Consider the numbered lines below:1)def price(PMT, IY, g) 2) return PMT / (IY – g) 3) 4)g = np.linspace(0, 0.09, 21) 5)IY = 0.1 6)PMT = 10 7)PV = np.zeros_like(g) 8)for i in range(len(g)): 9) PV[i] = price(PMT, IY, g[i]) 10)plt.plot(g, PV) 11)plt.xlabel(‘Growth Rate’) 12)plt.ylabel(‘Perpetuity with Growth Price’);If we execute the code above, we receive an error. In which line lies the error?

The term used to describe the volume of air exchanged during normal inspiration and expiration is:

Assume we download the stock price of Tesla and compute its return using the command  startdate = ‘2019-01-01’ enddate = ‘2021-01-01’ tesla = web.get_data_yahoo(“TSLA”, startdate, enddate)R_tesla = tesla[‘Adj Close’].pct_change().dropna() Which of the following commands is not a valid command in Python?  

Adam and SGD are examples of

Consider the following code with numbered lines: 1)betai = np.array([-2, -1.5, -1, -0.5, 0., 0.5, 1, 1.5, 2]) # Features 2)ERi = np.array([-0.08, -0.06, -0.03, -0.01, 0.02, 0.04, 0.07, 0.1 , 0.12]) # Labels 3)hidden = tf.keras.layers.Dense(units=1, input_shape=[1]) 4)model = tf.keras.Sequential([hidden]) 5)loss = ‘mse’ 6)optimizer = ‘Adam’ 7)model.compile(loss=loss, optimizer=optimizer) 8)history = model.fit(betai, ERi, epochs=10000, verbose=False) 9)plt.plot(history.history[‘loss’]) 10)plt.xlabel(‘Number of Epochs’) 11)plt.ylabel(‘Loss’);In which of line does the training of the neural network takes place?

Sentence nine is a/n

Suppose we consider a one-step binomial tree model to price a derivative that pays in the up state and in the down state.  What is the price of this derivative?

Consider the pseudo code below to obtain the effient portfolios:from scipy.optimize import minimize f = lambda w: TO BE FILLED mu = np.linspace(15, 30, 31) sd_optimal = np.zeros_like(mu) w_optimal = np.zeros([31, 5]) for i in range(len(mu)): # Optimization Constraints cons = ({‘type’:’eq’, ‘fun’: lambda w: np.sum(w) – 1}, {‘type’:’eq’, ‘fun’: lambda w: w @ ER * 252 * 100 – mu[i]}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal[i, :] = result.x sd_optimal[i] = np.sqrt(result.fun)Assuming that ER are Cov given, what should we substitute TO BE FILLED for in order to get the desired result?

Consider the same pseudo code from the previous question to compute the efficient portfolios:from scipy.optimize import minimize f = lambda w: TO BE FILLED mu = np.linspace(15, 30, 31) sd_optimal = np.zeros_like(mu) w_optimal = np.zeros([31, 5]) for i in range(len(mu)): # Optimization Constraints cons = ({‘type’:’eq’, ‘fun’: lambda w: np.sum(w) – 1}, {‘type’:’eq’, ‘fun’: lambda w: w @ ER * 252 * 100 – mu[i]}) result = minimize(f, np.zeros(5), constraints=cons) w_optimal[i, :] = result.x sd_optimal[i] = np.sqrt(result.fun)For any given iteration i, what is the shape of the array w_optimal[i, :]?

The law of diminishing marginal utility helps to explain why supply curves are generally upward sloping.