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Abstract

This paper constructs a hedged portfolio with a long positon in S&P 500 index and a short position in FTSE 100 index. To calculate the time-varying hedge ratio, we use four methods, rolling window, EWMA, GARCH model and B-S model. Firstly, we explain the methods we used, including the assumptions, formulas and implications. Also, we implement the methods in the Excel to get the value of hedge ratios. Finally, we show the advantages and disadvantages of every method by comparing between every two methods. We evaluate the methods both in theory and practical application.

Introduction:

We want to hold a long position in S&P 500 index, at the same time we want to minimum-variance. As this reason, we introduce to hedge the long position in S&P 500 index with a short position in the FTSE 100 index. We suppose that the return on the hedged portfolio is: rp,t=rS&P-htrFTSE Then the variance of hedged profile will be: δF2=δS&P2+h2δFTSE2-2hδS&P,FTSE

If we want to minimum the variance of the hedged portfolio, we must derivate of h in this function and let the equal to zero. Where ht is the time-varying hedge ratio, given by:

ht=δS&P,FTSE,tδS&P2

Because we want to get the result only focus on the two-index, we ignore the currency flotation by assume that the currency rate is perfectly hedged.

At very beginning, risk manager have assumed that the volatility is constant over time, which allow us to estimate it use sample variance of past time: δ2=1T-1t=1T(rt-r)2 By estimate the historical data, our find the variance tend to be clustered, it is obvious to see all the index not only have high volatility during some period, but also have some low volatility at some other periods. So an approach to estimating time-varying volatility is to use the data from the recent past. This leading us introduce rolling window model: δ2=1M-1i=t-M+1t(ri-rt)2

We use the most recent days, says M, as the windows length to evaluate the new variance. This approach show longer periods more stable, but this approach leave wholly unanswered the choice of the rolling windows. Long precision of the estimate but could miss undying variation in volatility (Value at risk, chapter 9.2). As this reason, the limitations of this model is quit obvious, if we think the most recent past data are more valuable than the pervious data, why yesterday’s data have equal weight to the 100 days ago data? And dose the data just passed window length really mean nothing? For solve this limitation of RW (rolling windows) model, we introducing EWMA model: δt+12=λδt2+(1-λ)rt2 Which states that today’s variance is a weighted average of yesterday’s variance and yesterday’s squared return, with the determined by λ. This approach give us the biggest weight for the most recent data, and a smoothly decline for the past.

EWMA model is the simplified model from GARCH model. The conditional variance depends on the latest innovation but also on the previous conditional variance. We usually use the GARCH(1,1) model, the simplest model to forecast the volatility, σt+12 = а0+ а1σt2+ ß1rt2.

We decided to use the multivariate Rolling Window, EWMA and GARCH approaches, and to evaluate each model over a five-year period at first. Show the effectiveness of each model by compare the reduction of each hedge variance depend on RW, EWMA and GARCH model.

Method to using 1. Classic method

To obtain a control group, we computed an unconditional volatility. The constant variance and covariance are based on the first year simple return. The reduction, is (the unhedged variance – the hedged portfolio variance)/the unhedged variance, as a yard stick to measure the hedging.

Suppose we only use the data in the first year 2007 to compute the constant variance and covariance. We do not add new information set into our forecast. Then we used the variance and covariance to calculate the hedge ratio in the next 5 years. Variance of FTSE 100 | 0.000188 | Covariance (S&P 500-FTSE 100) | 0.000090 | Hedge ratio | 0.482044 | Unhedged variance | 0.000271 | Hedged variance | 0.000173 | Reduction | 36.01% |

2. Rolling window method

Rolling Window method is a very crude method, but one that is employed widely, is the use a rolling windows of fixed length for estimating volatility. (Value at risk, 3rd Ed, chapter 9.3) Assuming that we use data over T days, the volatility estimate is analysis from rolling windows: δ2=1T-1t=1T(rt-r)2 Rolling window model estimates the time-varying volatility based on returns from the recent historical data. It uses the most recent data from M days ago, M known as a window. We observe returns rt over M days, the volatility estimate is constructed from a moving average. Each day, the forecast is updated by adding information from the preceding day and dropping information from (M+1) days ago. The information set is updated daily, we can get the conditional volatility based on the information we already have. All weights on past returns in the window are equal to 1/(M-1).

Assumptions:

We can forecast the future volatility on the information set that investors have now, namely, the historical data can be used to predict the future.

A moving window of fixed length for estimating volatility, we assume the window length M is constant.

Observations in the window have the same fixed positive weight 1/M, and zero weight if they are outside the window. It means recent information receives the same importance as older observations in the window, and we reject the older observations outside the window.

The implementation in Excel * Historical data selection

We download six years of data for the S&P 500 index and FTSE 100 index from YAHOO! FINANCE. We evaluated each model over a five year period, so we need one more year data to get the information set for rolling window estimator.

The Standard & Poor's 500, is a stock market index based on the market capitalizations of 500 leading companies publicly traded in the U.S. stock market, as determined by Standard & Poor's. The index value is updated every 15 seconds during trading sessions, and starts at 09:30 and ends at 16:00. The FTSE 100 Index, is a share index of the 100 companies listed on the London Stock Exchange with the highest market capitalization. The index is maintained by the FTSE Group, an independent company which originated as a joint venture between the Financial Times and the London Stock Exchange. It is calculated in real time and published every 15 seconds. Continuous trading on the London Stock Exchange starts at 08:00 and ends at 16:30 (when the closing auction starts), and closing values are taken at 16:35.

As a result of the difference of trading sessions between U.S. stock market and London Stock Exchange, the transaction date is not exactly the same with each other. We need to delete the dates that are not on the same trading day. Otherwise, it will have data error on the covariance. We used the ‘VLOOKUP’ function to screen the data are not on the same trading day for the two index, then deleted them. To obtain a series of correct data, we have to abandon the noisy data.

* Rolling window estimator

We chose 1 year as the window length. Actually, the first year 2007 only have 249 simple returns, M = 249. We compute the time-varying variance and covariance from 04/24/2008 to 04/19/2013, everyone chose the preceding 249 days’ simple returns as the information set. Unhedged variance | 0.000271 | Hedged variance | 0.000165 | Reduction | 39.06% |

* The optimal rolling window estimator

In the last step, we assumed a window length. But this is unlikely to be optimal, we decided to estimate an optimal window length to compute hedge ratio. We already computed the result when M=249, then we used the “table” in “what if analysis” to list the reduction on M= 2,3,4…249. Then the match function told us the optimal window length. Maximum reduction | 39.26% | Optimal window length | 124 |

3. EWMA model

“Formally, the forecast for time t is a weighted average of the previous forecast, using weight λ, and of the latest squared innovation, using weight(1-λ), that is,” (Value at risk, 3rd Ed, chapter 9.2.4). Today’s variance is a weighted average of yesterday’s variance and yesterday’s squared return, with the weight determined by λ. δt+12=λδt2+(1-λ)rt2 We call the λas decay factor, and it must less than 1. If we recursively replace δt2

into the equation, we can get δt+12 = (1-λ)i=0∞λirt-i2.

It places less and less weight on the observations as they move further into the past, by using the decay factor. The larger the value of decay factor, the more weight is placed on past observations and so the smoother the series becomes.

EWMA model is from GARCH model, the GARCH(1,1) process is σt+12 = а0+ а1σt2+ ß1rt2 when а0=0 and ß1 = 1-а1, the GARCH(1, 1) model reduces to the EWMA model.

In the application of finance, we also need to estimate covariance or correlation. Multivariate EWMA model can be applied to the covariance between two series of returns, σij,t = λσij,t + (1-λ)ri,trj,t.

Assumptions:

The mean return is equal to zero.

Decay factor λ is a constant parameter, and less than unity.

The recent information is more important than the past information. EWMA weight observations in the sample in a such way that their importance declines smoothly into the past.

The implementation in Excel * Historical data selection

We chose the simple return data the same as rolling window, the first year 2007 was used to initialize the EWMA estimator. We assume the variance and covariance on 04/24/2007 are zero. * EWMA(0.94)

The decay factor is assumed as 0.94. We calculate the variance and covariance from 04/25/2007 to 04/19/2013, then we got hedge ratio from 04/24/2008 to 04/19/2013.

Unhedged variance | 0.000271 | Hedged variance | 0.000170 | Reduction | 37.15% | * EWMA(optimal)

Decay factor λ= 0.94 is unlikely to be optimal, we decided to test a third model, which is multivariate EWMA model with a decay factor that is estimated. The optimal decay factor was obtained by solver. Solver changed the value of decay factor to get the maximum reduction.

Unhedged variance | 0.000271 | Hedged variance | 0.000165 | Optimal λ | 0.995177 | Reduction | 39.11% |

4. GARCH model

A more sophisticated conditional volatility model is known as GARCH. “Volatility estimation has moved toward models that put more weight on recent information. The first such model was generalized autoregressive conditional heteroskedastic (GARCH) model. The GARCH model assumes that the variance of returns follows a predictable process. The conditional variance depends on the latest innovation but also on the previous conditional variance.”(Value at risk, 3rd Ed, chapter 9.2.2). The simplest such model is the GARCH(1,1) process, that is rt=c+ εt σt+12 = а0+ а1σt2+ ß1εt2 where rt is the daily return and σt denotes the conditional variance of εt. In the ‘plain vanilla’ GARCH model the conditional variance is assumed to be normal distribution with mean zero.

So the GARCH(1,1) process is, σt+12 = а0+ а1σt2+ ß1rt2. Parameters а0, а1and ß1 are estimated by maximum likelihood estimation. We assume that returns rt are drawn from a normal distribution with zero mean. The probability density of rt is f(rt,σt2) = 12πσt2exp(-rt22σt2).

The sum of the log-likelihood function is t=1Tln L(а0, а1, ß1; rt)= t=1T(-lnπ2-12lnσt2-12rt22σt2).

We can get the estimators а0, а1and ß1 by setting the sum maximum.

As for the covariance between S&P 500 and FTSE 100, we used the Harris, Stoja and Tucker(2007) approach to estimate the implied covariance. “Consider two assets, i and j, whose per-period abnormal returns are given by εit= rit- υit εjt= rjt- υjt where rit and rjtare actual returns and υit and υjt are conditional mean returns. Given the time t −1 information set, an estimate of the conditional covariance matrix requires estimates of the conditional variances σit2 and σjt2, and an estimate of the conditional covariance. σij,t. The multivariate GARCH model that we propose involves firstly estimating the conditional variances, σit2 and σjt2, using a univariate GARCH model. We then construct the new series ε+,t= εit+εit ε-,t= εit-εit and use a univariate GARCH model to estimate σ+,t2 and σ-,t2. An estimate of the conditional covariance σij,t can then be obtained using the following identities.” (A Simplified Approach to Modeling the Comovement of Asset Returns, 2004) σ+,t2= σit2 + σjt2 + 2σij,t σ-,t2= σit2 + σjt2 - 2σij,t

In particular, combining the two functions, we have σij,t= 14(σ+,t2- σ-,t2)

Assumptions

The mean return is zero.

Returns rt are drawn from a normal distribution with zero mean: rt ~ N(0, σt2.

The implementation in Excel

To implement GARCH model in the Excel, we proceed as follows: * Historical data selection

We chose the simple return data the same as EWMA, the first year 2007 was used to initialize the EWMA estimator. We chose the initial value for the conditional variance σ02 = 0 on 04/24/2007. With the passing of one year, variance tends to be the true value. * Parameter estimation

Arbitrarily chose starting values for the parameters, а0=0.5, а1=0.3and ß1=0.4., and constructed the conditional volatility series σt2=а0+ а1σt-12+ ß1rt-12. Then calculated the log-likelihood for each return rt, and used Solver function to maxing the sum t=1Tln L(а0, а1, ß1; rt)= t=1T(-lnπ2-12lnσt2-12rt22σt2) by changing the parameters а0, а1and ß1. We got the estimators of pararmeters.

Then we constructed two new series equal to the sum and difference of S&P 500 and FTSE 100 returns, and used the same method above to estimate the two sets parameters.

| FTSE 100 | The sum of S&P 500 and FTSE 100 | The difference of S&P 500 and FTSE 100 | а0 | 0.00000179 | 0.00000838 | 0.00000211 | а1 | 0.90939119 | 0.89603092 | 0.80962323 | ß1 | 0.81728052 | 0.09095506 | 0.19614655 |

* The calculation of time-varying variance and covariance

We applied the estimators into the formula σt2=а0+ а1σt-12+ ß1rt-12, to get σFTSE 100,t2, σ+,t2 and σ-,t2. Then computed covariance between S&P 500 and FTSE 100, σij,t= 14(σ+,t2- σ-,t2).

Hedged variance | 0.00017522 | Reduction | 35.34% |

5. Option method

An alternative way to estimates variance of those index from analysis the option price. As we already know the index option price from today, option method is infer from the option price, and the implied volatility is determined by the option price. Because of rest of parameter we are already know. And the formulas of B-S model is:

C=S*Nd1-X*exp-r*T*N(d2) d1= lnSX+r+σ22*TσT d2= d1- σ*T

Assumptions

The log normality of stock return

Constant risk free interest rate

No dividend payment (even the FTSE 100 index do not have any dividend)

The implementation in Excel * Historical data selection

All the data are coming from the yahoo finance and NYSE EURONEXT in 24/04/2013. Consider about the exchange market at US, we choose the US Treasuries as the risk free rate.

FTSE 100 index | Strike Price | Risk free rate | Maturity time (year) | Option price | 6411.83 | 6400 | 3% | 0.4 | 132 |

* Calculation of the implied volatility of FTSE 100

Set to an initial guess of the unknown δ and using the guess sigma to valued a computed call option price by using the B-S model formulas. Then write down the actual call option price and calculated the different of those two call option price, actual C and computed C. At last, set the different between actual C and computed C to zero using Solver by changing sigma. Then we get the implied volatility of the underlying asset is equal to 0.2503% per annum. Which means 0.01597% per day. (δm2=mδ12) The parameter m is the period and δm2and δ12 are the volatility during m days and single day, separately.

* Calculation of the covariance between FTSE 100 and S&P 500

Using the single day volatility of FTSE 100 from option method and the covariance between FTSE 100 index and S&P 500 index from other method (GARCH, Rolling Windows and EMWA) to copulated the hedge ratio and the reduction of volatility of each hedged portfolio, since 2013/1/24. (Because we using a four months call option to evaluated the volatility.)

| Option (COV-GARCH) | Option (Cov-RW) | Option (Cov-EWMA) | GARCH | RW | EWMA | Unhedged Volatility | 5.29E-05 | 5.29E-05 | 5.29E-05 | 5.29E-05 | 5.29E-05 | 5.29E-05 | Hedged Volatility | 4.44E-05 | 4.14E-05 | 4.64E-05 | 4.26E-05 | 4.09E-05 | 4.21E-05 | %Reeducation | 16% | 21.06% | 12% | 19% | 23% | 20% |

It is obvious to see by using different covariance basic on different methods, the option method have different performance. But the option method at least close and some time batter than other methods.

Results and Analysis 1. Classic method VS rolling window

The unconditional volatility only used the first year data to compute the constant volatility. It has serious drawbacks. It ignores the new information totally, and applied the constant volatility to the next 5 years. If we add the new observations, the weights of the data will be smaller and smaller. Weight 1/M decline as the growth of the denominator As time goes on, the new information plays insignificant role in the estimation, for the weight is too small.

Rolling window solve the problem. It specify the scope of the data, window length M, ensure the weight is constant after the after the mew observations join in. The forecast is updated by adding information from the preceding day and dropping information from (M+1) days ago. The results below shows rolling window model has a 3.05% improvement than classic method. | Classic method | Rolling window | Hedged variance | 0.000173 | 0.000165 | Reduction | 36.01% | 39.06% | 2. Rolling window VS EWMA

Limitations of rolling window * Ghost features

Due to the large one day return, large increase in volatility the following day. If the influential observation leaves the sample, a large reduction in volatility occurred. This is the ‘ghost features’ in the estimation of volatility.

We can reject the ‘ghost features’ data on purpose. Using the excel functions to screen the absolute value of simple returns greater than 4% and deleted them. We can get the result below, the reduction decreased. It is useless to remove the ghost data. Because we cannot distinguish between the random unusual data and the data depend on market issue. It would make the result worse if we delete the date reflect the real market situation.

| Rolling window(1 year) | Rolling window(removing the ghost data) | Unhedged variance | 0.000271 | 0.000213 | Hedged variance | 0.000165 | 0.000142 | Reduction | 39.06% | 33.39% |

* The weights on the past observations

The rolling window estimator gives past observations either full weight( if they are recent enough to lie in the window) or zero weight( once they become so old that they fall out of the sample). Do the observations in the window have the same importance? Are the observations outside the window useless? Obviously not, the simple selection of data limits the effectiveness of rolling window.

If we increase the window length, the precision with the return variance will be increased. It will reduce the weight 1/(M-1) on the observations, and decrease the sensitivity of rolling variance estimator to the market. This is the built-in problem in the model. So we need a new method, GARCH model.

EWMA model is the simplification of GARCH model, and the advantages are essentially the same over rolling window. Later we will analyses the difference between them.

To correct the ‘ghost features’ problem of equal weights of rolling window model, volatility estimation has moved toward models that put more weight on recent information. EWMA model responds more quickly to the market change. It weight observations in the sample in a such way that their importance declines smoothly into the past. According to δ_(t+1)^2 = (1-λ)∑_(i=0)^∞▒〖λ^i r_(t-i)^2 〗., the weight of simple return of day t is (1-λ), day t-1 is (1-λ)λ, day t-2 is (1-λ)λ^2. We can see the weight from recent to past declines in geometric sequence. Compared with rolling window, EWMA not only solve the problem of weight is 1/(M-1) or zero, but also promise the sensitivity of the recent information.

EWMA do not suffer from “ghost feature”. The ghost date can be declined in a smooth path. The ghost date in rolling window receives weight 1/(M-1) or zero, which may cause the abnormal fluctuations of data. Obviously, EWMA can solve the problem by giving the observations the exponentially weights. | Rolling window (1 year) | Rolling window (optimal) | EWMA (0.94) | EWMA (optimal) | Hedged variance | 0.000165 | 0.000165 | 0.000170 | 0.000165 | Reduction | 39.06% | 39.11% | 37.15% | 39.11% |

In theory, EWMA progress rolling window. However, the reduction of rolling window is larger or equal than EWMA. We suppose there is calculation error in the process. The Excel may can not handle such a large amount of data. Obviously, EWMA is a more accurate model than rolling window.

In principal, of course the EWMA give us a more reasonable assumption. But in the real world market, it have a large number of fluctuation from nowhere, and have less effective to the future, we call this random variable. For example, rumour will affect the market at some time, but when the lies were exposed, the effect of it will disappear very quickly. But our model cannot distinguish which is the rumour and which information will have long-term effect. EWMA will accumulate this huge number of very small but have amazing amount size random variable than RW model. But in more efficiency market, the investors have ability to distinguish some information, have rumour will less effective. And the EWMA will have a better performance in these kinds of market.

3. GARCH VS EWMA

We know the GARCH(1,1) model is σt+12 = а0+ а1σt2+ ß1rt2. The volatility of next day is related with volatility and return today. To simplify the model, we assume а0=0 and а1+ ß1=1, is EWMA model.

In principal, GARCH nests the EWMA model, it should unequivocally offer an improvement over the EWMA model. “But the true conditional variance is not integrated introduces estimation error that is detrimental to its performance.”(Value at risk, 3rd Ed, chapter 9.2.4) EWMA doing well than GARCH is the real world sometime, and easier to using, so EWMA are much popular than GARCH. “The longer-period forecasts, however, are markedly different because the EWMA process does not revert to the mean.” (Value at risk, 3rd Ed, chapter 9.2.4) But we only need to do is to forcast the volatility of the next day.

| EWMA(optimal) | GARCH | Hedged variance | 0.000165 | 0.00017522 | Reduction | 39.11% | 35.34% | The GARCH model our using is a simplify model. In the real world our using this model, we can analysis the parameters day by day to get the dynamic GARCH model, which will report different decay factor bias on each dataset impact. But as the limitation of our knowledge, our team do not know how to use excel analysis the dynamically model automatically. But even the dynamic model can realization, as a coin have to side, such many parameters tend this model to be computationally burdensome (A Simplified Approach to Modeling the Comovement of Asset Returns, page 2). And as the true conditional variance is not integrated in the real world, even it is close to being integrated, such many computationally method introduces estimation error that will decline the accurate of GRACH model. That is the reason why the EWMA model has a better performance than the GRACH model sometime.

But even the model as complicate as GRACH model, still have some defective should be improved. More specifically, depend on analysis a widely range of historical data from all around the world, we find the expected stock returns will decline, volatility tends to increase; when the good news, that the expected stock returns will rise, volatility tends to decrease. GARCH model cannot explain this asymmetric phenomenon, because this model using rt2 as the residual factor which we lend a no different reflection of positive or negative return. So Nelson (1991) introduce E-GARCH model: lnht2=α0+i=1pαiϵt-1ht-1+i=1priϵt-1ht-1+i=1qβlnht-i2 In this model, ε introduce by two different ways, the observed value and absolutely value. This method help us analysis the asymmetric phenomenon. 4. Option method VS other methods

To analysis the option method, what we actually do is using the B-S method and the formula, through we already know the price of call option the investor give to us calculated what is the variance him think about this index, or we can say it is a forecast of the professional investor.

And compare the different between option methods; GARCH, RW and EWMA we can figure out all the method can decline the risk of the portfolio very effectively. We should make decision to use different method in different situation. But the option gives us a professional advisor and we should not ignore it. We should better to use option method to be a reference value or a testing value.

Conclusion

To determine the optimal hedge ratios based on the minimum risk framework. We used rolling window method, EWMA method, GRACH model and implied volatility method to estimate the minimum-variance hedge ratio for S&P 500 index portfolio, hedged using FTSE 100 index. We evaluated the performance of each model economically, by considering the variance of the hedged portfolio. We found that the four methods all reduced the portfolio variance effectively.

The comparison between methods is as follows:

| Rolling window (1 year) | EWMA(0.94) | Reduction | 39.06% | 37.15% |

Rolling window has a better performance than EWMA both under a certain value of parameter and the optimal parameter. It maybe not accord with theory. We think this may be caused by data deviation and contingency. In principal, EWMA is a more mature theory.

| Rolling window(optimal) | EWMA(optimal) | GARCH | Reduction | 39.26% | 39.11% | 35.34% |

We can see from the result above, GARCH model suffer from a number of problems in practice owing to the complexity of their specification. Due to the large number of parameters that must be estimated, it is too complicated for excel to compute. We can not guarantee that maximum likelihood estimation will converge to the correct parameter values. As to implied volatility, it base on the totally different theoretical basis with the three methods. The three methods make predictions on the historical data, while implied volatility depends on the option price that has the function of “discovery of volatility”. It contains the option writers’ expectation of volatility, so we only treat the implied volatility as a reference.

Reference:

Jorion, P., 2000. Value at Risk. 3rd ed. New York: McGraw Hill. Ch.9.

Alexander, C., and Leigh, C.,1997. On the covariance matrices used in Value at Risk. Journal of Derivatives, 4:3(1997), 50-62.

Harris, R.D.F., Stoja, E. and Tucker, J., 2007. A simplified approach to modeling the comovement of asset returns, Journal of Futures Markets. [online] Available at: <http://onlinelibrary.wiley.com/doi/10.1002/fut.20262/pdf> [Accessed 19 April 2013]

Harris, R.D.F., 2013, Volatility forecasting, ECONM2029_2012_13. Bristol University, unpublished.

Benninga, S., 2008. Financial Modeling. 3rd ed. Cambridge: The MIT Press. Ch.19. http://finance.yahoo.com/ https://globalderivatives.nyx.com/

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...Teams Alexandria Aguirre Dr. Nate CotePrinciples of Supervision 1 (D50)Dona Ana Community College | What are teams? Why are they important? When you think about teamwork, you may recognize effective, productive teams. You may also recognize groups of individuals who have been put together to work on a task who just don't seem to make the same progress. Your answer as to what a team is may be working together with other people to figure out a problem; and you are exactly correct. Teams are better in some situations, but not necessary needed in all. In fact, they may have some disadvantages that are inappropriate for the work place. Teams typically outperform individuals when the tasks being done require multiple skills, judgment, and experience, but when the individual isn’t a team player, teams are just about as good as the individual himself. (Robbins and DeCenzo 275-84) Many times, teams are often confused with groups. Teams and groups are similar, but not completely. What differentiates both is that in a team, the members are committed to a common purpose, have a set of specific performance goals, and hold themselves equally responsible for the team’s results. A group is individuals working interdependent who come together to reach a particular objective. There are four types of teams that carry different level of effectiveness. They are: a working group, a pseudo team, a potential team, and a real team. In a working group, there is no work or opportunity to......

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...Managing Conflict in the Workplace Professional Development, MBA 525 Managing Conflict in the Workplace Introduction Conflict is something we all have experienced or will experience sometime in our lives; one could say conflict is inevitable. Conflict can sometimes get out of hand and can cause havoc in a person’s work life and family life. Conflict is perceived incompatible differences that result in interference or opposition (Robbins, Coutler, 2011). Whether the differences are real or not is irrelevant (Robbins, Coutler, 2011). If people in a group perceive that differences exist, then there is conflict (Robbins, Coutler, 2011). Because of the environment we live in, the strategic alignment of organization’s expanding their workforce globally, the strategic business goals alignment of workplace diversity initiatives, and companies expanding more into work teams and workgroups; conflict in the workplace has become inevitable. There will always be differences in opinions among work groups; however; effectively managing conflict is the key to balancing conflict resolution in the workplace. Recognizing Conflict Being able to recognize the causes of conflict is an important part of preventing conflict (Pace, 2006). When conflict can be recognized a solution can be created to limit conflict in the workplace. There will be varies opinions in the workplace and work teams; however, when......

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...Listing of alternative courses of action that could be taken & Evaluation of alternative courses of action. 1. Team Work * Sniff and Scurry display team work in their quest to find cheese. They work together. Sniff, “sniffed ahead” and Scurry, “scurried ahead” looking for cheese. Their effective team work allowed them to stay on the cheese. In order to work effectively together the two of them had to communicate well with each other. Hem and Haw did not work together. 2. Listening * Sniff and Scurry demonstrated how important it is to listen to each other. By listening to each other they were able to navigate the maze and find cheese. Hem demonstrated how not to listen. Hem would not listen to Haw when Haw was trying to get him to move forward. 3. Motivate * Haw used motivational quotes to communicate with not only others but himself. His quotes kept him focused on changing with change. His quotes were also left for Hem, in case he decided to join the journey. 4. Imagination and Creativity * Spencer Johnson, the author used his imagination in creating these characters, the maze, and the cheese. His ability to use his imagination and creativity was an effective communication tool. If the book had been about four ordinary people, the book would not have the same effect. 5. Accept Change * A person should realize if they are not changing and looking to get better they will get comfortable and be left behind the times....

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...Team Building First let’s define the word team. Team is a group of people with a common, collective goal. A team is not based on one person. Like the saying “There is no 'I' in TEAM”. Forming successful teams can become a challenge. It’s a challenge because you have to get all the right people in one group. A bad apple can spoil the whole group. To have a successful team, a leader must have background knowledge of everyone on the team so that he will be able to help each one in a respectable manner, and it will help also later if a problem might occur among them. Team Building plays an important role in the workplace. Some team building experiences are successful and some are unsuccessful. Team building incorporates team work. Team work is when employees put aside their personal goals and preferences and work together cooperatively to achieve the team’s goal. Team Building also forms friendships and trust between employees. When the team is on the same page, everything runs smoothly. The steps a team leader takes to select workable teammates for a project can be challenging and frustrating task. Leaders should follow the four steps to create an effective team building: 1. Assess, 2. Plan, 3. Execute, 4. Evaluate. Step 1: Assess the teams developmental needs base on strengths and weaknesses by creating a lists with different categories. Step 2: Plan team-building activities based on the needs identified; referring to the entire no’s the team had. Step 3: Execute the......

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...Ethics Case: "A Good Team Player" 1) Describe the factual situation Steven, a staff accountant in the accounts payable section, is confident that he knows the “ins” and “outs” of the bureaucratic organization he works in. Kristin, a new manager of accounts payable, no non-sense type of manager, Kristin was experienced and determined to perform her new assignment with the same vigor that had brought her so much success throughout her career. Steven believes people seem to gain promotions and have the opportunity to work overtime based on who likes them rather than the quality of their work. As a result, Steven who is dissatisfied with what he senses are political machinations that have influenced managerial decision making within his firm, suggests to Kristin that things would be better if the political could be stopped. Kristin uses the power of her new position to get Steven to give her the names of the bad team players or else she will start to think he is part of the problem. Steven, stunned, cannot think of a way to respond. 2) Identify the possible courses of action. There are three possible courses of action that I can take away from this situation. One situation involves Steven and the company, and one that involves Steven only. 1. Steven would be to respond Kristin's demands and give her the names of the bad team players. By doing this, it would benefit himself in the long run and will allow the company to better their work force. 2. ......

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...To succeed in college it’s important that students possess an attitude of personal responsibility. Unlike high school, in college no one tells a student when to go to class, when assignments are due or when to study for test. In college, students are expected to know these things on their own. Although personal responsibility might cause one to neglect teamwork, personal responsibility is needed to succeed in school because it allows a student to take charge over their own life and cultivates growth in all areas of their life Before one can accept such an attitude they must first understand what personal responsibility means. Personal responsibility means taking individual accountability for one’s own actions. In other words, it’s being able to take care of one’s own wellbeing without expecting or blaming others to do it for you. In school, it means turning in your work on time, studying for you tests and working effectively with your classmates. It also means you can’t blame your parents, kids, and work for not being able to do well in school. Some might argue that too much reliance on the self can cause you to neglect team work and important relationships. Some may want to help you in reaching your objectives especially group projects. When you have an attitude of I can do it by myself you leave out benefits of working with others who has different skills, knowledge, and ideas. You could avoid this by realizing your limitations and reaching out for help after you do......

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...How might these factors, diversity, attitude, learning and work styles, and ethical perspective be used to resolve conflicts? People tend to work in different ways according to their life’s schedule. Some like to wait until the last minute because they work better under pressure, and the team members must respect that. Others like to take their time and have an assignment ready ahead of time. Both of these are perfectly fine. What we must consider are deadlines. To avoid conflict, we have to understand that there are due dates and we have to respect one another by completing our individual parts in order to achieve the grades we all want. Attitude is one of the most important factors in teamwork. We all need to listen and try to understand each other. We must analyze everyone’s work and give good criticism instead of just rejecting someone’s work. Diversity could very much be a benefit to the team. We all should accept others for who they are and what they contribute, without judging the color of their skin or beliefs. We all come from different backgrounds and therefor have different ideas that could lead us to new and creative ideas that can push the team forward in whatever the task may be. Everyone has different methods of learning. Some faster than others, this could definitely affect team building. We all have to be patient and help one another so we can all be on the same page to accomplish the same...

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...I. TEAM AGREEMENT: A. Introduction/Scope/Objective Our team is a network of consultants geared toward analyzing corporate behavior and providing recommendations for improving corporate social responsibility efforts. B. Mission Statement: Universal Responsibility Solutions will be the leader in providing social responsibility solutions to organizations around the world. C. Vision Statement: Universal Responsibility Solutions seeks to be the social responsibility stewards for today and tomorrow and the future. D. Shared Values: Fairness: It is important to be impartial and to treat everyone equally. Reliability: Since this is a group project, we’re relying on one another to perform all tasks and duties assigned. It is important that each member performs to the best of his/her abilities and remains accountable for his/her actions. Diversity: Instill a more racial and gender diverse workplace environment. We believe that diversity helps us embrace a broader approach to issues and solutions. Ethics and integrity: Each member is doing quality work to the best of his/her ability. E. Desirable team behaviors and consequences for non-compliance: Respect: Show others respect in discussions by having an open mind about different positions. Communication between group members should always be courteous. We can respectfully disagree. Activity Documentation: Document progress (decisions and processes) Accountability: Tasks assigned should be completed on time....

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...In case scenario number one, the psychiatric nurse was being aggressive. Her choice of words and communication style were inappropriate, unprofessional, and rude. This situation required intervention from management including counseling with psychiatric nurse on respectful and tactful communication. The staff RN clearly does not value the input of the CNA and has no respect for his position or duties. Without teamwork and collaboration of all staff, there will be a breakdown in communication resulting in poor patient care. Rashad was trying to clarify his role and responsibilities through passive communication. Due to the aggressive response made by another team member, Rashad likely felt unappreciated and angered by the lack of respect and condescending commentary from his team member. Rashad should discuss this with management and express his concerns and feelings regarding the inconsideration and demeaning behavior from staff RN. Rashad’s after thoughts of sabotage and insubordination are passive aggressive. This type of communication is indirect and ineffective in problem solving. Aggressive communication can create a hostile work environment, untrusting relationships, and low morale. This type of communication creates a vicious cycle of arrogant dictatorship and indirect antagonism. When there is lack of collaboration and listening, there is little chance of a productive working relationship (Hansten & Jackson, 2009). In case scenario two, Pamela was being......

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...UGANDA CHRISTIAN UNIVERSITY Faculty of business and administration MUKONO DISTRICT LOCAL GOVERNMENT UGANDA WANKULUKUKU ROAD BY KEMIHINGIRO BONITA REG NO: S1324/1003 Internship report submitted to The faculty of business and administration in partial fulfillment for the Award of a bachelor’s degree in project planning and entrepreneurship April 2015 Agency supervisor NAME…………………………….. SIGNATURE…………………….. DECLARATION I kemihingiro Bonita declare that this internship report is my original work and has never been submitted to any institution for any award of a Bachelors’ Degree in Project planning and entrepreneurship Signed …………………………. Date ………………………… APPROVAL This is to certify that this internship report by kemihingiro Bonita has been carried out at mukono district local government He has been under my supervision and the internship report is now ready for submission to the faculty of Business studies at Uganda Christian University. Name ……………………………… Signature …………………………… Date……………………………. DEDICATION I dedicate this piece of work to my grandfathers, Brothers, Sisters and Friends for the continued support and encouragement both financially and academically throughout my course of study. ACKNOWLEDGEMENT Like any other report, is not well presented by the name of a single author, rather its contents reflect the contribution from various sources without anyone of which the report would certainly have been......

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...For many years now many companies and even schools have used teams or groups to balance out each others weaknesses and strengths. Returning to the workplace, it is estimated that between 70 and 82 percent of U.S. companies use the team concept, making teamwork skills one of the most commonly required skills in the work environment. There are many benefits to working in teams as most corporations are adapting to this method. Team work is even used in professional sports being a successful baseball pitcher can’t be accomplished without the help of a catcher or even an outfield team to help catch fly balls. A winning team is a team effort and cannot be accomplished by just one person. In a declining economy as we are facing now, times are tougher and harder than they have ever been and for many corporations the benefits of team work and there members participating roles are more crucial now then ever. Team work is a group or a team working together towards a common goal. The essence of teamwork is to create a product through a collective effort that exceeds the quality of any individual endeavor or the collective efforts of several individuals. Each team member has and plays an important role in working together as a team, such as setting goals, and what is expected of each team member to reach those goals, clarifying each members role, communication with each other by phone, email, and or at weekly meetings. One important role to any team is the team leader. Having a......

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