An Introduction To Analysis Of Financial Data With R 1st Edition Ruey S Tsay - Solutions

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Consider the two bond yield series of the previous exercise. What is the relationship between the two series? To answer this question, take the log transformation of the data to build a time series model for the Aaa yields using Baa yields as an explanatory variable. Write down the fitted model,
Consider the daily simple returns of American Express (AXP), CRSP value weighted index (VW), CRSP equal-weighted index (EW), and the S\&P composite index (SP) from September 01, 2001 to September 30, 2011. Returns of indices include dividends. The data are in the file $d-a x p 3 d x-0111$. txt
Answer the same questions as Problem 1 but using monthly returns for General Electric (GE), CRSP value-weighted index (VW), CRSP equal-weighted index (EW), and S\&P composite index from January 1940 to September 2011. The returns include dividend distributions. Data file is \(\mathrm{m}-\mathrm{ge}
Consider the monthly stock returns of S\&P composite index from January 1940 to September 2011 in Problem 2. Perform the following tests and draw conclusions using the $5 \%$ significance level.(a) Test $H_{0}: \mu=0$ versus $H_{a}: \mu eq 0$, where $\mu$ denotes the mean return.(b) Test
Consider the daily log returns of American Express stock from September 1, 2001 to September 30, 2011 as in Problem 1. Use the 5\% significance level to perform the following tests: (i) Test the null hypothesis that the skewness measure of the returns is zero and (ii) test the null hypothesis that
Daily foreign exchange rates (spot rates) can be obtained from the Federal Reserve Bank in Chicago. The data are the noon buying rates in New York City certified by the Federal Reserve Bank of New York. Consider the exchange rates between the U.S. dollar and the British pound and Japanese yen from
Consider the monthly US unemployment rate from January 1948 to November 2011 in the file m-unrate-4811. txt. The data are seasonally adjusted and obtained from the Federal Reserve Bank at St Louis.(a) Does the monthly unemployment rate have a unit root? Why?(b) Build a time series model for the
Consider the monthly simple returns of CRSP Decile 1, 2, 5, 9, and 10 portfolios based on the market capitalization of NYSE/AMEX/NASDAQ. The data span is from January 1961 to September 2011.(a) For the return series of Decile 2 and Decile 10, test the null hypothesis that the first 12 lags of
Consider the daily range (daily high-daily low) of Apple stock from January 2, 2007 to December 23, 2011. One can obtain the data by the package quantmod from Yahoo. Compute the first 100 lags of ACF of the series. Is there evidence of long-range dependence? Why? If the range series has long
Consider the monthly yields of Moody's Aaa \& Baa seasoned bonds from January 1919 to November, 2011. The data are obtained from FRED of Federal Reserve Bank of St. Louis. Consider the log series of monthly Aaa bond yields. Build a time series model for the series, including model checking.
Consider again the monthly log series of Moody's Aaa bound yield. Use the exponential smoothing method to produce 1- to 12-step ahead out-of-sample forecasts at the forecast origin November 2010.
Consider the quarterly earnings per share of the Johnson & Johnson from the first quarter of 1992 to the second quarter of 2011. The data are in the file q-jnj-earns-9211.txt and are obtained from the First Call Historical Database of Thomson Reuters. Take log transformation of the data if
Consider the US quarterly real gross national product from the first quarter of 1947 to the third quarter of 2011. The data are in the file q-GNPC96.txt, seasonally adjusted, and in billions of chained 2005 dollars. Let $x_{t}$ be the growth rate series of the real GDP.(a) The ar command
Consider the monthly unemployment rates of the State of California and United States from January 1976 to September 2011. The data are in the file m-CAUS7611 . txt (year, mon, CA, US).(a) Build a pure time series model for the monthly unemployment rates of California. Perform model checking and
Consider the US monthly 30-year conventional mortgage rates from April 1971 to November 2011. The data are available from FRED and are in the file m-morgfed-7111.txt (year, mon, day, morg, fed).(a) Build a pure time series model for the monthly mortgage rate. Perform model checking and write down
Consider the daily returns of the exchange trade fund (ETF) SPDR S\&P 500 of State Street Global Advisors from September 4, 2001, to September 30, 2011. The tick symbol is SPY and there are 2535 observations. The simple returns are available from CRSP and in the file d-spy-0111.txt. Transform the
Consider, again, the SPY log return series of Problem 1.(a) Fit an ARMA-APACRH model with Gaussian innovations to the data. Perform model checking and write down the model. You may ignore the lag-1 ARCH parameter as it is not statistically significant.](b) Fit an ARMA-APARCH model with Student
Consider the monthly stock returns of the Coca-Cola Company (KO) from January 1961 to September 2011. The simple returns are available from CRSP and in the file m-ko-6111.txt. Transform the simple returns to log returns.(a) Is the expected monthly log return zero? Is there any serial correlation in
Consider again the monthly log returns of KO stock. Multiple the log returns by 100 . That is, use percentage log returns.(a) Fit a TGARCH model to the series. Perform model checking and write down the fitted model. Is the level effect different from zero?(b) Fit an NGARCH model to the series.
Consider the daily stock returns of Procter & Gamble from September 1, 2001, to September 30, 2011. The simple returns are available from CRSP and in the file d-pg-0111.txt. Transform the simple returns to log returns.(a) Is there any serial correlation in the log returns?(b) Fit an ARMA model to
Use the quantmod package to obtain the daily prices of Apple stock from January 2, 2007, to November 30, 2011.(a) Consider the log prices. Use Yang and Zhang method with window sizes 63 and 32 to obtain the daily volatility of the stock.(b) Use an ARMA-GARCH model to obtain the daily volatility of
Consider the weekly world crude oil prices from January 3, 1997 to September 24, 2010. The data are available from the US Energy Information Administration and in the file w-petroprice.txt. Focus on the change series of the price.- Build a pure ARMA model for the change series of the world oil
Consider the daily log returns of the Abbott Laboratories stock from January 2, 2001 to December 31, 2010. The simple return of the stock is given in da2a-0110 . txt. Employ a GARCH(1,1) model with Gaussian innovations to obtain the volatility term structure from $t=2011$ to $t=2515$ for $h=$
Use the S\&P 500 index to represent the US Market. Obtain the time-varying betas for the Abbott Laboratories stock. The sample period is from January 2, 2001 to December 31, 2010. The data are available from CRSP or Yahoo Finance, or the file d-abtsp-0110 . txt.
Suppose that a portfolio consists of three US stocks, namely, Alcoa, American Express, and Abbott Laboratories. The daily simple returns are available from the file d-a2a-0110.txt. Use GARCH models to obtain the conditional covariance matrices of the stocks for the period from December 29, 2008 to
Consider the daily returns of Apple stock from January 2, 2001 to December 31, 2010. The data are available from the file $\mathrm{d}-\mathrm{a2a}-0110 . \mathrm{txt}$. Build a Gaussian GARCH $(1,1)$ model for the daily log returns. Assume that the risk free interest rate is $1 \%$ per annum
Let $r_{t}$ be the log return of an asset at time $t$. Assume that $\left\{r_{t}\right\}$ is a Gaussian white noise series with mean 0.02 and variance 0.04 . Suppose that the probability of a trade at each time point is $50 \%$ and is independent of $r_{t}$. Denote the observed return by
Let $P_{t}$ be the observed market price of an asset, which is related to the fundamental value of the asset $P_{t}^{*}$ via Equation (6.9). Assume that $\Delta P_{t}^{*}=P_{t}^{*}-P_{t-1}^{*}$ forms a Gaussian white noise series with mean 0 and variance 1.0. Suppose that the bid-ask spread
The file taq-aa-t-june 72010 . txt contains the tick-by-tick trading data of Alcoa stock on June 7, 2010. It has seven columns, namely, date, hour, minute, second, price, and volume. Focus on the transactions that occurred during the normal hours.(a) Obtain the nonzero time durations between
Consider, again, the transactions data of Alcoa stock on June 7, 2010. Focus on the transactions occurred during the normal trading hours.(a) Obtain the price change series and its histogram.(b) Divide the price changes into seven categories as those in Table 6.3. What is the percentage of
Consider the questions of the prior problem, but using the transactions data of Alcoa stock on June 8, 2010. The data are in the file taq-aa-t-june82010.txt.
The file taq-aa-t-june7t112010. txt contains the transactions data of Alcoa stock from June 7 to June 11, 2010. Focus on the transactions during the normal trading hours. Consider the number of transactions within a 5-min time interval. Denote the resulting series by $x_{t}$. Is there any diurnal
Consider the tick-by-tick transactions data of the stock of Starbucks from July 25 to July 29, 2011. The data are in taq-sbux-jul2011.txt. Focus on trades occurred in the normal trading hours.(a) Obtain the intraday 5-min log returns of the stock. Plot the histogram of the returns.(b) Obtain daily
Consider a long position of $\$ 1$ million on the Apple stock. To assess the risk of the position, we employ daily returns of the stock from January 2, 2001 to September 30, 2011 for 2704 observations. The daily simple returns are obtained from CRSP and in the file d-aapl-0111.txt. Let the tail
Again, consider the position and data of Exercise 1. Compute the VaR for the next trading day using the following methods:(a) Empirical quantile with $p=0.05$ and 0.01 .(b) Quantile regression with $1-p=0.95$ and 0.99 using lag-1 volatility of a Gaussian GARCH $(1,1)$ model and lag-1 absolute
Again, consider the position and data of Exercise 1. Answer the following questions:(a) Obtain estimates of $\mu, \sigma$, and $\xi$ using the traditional EVT with subperiod length $n=21$ and 42 , respectively.(b) Use the estimates to compute $\mathrm{VaR}_{0.95}$ and
Again, consider the position and data of Exercise 1. Apply the POT method to calculate risk measures for the position:(a) Use threshold 2.5\%. Write down the estimates, including standard error, and compute the corresponding $\mathrm{VaR}_{0.99}$ and $\mathrm{ES}_{0.99}$.(b) Use threshold \(2
Again, consider the position and data of Exercise 1. In this exercise, apply the GPD with threshold $2.5 \%$ to obtain the parameter estimates and the corresponding diagnostic plots. Compute $\mathrm{VaR}_{0.99}(10)$ of the position, that is, the VaR for the next 10 trading days.Data From
Consider the daily log returns of Apple stock and the log returns of Bank of America Merrill Lynch U.S. Corp AAA Index Value from January 3, 2001 to September 30, 2011. The bond index has more daily observations, but we match the dates before computing the daily log returns of bond index. The data

An Introduction To Analysis Of Financial Data With R 1st Edition Ruey S Tsay - Solutions

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