N1569 FINANCIAL RISK MANAGEMENT (Research Paper Sample)

N1569 FINANCIAL RISK MANAGEMENT: ESS A1 Student First and Last Name Course Name Instructor First and Last Name University Name January 07, 2023 Introduction The rate at which financial risk is increasing has increased dramatically in recent years. Since the turn of the previous century, economic globalization and financial integration have greatly increased the interconnection of the global macroeconomic market. The global economic and financial market is expanding at a breakneck pace, and with it comes a slew of new financial innovations and structured finance that, although they serve to mitigate risk in certain situations, may also pose risks of their own due to their complexity and specificity. For these reasons, the financial risk faced by banking institutions is becoming steadily more severe. The term "financial risk" is often used to describe the danger of economic damage that stems from the inherent unpredictability of economic transactions (Kirikkaleli 2019, p. 10). Market risk, credit risk, operation risk, liquidity risk, and other similar financial hazards are all examples. The risk associated with the financial markets is unique among other types of financial uncertainty. A risk associated with the financial market affects all financial assets and is frequently the root cause of other forms of financial risk. The price fluctuations of financial instruments are a primary source of financial risk. Financial risks are rising due to the increased diversity of financial products and commodities. For investors, a company's beta indicates how far that stock deviates from the market average in terms of risk. A stock's beta is determined by comparing its return history to a benchmark index like the S&P 500. If we have 20 different periods and ten different stock returns, we can get the average return by adding up all the rates of return and reducing by 20 (Corelli 2019, p.520). A stock's beta indicates its volatility relative to the market as a whole. If the stock's beta is 1, it is as volatile as the market; if it is larger than 1, it is more volatile than the market, and if it is less volatile than the market, it is less unstable than the market. The beta can be used in the capital asset pricing model (CAPM) to figure out how much an investment is expected to earn (Corelli 2019, p.530). When weighing the possible reward against the risk of an investment, it is crucial to consider this. Data Description For an exponentially weighted mean of 3-0.95: Therefore, you should choose a weighting factor between 0.3 and 0.95 to arrive at an exponentially weighted average of the returns from each stock. By putting more emphasis on more recent returns and less on earlier ones, an exponentially weighted average emphasizes the trend of increasing returns over time. The weighting factor controls the pace at which the weights fall. With a weighting factor of 3-0.95, the most recent return would be given the same weight as three of the most recent returns from the combined three periods. The company might expect this rate of return from a risk-free investment, like a connection to the U.S. Treasury. This is the return one might anticipate from the market, as represented by an index such as the S & P 500. To put it another way, this metric assesses the asset's volatility compared to the market (Boussetta 2022, p.120). With the data supplied, we can apply the following calculation to determine the anticipated return of the asset: Expected return = risk-free rate + beta * (expected return of the market -risk-free rate) If the risk-free rate is 2%, the expected return of the market is 10%, and the beta of the asset is 1.5, the expected return of the asset would be: Expected return = 2% + 1.5 * (10% - 2%) = 2% + 1.5 * 8% = 2% + 12% = 14% Recent data are given a greater weight in an exponentially weighted average, a moving average, while older observations are given a smaller weight. The weighting factor sets the pace at which the weights decrease. The weighting factor can be changed to make the average more or less responsive. Changing the weighting factor can make the average more or less responsive (Boussetta, 2022, p.109). We must first give each observation a certain value to arrive at an exponentially weighted summation. With exponentially decreasing weights for previous data, most respondents would have the greatest weight. The exponentially weighted average may be calculated by multiplying each measurement by its weight and then adding the products (Li & Yuan 2019). The following returns for a stock: 0.05, 0.03, 0.01, and 0.02. Using a weighting factor of 3-0.95, the weights for these returns would be: 0.95, 0.95 * 0.95, 0.95 * 0.95 * 0.95, and 0.95 * 0.95 * 0.95 * 0.95, respectively. The exponentially weighted average of these returns would be: (0.05 * 0.95) + (0.03 * 0.95 * 0.95) + (0.01 * 0.95 * 0.95 * 0.95) + (0.02 * 0.95 * 0.95 * 0.95 * 0.95) A weighting factor of 3-0.95 gives more weight to the most recent result than the second most triumphant arrival. R1's mass is three times that of R2, which is itself three times that of R3. This is because the importance of each return is determined by increasing its weight by its position in the list of returns, with the most current result being given the most importance. This may be shown by looking at an illustration with three returns, R1, R2, and R3. The weights for these returns, with a factor of 0.03-0.95, would be as follows: Weight for R1 = 0.95 Weight for R2 = 0.95 * 0.95 = 0.9025 Weight for R3 = 0.95 * 0.95 * 0.95 = 0.857375 Technical Description of the Models Used of section 1 An investment's beta indicates its level of volatility in comparison to the market as a whole. It is a key component of the capital asset pricing model (CAPM), which estimates a portfolio's anticipated return given its risk level. An investment with a beta of 1 is likely to be as unpredictable as the market; a beta larger than 1 suggests more unpredictability; and a beta less than 1 indicates lower volatility. The monthly returns were utilized to compile the total returns over a longer period of time for the two equities. Returns are determined by taking the stock price before the period and after the period and dividing that number by the stock price at the start of the period. If a stock's price was $100 at the start of a period of time and $110 at the conclusion of the interval, the return for that period of time would be ($110 -$100) / ($100) = $0.1 or 10%. After determining the returns of the two equities and the index, we ran a statistical method in a statistical data management platform to get the betas. The slope of a regression line depicting the link between the commodity's returns and the earnings of the market portfolio over a certain time period is the beta for that period. Next, we need to figure out how much money the stock and the index made. The formula we used was =((Cell2-Cell1)/Cell1)*100, where "Cell1" is the last data point from the previous period and "Cell2" is the first data point from the current period. When determining a stock's beta, it is necessary to do a regression analysis using the stock's return data in addition to the benchmark index's return data (Thrane 2019, p.60). The link between the stock's performances and the values of the broader market is shown by a linear regression, the value of which is the beta. The percentage earnings on the commodity and the indexes must be determined before doing the multiple regression. Percentage returns may be computed using the formula =((Cell2-Cell1)/Cell1)*100, where Cell1 is the data point from the prior period and Cell2 is the data point from the current period. If the price of a share of stock was $1114 at the start of 2019, and $1144.39 at the conclusion of the same time, the gross profit percentage would be ((1144.39-1114)/50)*100 = 4%. Similarly, if the price of the benchmark was $100 at the start of 2019 and $105 at the conclusion of the term, the index's return would be ((105-100)/100)*100 = 5%. After computing the percentage returns for the stock and the benchmark, we utilized a statistical spreadsheets tool to conduct a multiple regression and get the beta. The the slope of the regression line depicting the link between investor sentiment and index returns is the beta. center635Figure 1 Simply adding up all of the betas and dividing by the total number of betas yields the equally weighted average of the betas. If there are 10 different betas, for instance, we may divide the total by 10 to get an evenly weighted average. We used a weighting ratio of 0.95 to give a weight to each beta, multiplied each beta by its weight, and then added all the products to produce the exponential average value. The most current beta would have a weight of 0.95, the beta with the second-highest weight would have a weight of 0.95 * 0.95 = 0.9025, and so on (Thrane 2019, p.49). The weighted average is calculated by multiplying each beta by its assigned weight and summing the resulting results. Different betas range from 0.9 to 1.3, such as 1.2, 1.1, and 1.3. With a 0.95 weighting function, the relative importance of each beta would be determined by the following values: 0.95, 0.95 * 0.95 = 0.9025, 0.95 * 0.95 * 0.95 = 0.857375, and 0.81450625. This group's betas add out to (0.9 * 0.95) + (1.2 * 0.9025) + (1.1 * 0.857375) + (1.3 * 0.81450625), or an exponentially weighted average of (0.9 * 0.95) + (1.1 * 0.857375) + (1.3 * With a weighting factor of 0.95, the most current betas are given more consideration than their older counterparts. By multiplying the prior weight by the weighting function, we get weights of 0.95, 0.9025, 0.857375, and 0.81450625 for the betas. Simply multiply each beta by its weight and add the products to get the rolling average of the betas. To calculate the average beta using an exponential weighting scheme, we would add together (0.9 * 0.95) plus (1.2 * 0.9025) ...