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The purpose of this paper is to present a model and simulation results for the corporate financial value of an industry in a cobweb economy. Production‐consumption cycles affect profit and financial needs in terms of equity and debt capital, and thus corporate value over time. The model is applied to the Champagne industry.

The paper simulates the financial value of Champagne makers by taking into account developments on the Champagne market and the short‐ and long‐term responses by input suppliers and Champagne makers. Financial modeling is combined with a vertical coordination model of production and consumption in a cobweb economy.

This paper makes theoretical advances in modeling the impact of short‐ and long‐run temporal tensions in production decisions on the financial value of processors. Temporal tensions are central to the decisions made by input suppliers (grape growers) and processors (Champagne makers) as they negotiate in a context of vertical coordination in a cobweb economy. Financial aggregates are forecast by the model and used as market multiples for estimating corporate financial value. Furthermore, this research strengthens previously published simulation studies in agriculture and food markets since system dynamics (SD) is applied in modeling both input production and consumption and the processor's financial value. SD modeling is well suited to simulation in a critical context, and Champagne makers find themselves in such a context: the Champagne protected designation of origin (PDO) area has reached its legally authorized size limit, while world demand continues to grow.

The market for corporate control of Champagne makers is active. The model presented is a useful guide for decision makers because it improves the anticipation of corporate value and improves understanding of the future of value creation in a legal framework currently considering revision of both authorized annual yield (short‐run decision) and the size of the appellation area (long‐run decision).

Two original features of this paper add specific value to the existing research: first, the theory is enlarged to capture the temporal tensions affecting decision making by input suppliers and processors operating in a cobweb economy, and deduce processors' financial value using financial aggregates forecast by the model. Second, the SD simulation method is applied in modeling input production and consumption and processors' financial value.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Lessons from the most recent financial crisis show specific vulnerabilities of financial markets due to weaknesses in the structure of the financial system (structural fragility). As the literature points out, the impact of systemic risk can be closely related to issues of concentration (“too big to fail”) and dependency (“too connected to fail”). However, different structural variables are emphasized in various ways, and most authors analyze each variable separately. This raises the questions of how structural fragility, as a cause of systemic distress, can be assessed more comprehensively and consistently, and what the implications are for modeling it within an integrated systemic risk framework. This paper seeks to address these issues.

On the basis of theoretical considerations and in the light of current transformations in financial markets, this paper explores elements of structural fragility and the requirements for modeling them.

The paper suggests an extended approach for conceptualizing structural fragility, evaluates directions for quantifying structural issues in early warning systems (EWSs) for systemic crises, and lays a theoretical groundwork for further empirical studies.

The need for supervisory actions to prevent crises is urgent, as is the need for integrating structural aspects into EWSs for systemic financial crises. Since a significant aspect of a financial firm's risk comes from outside the firm, individual institutions should understand and monitor the structural aspects of the various risk networks they are in.

Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:

This study aims to critically review recent contributions to the methodology of financial economics and discuss how they relate to one another and directions for further research.

A critical review of recent literature on new methodologies for financial economics.

Recent books have made important contributions to the study of financial economics. They suggest new approaches that include an emphasis on radical uncertainty, adaptive markets, agent-based modeling and narrative economics, as well as extensions of behavioral finance to include concepts such as diagnostic expectations. Many of these contributions can be seen more as complements than substitutes and provide fruitful directions for further research. Efficient markets can be seen as holding under particular circumstances. A major them of most of these contributions is that the study of financial crises and other aspects of financial economics requires the use of multiple theories and approaches. No one approach will be sufficient.

There are great opportunities for further research in financial economics making use of these new approaches.

These recent contributions can be quite useful for improved analysis by researchers, private participants in the financial sector and macroeconomic and regulatory officials.

Provides an introduction to these new approaches and highlights fruitful areas for their extensions and applications.

The purpose of this paper is to build and compare models that assess university students’ financial literacy. Financial literacy, understood as the mastery of a set of knowledge, attitudes and behaviors, has assumed a fundamental role in allowing and enabling people to make responsible decisions as they strive to attain financial wellbeing. To this end, models that integrate financial knowledge, behavior and attitude are integrated. The models are subsequently estimated, and many comparative tests are performed.

The study investigated a random sample of 534 university students attending public and private universities in southern Brazil. The choice of scale was based on consideration of the best adjustment for the Brazilian context, appropriate translation and content validation. For an analysis of the collected data, structural equation modeling was employed using two strategies.

The findings indicate that, in the model estimation stage, the scales for behavior and attitude have been reduced. Among all of the models estimated, the best adjusted model indicates that financial knowledge and financial attitude have positive impacts on financial behavior.

The results are not generalizable to the wider population; to enable such generalization, different profiles should be researched using a larger sample. In practical terms, the financial behavior of Brazilian university students expresses the ability to establish long-term aims and saving aimed at future acquisitions and unexpected spending. This behavior is directly influenced by basic and advanced questions of financial knowledge and also by the importance attributed to attitude by establishing aims, control of spending and financial reserves.

This paper describes a pioneer study with respect to modeling financial literacy in Brazil. This topic can be improved as the need for rigorous evaluation of financial literacy grows at the same speed as the creation of more complex financial products.

Currently many jobs for undergraduate finance majors require that the student demonstrate advanced Excel modeling skills. The purpose of this paper is to illustrate and explain the Excel Best Practices which should enhance their financial modeling efficiency.

The focus is a way to teach the Excel Best Practices when teaching financial modeling with Excel 2007. It uses a chronological modeling procedure that is consistent with current learning theory and the way students should use these Excel Best Practices. A capital budgeting replacement problem is used to illustrate many of the Excel Best Practices.

It was found that using a chronological modeling procedure is consistent with current learning theory.

Using the procedures mentioned in this paper should result in efficient financial modeling. Efficient models are created in less time, have fewer errors, if any, and are designed for ease of use.

The purpose of this paper is to investigate whether Markov mixture of normals (MMN) model is a viable approach to modeling financial returns.

This paper adopts the full Bayesian estimation approach based on the method of Gibbs sampling, and the latent state variables simulation algorithm developed by Chib.

Using data from the S&P 500 index, the paper first demonstrates that the MMN model is able to capture the unconditional features of the S&P 500 daily returns. It further conducts formal model comparisons to examine the performance of the Markov mixture structures relative to two well‐known alternatives, the GARCH and the t‐GARCH models. The results clearly indicate that MMN models are viable alternatives to modeling financial returns.

The univariate MMN structure in this paper can be generalized to a multivariate setting, which can provide a flexible yet practical approach to modeling multiple time series of assets returns.

Given the encouraging empirical performance of the MMN models, it is hopeful that the MMN models will have success in some interesting financial applications such as Value‐at‐Risk and option pricing.

The paper explicitly formulates the Gibbs sampling procedures for estimating MMN models in a Bayesian framework. It also shows empirically that MMN models are able to capture the stylized features of financial returns. The MMN models and their estimation method in this paper can be applied to other financial data, especially in which tail probability is of major interest or concern.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.