This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

Introductory Econometrics for Finance

Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. Using “extreme value theory” to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets. INTERNATIONAL EQUITY MARKET CORRELATION has been widely studied. Previous studies 1 suggest that correlation is larger when focusing on large absolutevalue returns, and that this seems more important in bear markets. The conclusion that international correlation is much higher in periods of volatile markets ~large absolute returns! has indeed become part of the accepted wisdom among practitioners and the financial press. However, one should exert great care in testing such a proposition. The usual approach is to condition the estimated correlation on the observed ~or ex post! realization of market returns. Unfortunately correlation is a complex function of returns and such tests can lead to wrong conclusions, unless the null hypothesis and

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Extreme Correlation of International Equity Markets

A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides.

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Historically, the fields of exchange-rate economics and microstructure finance have progressed independently of each other. Recent interaction, however, has given rise to a microstructure approach to exchange rates. This book focuses on the economics of financial information and how microstructure tools help to clarify the types of information most relevant to exchange rates. The microstructure approach views exchange rates from the perspective of the trading room, the place where exchange rates are actually determined. Emphasizing information economics over institutional issues, the approach departs from three unrealistic assumptions common to previous approaches: that all information relevant to exchange rates is publicly available, that all market participants are alike in their goals or in how they view information, and that how trading is organized is inconsequential for exchange rates. The book shows how exchange-rate behavior previously thought to be particularly puzzling can be explained using the microstructure approach. It contains a combination of theoretical and empirical work.

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The Microstructure Approach to Exchange Rates

https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

Financial returns are known to be nonnormal and tend to have fat-tailed distributions. This article presents a simple methodology that accurately estimates the degree of tail fatness, characterized by the tail index, in small samples. Our method is a weighted average of Hill estimators for different threshold values that corrects for the small-sample bias apparent in the latter. Using this estimator we produce tail-index estimates for returns on exchange rates that are close to nonbiased estimates obtained from extremely large datasets. The results indicate that many documented conclusions concerning the tail behavior of financial series are likely to have overestimated the tail fatness in small samples.

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Tail Index Estimates in Small Samples

https://repub.eur.nl/pub/111/erimrs20010815170147.pdf

Regime jumps in electricity prices

In this paper, we develop a portfolio selection model which allocates financial assets by maximising expected return subject to the constraint that the expected maximum loss should meet the Value-at-Risk limits set by the risk manager. Similar to the mean–variance approach a performance index like the Sharpe index is constructed. Furthermore when expected returns are assumed to be normally distributed we show that the model provides almost identical results to the mean–variance approach. We provide an empirical analysis using two risky assets: US stocks and bonds. The results highlight the influence of both non-normal characteristics of the expected return distribution and the length of investment time horizon on the optimal portfolio selection.

Optimal Portfolio Selection in a Value-at-Risk Framework

We examine the effects of price disclosure on market performance in a continuous experimental multiple-dealer market in which seven professional market makers trade a single security. The dealers trade with one another and with computerized informed and liquidity traders. Our key comparison is between fully public price queues (pretrade transparent market) and bilateral quoting (pretrade opaque). We find that opening spreads are wider and trading volume is lower in the opaque markets due to higher search costs there. More importantly, however, higher search costs also induce more aggressive pricing strategies, so that price discovery is much faster in the opaque markets. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Quote Disclosure and Price Discovery in Multiple-Dealer Financial Markets

Electricity prices are known to be very volatile and subject to frequent jumps due to system breakdown, demand shocks, and inelastic supply. As many international electricity markets are in some state of deregulation, more and more participants in these markets are exposed to these stylised facts. Appropriate pricing, portfolio, and risk management models should incorporate these facts. Authors have introduced stochastic jump processes to deal with the jumps, but we argue and show that this specification might lead to problems with identifying the true mean-reversion within the process. Instead, we propose using a regime jump model that disentangles mean-reversion from jump behaviour. This model resembles more closely the true price path of electricity prices.

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Ronald Huisman

Papers

Tail Index Estimates in Small Samples

Regime jumps in electricity prices

Optimal Portfolio Selection in a Value-at-Risk Framework

Quote Disclosure and Price Discovery in Multiple-Dealer Financial Markets

Regime Jumps in Electricity Prices