited at any given time. However, the model suffers some setbacks in the operation. Due to the discretionary principles that govern the model, it would most likely compromise to project a simultaneous inflation in low yielding bonds. The model also has a disadvantage of not projecting a two digit inflation rates in one economic year then consequently followed by an economic year of deflation as it becomes confusing and cumbersome too. The class of assets is found by use of Generic Asset Class model will financially generate a forward skewed return using the classes as the factors to be considered. This is chose to ensure that on investor do not specify so much on one model. This ensures that the performance of most financial assets can sufficiently describe by use of class. The objective limiting the span of this model is to include only the class with risk factors that are priced separately. The model takes the inputs of class asset and generates the expected returns alongside the covariance. The model comprises the short-term fixed returns with a maturity of up a year (Worthington & Higgs, 2006). The expected returns of assets are calculated by use of past data to estimate the covariance and the correlations on monthly basis. The historic returns are unrealistic as they do not help in future decision making process. Another disadvantage is that the data available for calculation of the returns is not adequate. To counteract the “reverse optimization” the only possible way is to skew forward the returns data available in the market portfolio, The modeling of the investments Modeling of investments takes place every month to attract investment contracts. The main reason as to why this is done is to get the economic characteristics of every investment at a time. Some of these characteristics include the market forces. The process estimates the risks involved in an investment and make relevant adjustments based on the type of security such as portfolio turnover, efficiency of tax and the management performance (David, 2009). After all these factors have been considered, modeling then provides the future benefits that are likely to occur. Fund modeling Portfolio online engines analyze and evaluate on monthly basis the mutual funds of various institutions by use of exclusive methods. They consider both the non- retirement accounts and the retirement schemes. The portfolio achieve this in a balanced way that is of due diligence, regular review of the input data and solicitation of safety scrutiny models. The returns based style analysis The asset allocation for a huge part of variation in returns of an investment portfolio is noted. Return based analysis is relevant in determining the contribution of asset to the portfolio improvement. The examination uses the monthly data on returns to identify the real combination of the assets indices to imitate real performance of the stock (Nummelin et al., 2004). This result is known as “style benchmark”. In an example if the fund has a large cap growth and has an exposure of 3% cash, over 50% large cap growth stocks, and 12% equity. The analysis reveals that the funds do not only expect exposures but also the unforeseen possibility of loss as those of equity and cash. These unforeseen exposures are dependent of funds in a number of asset classifications. Simple classification of funds results into a miss in persistent risks and contributing to the performance of the whole fund (Chuck, 2014). Due to this setback, a benchmark is called for. Measurement of the probable returns involves putting into consideration factors backing to possible forthcoming performance. A baseline predictor defined by the probable return of weighted investment style, adjusts the expenses of the fund, trading costs and finally management expenditure. It is a good estimator especially when a product is inertly managed there is low tracing error of fund compared to its great targeted index. It is however critical to measure the qualitative aspect of manager performance and at the same time measure the quantitative phase of funds. The portfolio engines use investment custom stylish to adequately and completely measure the performance of a manager and fund. Manager performance is measured by use of alpha of funds’ performance in relation to revenues of zero cost index funds in the similar investment elegance (Tressel et al., 2007). If the result is a negative alpha, there is underperformance while when the result is a positive alpha, then there is an improvement in performance. Additionally, there some incidences where funds are more tax efficient than other times due to the susceptibility to dispense dividends and may be short and/or long-term capital gains. Qualitative due diligence and miscalculation The portfolio engines ensure that funds are vouched in a professional manner and as per the international standards of auditing. The engines also carry out a qualitative aspect to recommend on periodic services of advice (Sanwal, 2007). The review discovers the following concerns: the pending lawsuit involved in funds, the vouching of the accounting procedure used in preparation of accounts, the analysis the funds of the service providers company such as administrator and independent auditors and employees in general. Review of the probable for enhancement of accuracy of fund data. Portfolio simulation Portfolios engines help understand the anticipated outcomes that occur from different investment strategies. The engines helps plan effectively and reach sound decisions about appropriate uncertainty levels, reserves and horizon of time to improve the prospect of attainment of financial aims (Edelman, 2007). Simulations consider specific security characteristics like investment style, industry risk, manager performance, expenses, security- specific and distributions (Dee et al., 2005). There are more concerns of forecasting the portfolio of households, taxable accounts and even tax- deferred. This also includes social security the pensions, retirement income and/or retirement benefits (Gilbert & Tower, 2013). The main aim of simulation is to generate feasible scenarios for how a portfolio of assets may perform through a wide variety of market conditions. It mainly consist the following steps: Take for example current economic variables like inflation and interest rate. Using the discussed “pricing kernel” model, create possible ways for vital assets. This yields the so called arbitrage-free. Map each generic asset class onto a combination of the core asset set-ups drawing random invention term of probable residual variances asset variance. Again map every stock by use of investment style while adjusting returns so that you can account for portfolio turnover and other expenses (Brink & Viviers, 2003). Calculate the value of each portfolio at every single step. Future distribution of the portfolio is the last thing to do across many scenarios over a period of time. The portfolio engines ensure consistency among core economic variables and returns (Cumming, 2010). Simulation considers growth in salaries, contribution to the state, the dividends of the organization. The incorporation of such complexities balances the participants. The portfolio balances are converted into retirement fund since most of the investors receive from social security income into equivalent basis to an inflation adjusted income tributary that lasts (Rajegopal at el., 2007). This approach is to help one understand the living standards. The assumption on this approach is that income is assumed to last for life. Generating portfolio recommendations There are two inputs in recommendations. One is the information concerning the future market expectations and knowledge about the preference investor. Risk tolerance is the key factor to consider when it comes to portfolio optimization. The assumption of risk tolerance can effect to a tradeoff between saving for the future and the consumption rate (Bansal et al., 2007). Most investors in world market are risk takers at their young age while horizon investment is longer. Apparently, the factors that influence individual tolerance of risk include ones job, health, appetite risk and job securities. The beginning of risk tolerance of a large population is segmented into horizon of investment based on their retirement ages and their present age (Jones, 2002). A relevant situation is where a management program allows one to choose either high or low risk tolerance corresponding to 30th and 70th percentiles. The tolerance of investors trends down as the retirement age approach. On other hand, risk distributions heighten with the age advances. Year 70th percentile Medium risk 30th percentile 2035 92 84 68 2030 90 78 60 2025 88 75 55 2020 80 68 45 2015 76 54 30 2010 70 48 15 In the above diagram it is clearly shown that indeed the risk grows bigger as the age of retirement continues. In conclusion, investors need to analyze the risks, the returns, the profitability index and overall profit that can be generated from an investment. This is of great importance in making sound financial decisions. One should be able to watch out the contingencies that surround the economic environment like inflation, market value of shares and the discretionary policies before they make a point of investing. Bibliography Andersen, T. G., Bollerslev, T., Christoffersen, P. F., & Diebold, F. X. (2011). Financial Risk Measurement for Financial Risk Management. Bansal, R., & National Bureau of Economic Research. (2007). Long-run risks and financial markets. Cambridge, Mass: National Bureau of Economic Research. Brink, N., & Viviers, W. (2003). Obstacles in attracting increased portfolio investment into southern Africa. Development Southern Africa, 2(3), 46-194. doi:10.1080/03768350302958 Chuck, J. (2014). 9 financial risks everyone should understand - Chuck Jaffe - MarketWatch. Retrieved from http://www.marketwatch.com/story/whats-your-greatest-financial-fear-2014-06-13 Cumming, D. (2010). Private equity: Fund types, risks and returns, and regulation. Hoboken: John Wiley & Sons. David, R. (2009). How to Reduce Financial Risk: 5 Steps (with Pictures) - wikiHow. Retrieved July 3, 2014, from http://www.wikihow.com/Reduce-Financial-Risk Dee, P. S., & Ferrantino, M. J. (2005). Quantitative methods for assessing the effects of non-tariff measures and trade facilitation. River Edge, NJ: World Scientific Pub. Edelman, R. (2007). The lies about money: Achieving financial security and true wealth by avoiding the lies others tell us-- and the lies we tell ourselves. New York: Free Press. Finke, M. S., & Huston, S. J. (2003). The Brighter Side of Financial Risk: Financial Risk Tolerance and Wealth. Day Care & Early Education, 1(2), 87-154. doi:10.1023/A:1025443204681 Fouque, J. P., Papanicolaou, G., & Sircar, K. R. (2000). Derivatives in Financial Markets with Stochastic Volatility. Gilbert, J., & Tower, E. (2013). An introduction to numerical simulation for trade theory and policy. New Jersey: World Scientific. Hua, C., & Liu, L. (2010). Risk-return Efficiency, Financial Distress Risk, and Bank Financial Strength Ratings. Jones, C. P. (2002). Investments: Analysis and management. New York: J. Wiley & Sons. Li, C., Whalley, J., & National Bureau of Economic Research. (2012). Chinas potential future growth and gains from trade policy bargaining: Some numerical simulation results. Cambridge, Mass: National Bureau of Economic Research. Malevergne, Y., & Sornette, D. (2006). Extreme financial risks: From dependence to risk management. Berlin: Springer-Verlag. Nummelin, K., & Svenska handelshögskolan (Helsinki, Finland). (2004). Expected asset returns and financial risks: Some empirical on Swedish data. Helsingfors: Swedish School of Economics and Business Administration. Rajegopal, S., Philip M., and James W. (2007). Project Portfolio Management: Leading the Corporate Vision. Basingstoke: Palgrave Macmillan. ISBN 978-0-230-50716-6. Sanwal, A. (2007). Optimizing Corporate Portfolio Management: Aligning Investment Proposals with Organizational Strategy. Wiley. ISBN 978-0-470-12688-2. Tressel, T., Verdier, T., & International Monetary Fund. (2007). Financial globalization and the governance of domestic financial intermediaries. Washington, D.C: International Monetary Fund. Worthington, A. C., & Higgs, H. (2006). A Note on Financial Risk, Return and Asset Pricing in Australian Modern and Contemporary Art. Journal of Cultural Economics, 1(1), 72-165. doi:10.1007/s10824-005-9000-5 Read More