Introductory Econometrics for Finance. SECOND EDITION. This best-selling textbook addresses the need for an introduction to econometrics specifically written. Introductory econometrics for finance / Chris Brooks, The ICMA Centre, Henley. Business School .. The pdf for a normal distribution. The cdf for a. Cambridge Core - Finance and Accountancy - Introductory Econometrics for Finance - by Chris Brooks. pp i-iv. Access. PDF; Export citation. Contents. pp v- xi.

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Cambridge Core - Econometrics and Mathematical Methods - Introductory Econometrics for Finance - by Chris Brooks. 1 Introduction. 1. What is econometrics? 1. Is financial econometrics different from 'economic econometrics'? Some stylised characteristics of financial . Introductory Econometrics for Finance Is financial econometrics different from 'economic .. The pdf for a normal distribution.

Keywords: Employee stock option, Sentiment, Subjective value, Illiquidity, Change of measure, Hierarchical clustering with K-Means approach, Standard errors in finance panel data, Exercise boundary, Jump diffusion model Chapter Structural Change and Monitoring Tests This chapter focuses on various structural change and monitoring tests for a class of widely used time-series models in economics and finance, including I 0 , I 1 , I d processes and the co-integration relationship.

In general, structural change tests can be categorized into two types: One is the classical approach to testing for structural change, which employs retrospective tests using a historical data set of a given length; the other one is the fluctuation-type test in a monitoring scheme, which means for given a history period for which a regression relationship is known to be stable, we then test whether incoming data are consistent with the previously established relationship.

Several structural changes such as CUSUM squared tests, the QLR test, the prediction test, the multiple break test, bubble tests, co-integration breakdown tests, and the monitoring fluctuation test are discussed in this chapter, and we further illustrate all details and usefulness of these tests.

Keywords: Co-integration breakdown test, Structural break, Long memory process, Monitoring fluctuation test, Boundary function, CUSUM squared test, Prediction test, Bubble test, Unit root time series, Persistent change Chapter Consequences of Option Pricing of a Long Memory in Volatility This chapter use conditionally heteroskedastic time-series models to describe the volatility of stock index returns.

Volatility has a long memory property in the most general models and then the autocorrelations of volatility decay at a hyperbolic rate; contrasts are made with popular, short memory specifications whose autocorrelations decay more rapidly at a geometric rate.

Options are valued for ARCH volatility models by calculating the discounted expectations of option payoffs for an appropriate risk-neutral measure. Monte Carlo methods provide the expectations. The speed and accuracy of the calculations is enhanced by two variance reduction methods, which use antithetic and control variables.

The economic consequences of a long memory assumption about volatility are documented, by comparing implied volatilities for option prices obtained from short and long memory volatility processes. Keywords: ARCH models, Implied volatility, Index options, Likelihood maximization, Long memory, Monte Carlo, Option prices, Risk-neutral pricing, Smile shapes, Term structure, Variance reduction methods Chapter Seasonal Aspects of Australian Electricity Market This chapter develops econometric models for seasonal patterns in both price returns and proportional changes in demand for Australian electricity.

Australian Electricity spot prices differ considerably from equity spot prices in that they contain an extremely rapid mean reversion process. This has implications for derivative pricing and risk management. We also model extreme spikes in the data. Our study identifies both seasonality effects and dramatic price reversals in the Australian electricity market.

Introductory Econometrics for Finance Solutions Manual

The pricing seasonality effects include time-of-day, day-of-week, monthly, and yearly effects. There is also evidence of seasonality in demand for electricity. Keywords: Electricity, Spot price, Seasonality, Outlier, Demand, Econometric modeling Chapter Pricing Commercial Timberland Returns in the United States This chapter uses both parametric and nonparametric approaches to evaluate private- and public-equity timberland investments in the United States. Private-equity timberland returns are proxied by the NCREIF Timberland Index, whereas public-equity timberland returns are proxied by the value-weighted returns on a dynamic portfolio of the US publicly traded forestry firms that had or have been managing timberlands.

Static estimations of the capital asset-pricing model and Fama-French three-factor model are obtained by ordinary least squares, whereas dynamic estimations are obtained by state-space specifications with the Kalman filter.

In estimating the stochastic discount factors, linear programming is used. Keywords: Alternative asset class, Asset pricing, Evaluation, Fama-French three-factor model, Nonparametric analysis, State-space model, Stochastic discount factor, Timberland investments, Time series, Time-varying parameter Chapter Optimal Orthogonal Portfolios with Conditioning Information This chapter derives and characterizes optimal orthogonal portfolios in the presence of conditioning information in the form of a set of lagged instruments.

In this setting, studied by Hansen and Richard , the conditioning information is used to optimize with respect to the unconditional moments. We present an empirical illustration of the properties of the optimal orthogonal portfolios.

The methodology in this chapter includes regression and maximum likelihood parameter estimation, as well as method of moments estimation. We form maximum likelihood estimates of nonlinear functions as the functions evaluated at the maximum likelihood parameter estimates. Keywords: Asset-pricing tests, Conditioning information, Minimum variance efficiency, Optimal portfolios, Predicting returns, Portfolio management, Stochastic discount factors, Generalized, Method of moments, Maximum likelihood, Parametric bootstrap, Sharpe ratios Chapter Multi-factor, Multi-indicator Approach to Asset Pricing: Method and Empirical Evidence This chapter uses a multifactor, multi-indicator approach to test the capital asset-pricing model CAPM and the arbitrage pricing theory APT.

This approach is able to solve the measuring problem in the market portfolio in testing CAPM, and it is also able to directly test APT by linking the common factors to the macroeconomic indicators. Therefore, the measurement error problem on beta is more serious than that on the market portfolio.

At the same time, this chapter will discuss the Binomial Option Pricing Model in a less mathematical fashion. All the mathematical calculations will be taken care by the Microsoft Excel program that is presented in this chapter. Firms: An Econometric Approach This chapter uses the econometric methodology to deal with the dynamic inter-relationships between dividend payments and share redownloads and investigate endogeneity of certain explanatory variables.

Identification of the model parameters is achieved in such models by exploiting the cross-equations restrictions on the coefficients in different time periods. Moreover, the estimation entails using nonlinear optimization methods to compute the maximum likelihood estimates of the dynamic random-effects models and for testing statistical hypotheses using likelihood ratio tests.

This study also highlights the importance of developing comprehensive econometric models for these interrelationships. Keywords: Compustat database, Corporate policies, Dividends, Dynamic random-effects models, Econometric methodology, Endogeneity, Maximum likelihood, Intangible assets, Model formulation, Nonlinear optimization, Panel data, Share redownloads Chapter Term Structure Modeling and Forecasting Using the Nelson-Siegel Model In this chapter, we illustrate some recent developments in the yield curve modeling by introducing a latent factor model called the dynamic Nelson-Siegel model.

This model not only provides good in-sample fit, but also produces superior out-of-sample performance. Beyond Treasury yield curve, the model can also be useful for other assets such as corporate bond and volatility. Moreover, the model also suggests generalized duration components corresponding to the level, slope, and curvature risk factors.

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The dynamic Nelson-Siegel model can be estimated via a one-step procedure, like the Kalman filter, which can also easily accommodate other variables of interests.

Alternatively, we could estimate the model through a two-step process by fixing one parameter and estimating with ordinary least squares. The model is flexible and capable of replicating a variety of yield curve shapes: upward sloping, downward sloping, humped, and inverted humped. Forecasting the yield curve is achieved through forecasting the factors and we can impose either a univariate autoregressive structure or a vector autoregressive structure on the factors.

This chapter examines the presence and significance of an intertemporal relation between expected return and risk in the foreign exchange market.

This chapter tests the existence and significance of a daily risk-return trade-off in the FX market based on the GARCH, realized, and range volatility estimators. We also use the seemingly unrelated SUR regressions and panel data estimation to investigate the significance of a time-series relation between expected return and risk on currency. VaRs estimated by the QR method display some nice properties. In this chapter, different QR models in estimating VaRs are introduced. Comparing the proposed QR method with traditional methods based on distributional assumptions, the QR method has the important property that it is robust to non-Gaussian distributions.

Quantile estimation is only influenced by the local behavior of the conditional distribution of the response near the specified quantile. As a result, the estimates are not sensitive to outlier observations.

Such a property is especially attractive in financial applications since many financial data like, say, portfolio returns or log returns , are usually not normally distributed.


Numerical evidence indicates that QR is a robust estimation method for VaR. The econometric method employed is regressions of panel data. In a panel data setting, I address both cross-sectional and time-series dependence.

Following Gow et al. Keywords: Earnings quality, Board structure, Corporate ownership structure, Panel data regressions, Cross-sectional and time-series dependence, Two-way clustering method of standard errors Chapter Rationality and Heterogeneity of Survey Forecasts of the Yen-Dollar Exchange Rate: A Reexamination This chapter examines the rationality and diversity of industry-level forecasts of the yen-dollar exchange rate collected by the Japan Center for International Finance.

Our GMM tests of micro-homogeneity uniformly reject the hypothesis that forecasters exhibit similar rationality characteristics. The theoretical asymptotic distribution of the measurement error process for these estimators is considered for particular features observed in intraday financial asset price processes.

Specifically, I consider the effects of i induced serial correlation in returns processes, ii excess kurtosis in the underlying unconditional distribution of returns, iii market anomalies such as market opening and closing effects, and iv failure to account for intraday trading patterns.

Value-at-risk VaR measures the worst expected loss over a given time horizon under normal market conditions at a specific level of confidence. VaR is determined by the left tail of the cumulative probability distribution of expected returns.

Our result indicates the cumulative probability distribution has a fatter left tail, compared with the left tail of a normal distribution. This implies a riskier market. We also examined a two-sector asset allocation model subject to a target VaR constraint. The growth rate is found by taking the product of the retention rate and the return on equity.

What is less well understood are the basic assumptions of this model. In this paper, we demonstrate that the model makes strong assumptions regarding the financing mix of the firm.

We demonstrate that the average percentage change is very sensitive to extreme observations. Moreover, on average, the regression method yields similar but somewhat smaller estimates of the growth rate compared to the compound-sum method. We also discussed the inferred method suggested by Gordon and Gordon to estimate the growth rate.

Advantages, disadvantages, and the interrelationship among these estimation methods are also discussed in detail. Keywords: Compound sum method, Discount cash flow model, Growth rate, Internal growth rate, Sustainable growth rate Chapter Econometric Measures of Liquidity A security is liquid to the extent that an investor can trade significant quantities of the security quickly, at or near the current market price, and bearing low transaction costs.

As such, liquidity is a multidimensional concept.

These alternative proxies have been designed to be estimated using either low-frequency or high-frequency data, so I discuss four liquidity proxies that are estimated using low-frequency data and two proxies that require high-frequency data. Low-frequency measures permit the study of liquidity over relatively long time horizons; however, they do not reflect actual trading processes. To overcome this limitation, high-frequency liquidity proxies are often used as benchmarks to determine the best low-frequency proxy.

In this chapter, I find that estimates from the effective tick measure perform best among the four low-frequency measures tested. We also provide empirical support that GARCH models based on the normal innovations appear not to be as well suited as infinitely divisible models for predicting financial crashes.

Securities trading is one of the few business activities where a few seconds of processing delay can cost a company big fortune. Grid and Cloud computing will be briefly described. How the underlying algorithm for financial analysis can take advantage of Grid environment is chosen and presented.

One of the most popular practiced algorithms Monte Carlo Simulation is used in our cases study for option pricing and risk management. The various distributed computational platforms are carefully chosen to demonstrate the performance issue for financial services. Keywords: Financial service, Grid and cloud computing, Monte Carlo simulation, Option pricing, Risk management, Cyberinfrastructure, Random number generation, High end computing, Financial simulation, Information technology Chapter Long-Run Stock Return and the Statistical Inference This chapter introduces the long-run stock return methodologies and their statistical inference.

Two categories of long-run return methods are illustrated in this chapter: the event-time approach and calendar-time approach. The event-time approach includes cumulative abnormal return, download-and-hold abnormal return, and abnormal returns around earnings announcements. In former two methods, it is recommended to apply the empirical distribution from the bootstrapping method to examine the statistical inference, whereas the last one uses classical t-test.

In addition, the benchmark selections in the long-run return literature are introduced. The measure of accuracy includes the unconditional coverage test by Kupiec as well as two loss functions, quadratic loss function and unexpected loss.

Sequentially, the VaR is evaluated by the framework proposed by Jorion Turning to the semi-parametric approach of Hull and White , before performing the traditional historical simulation, the raw return series is scaled by a volatility ratio where the volatility is estimated by the same procedure of parametric approach. To build a time-varying structure for the correlation matrix of t copula, we employ a dynamic conditional correlation DCC specification.

Brooks C. Introductory Econometrics for Finance

We illustrate the two-stage estimation procedures for the model and apply the model to 45 major US stocks returns selected from nine sectors. As it is quite challenging to find a copula function with very flexible parameter structure to account for difference dependence features among all pairs of random variables, our time-varying t copula model tends to be a good working tool to model multiple asset returns for risk management and asset allocation purposes. Our model can capture time-varying conditional correlation and some degree of tail dependence, while it also has limitations of featuring symmetric dependence and inability of generating high tail dependence when being used to model a large number of asset returns.

This shows the superiority of the MVR test statistic in revealing short-term performance and, in turn, enables investors to make better decisions in their investments. The methodologies used in this chapter are mean-variance ratio, Sharpe ratio, hypothesis testing, and uniformly most powerful unbiased test.

The basic technical elements of CoVaR estimation are two levels of quantile regression: one on market risk factors; another on individual risk factor.

RATS Handbook to Accompany Introductory Econometrics for Finance

Tests on the functional form of the two-level quantile regression reject the linearity. A flexible semi-parametric modeling framework for CoVaR is proposed. A partial linear model PLM is analyzed. In applying the technology to stock data covering the crisis period, the PLM outperforms in the crisis time, with the justification of the backtesting procedures. Moreover, using the data on global stock markets indices, the analysis on marginal contribution of risk MCR defined as the local first order derivative of the quantile curve sheds some light on the source of the global market risk.

Keywords: CoVAR, Value-at-risk, Quantile regression, Locally linear quantile regression, Partial linear model, Semi-parametric model Chapter Strike Prices of Options for Overconfident Executives This chapter uses Monte Carlo simulation to investigate the impacts of managerial overconfidence on the optimal strike prices of executive incentive options.

Although it has been shown that optimally managerial incentive options should be awarded in-the-money, in practice, most firms award them at-the-money. We show that the optimal strike prices of options granted to overconfident executive are directly related to their overconfidence level, and that this bias brings the optimal strike prices closer to the institutionally prevalent at-the-money prices.

The Monte Carlo simulation procedure uses a Mathematica program to find the optimal effort by managers and the optimal for stockholders contract parameters.

An expanded discussion of the simulations, including the choice of the functional forms and the calibration of the parameters, is provided. Keywords: Overconfidence, Managerial effort, Incentive options, Strike price, Simulations, Behavioral finance, Executive compensation schemes, Mathematica optimization, Risk aversion, Effort aversion Chapter Density and Conditional Distribution Based Specification Analysis This chapter uses densities and conditional distributions analysis to carry out consistent specification testing and model selection among multiple diffusion processes.

In this chapter, we discuss advances to this literature introduced by Corradi and Swanson , who compare the cumulative distribution marginal or joint implied by a hypothesized null model with corresponding empirical distributions of observed data.

In particular, parametric specification tests in the spirit of the conditional Kolmogorov test of Andrews that rely on block bootstrap resampling methods in order to construct test critical values are discussed. The methodologies used in this chapter are continuous time simulation methods, single process specification testing, multiple process model selection, and multifactor diffusion process, block bootstrap, and jump process.

Keywords: Multifactor diffusion process, Specification test, Out-of-sample forecasts, Conditional distribution, Model selection, Block bootstrap, Jump process Chapter Assessing the Performance of Estimators Dealing with Measurement Errors This chapter describes different procedures to deal with measurement error in linear models, and assess their performance in finite samples using Monte Carlo simulations, and data on corporate investment.

We consider the standard instrumental variables approach proposed by Griliches and Hausman as extended by Biorn [OLS-IV], the Arellano and Bond instrumental variable estimator, and the higher-order moment estimator proposed by Erickson and Whited , In the presence of fixed effects, under heteroscedasticity, or in the absence of a very high degree of skewness in the data, the EW estimator is inefficient and returns biased estimates for mismeasured and perfectly measured regressors.

The IV approach, however, requires assumptions about the autocorrelation structure of the mismeasured regressor and the measurement error. We illustrate the application of the different estimators using empirical investment models. Methodologically, we extend the Dynamic Conditional Correlation DCC multivariate GARCH model to examine the volatility and correlation dynamics depending on the variances of price returns involving a threshold structure.

The results indicate that the periods of market turbulence are associated with an increase in co-movements in commodity gold and oil prices. The results imply that gold may act as a safe haven against major currencies when investors face market turmoil.

Responses reveals that an expansionary interest policy shock leads to a decrease in price level, a fall in output, an appreciation in the exchange rate, an improvement in the share prices in the very short run for the most of Pre-IT period.

The purpose of this chapter is to investigate whether the influences of accounting factors on capital structure change and whether the important factors are consistent with the previous literature. It is shown that OLS hedge strategy outperforms most of the optimal conditional hedge strategies when EHE is used as the hedging effectiveness criteria. Before concluding that OLS hedge is better than the others; however, we need to understand under what circumstances the result is derived.

Keywords: Futures hedging, Portfolio management, Ederington hedging effectiveness, Variance estimation, Unconditional variance, Conditional variance, OLS hedging strategy, GARCH hedging strategy, Regime-switching hedging strategy, Utility-based hedging strategy Chapter Evidence on Earning Management by Integrated Oil and Gas Companies This chapter uses Jones Model which projects the expected level of discretionary accruals and demonstrates specific test methodology for detection of earnings management in the oil and gas industry.

This study utilized several parametric and nonparametric statistical methods to test for such earnings management. By comparing actuals versus projected accruals, we are able to compute the total unexpected accruals. Keywords: Earning management, Jones model, Discretionary accruals, Income from operations, Nonrecurring items, Special items, Research and development expense, Write-downs, Political cost, Impression management, Oil and gas industry Chapter A Comparative Study of Two Models SV with MCMC Algorithm This chapter examines two asymmetric stochastic volatility models used to describe the volatility dependencies found in most financial returns.

Therefore, the technique used is a Metropolishastings Hastings , and the Gibbs sampler Casella and George ; Gelfand and Smith ; Gilks et al. We re-estimate the models using rank regression technique to assess the sensitivity of the results to the underlying functional form assumption made by OLS.

We show that short-term financing is a response to the adverse economic shocks rather than a cause of the recent crisis. The likelihood of financial crisis actually stems from the illiquidity and low creditworthiness of the investment. Our results are robust to endogeneity concerns when we use a difference-in-differences DiD approach with the Lehman bankruptcy in proxying for an exogenous shock. OLS large sample results robust inference 6.

Endogeneity and instrumental variables 7. Practical exercises for material covered in lecture "Introductory Econometrics" Date: Wednesday, Twenty five easy pieces in mathematical statistics To be prepared for next practical classes Slides from introductory lecture of today You can let the programs run on these machines.

To borrow the book you need to leave your student ID with Mrs. If you use the 3. The name of the file must not have more than 8 characters. Note also that the folder names must not exceed 8 characters. This wednesday we will have 2 hours lecture. The exercise by Oliver will be given on Monday 2 hours.

If there is additional demand a second exercise is possible from 6 p. Please prepare the following tasks for the Wednesday exercise:By means of Cumulative Accuracy Profile, the Receiver Operating Characteristics, and McFadden, we measure the goodness-of-fit and the accuracy of each prediction model.

Keywords: Alternative asset class, Asset pricing, Evaluation, Fama-French three-factor model, Nonparametric analysis, State-space model, Stochastic discount factor, Timberland investments, Time series, Time-varying parameter Chapter Optimal Orthogonal Portfolios with Conditioning Information This chapter derives and characterizes optimal orthogonal portfolios in the presence of conditioning information in the form of a set of lagged instruments.

Plus, we regularly update and improve textbook solutions based on student ratings and feedback, so you can be sure you're getting the latest information available. This is prevalent especially for out-of-the-money OTM options where the previous lower bounds perform badly. As finance and financial products have become more complex, financial econometrics has emerged as a fast-growing field and necessary foundation for anyone involved in quantitative finance.

Slight change of plan: Limited dependent variable models; Constant width text is generally used in paragraphs to refer to R code. We also discuss the effect of issuer default risk on BCLN.