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The purposes of this article are to evaluate models of stock market risk developed by Robert Engle, and related models (ARCH, GARCH, VAR, etc.); to establish whether prospect theory, cumulative prospect theory, expected utility theory, and market‐risk models (ARCH, GARCH, VAR, etc.) are related and have the same foundations.

The author critiques existing academic work on risk, decision making, prospect theory, cumulative prospect theory, expected utility theory, VAR and other market‐risk models (ARCH, GARCH, etc.) and analyzes the shortcomings of various measures of risk (standard deviation, VAR, etc.).

Prospect theory, cumulative prospect theory, expected utility theory, and market‐risk models are conceptually the same and do not account for many facets of risk and decision making. Risk and decision making are better quantified and modeled using a mix of situation‐specific dynamic, quantitative, and qualitative factors. Belief systems (a new model developed by the author) can better account for the multi‐dimensional characteristics of risk and decision making. The market‐risk models developed by Engle and related models (ARCH, GARCH, VAR, etc.) are inaccurate, do not incorporate many factors inherent in stock markets and asset prices, and thus are not useful and accurate in many asset markets.

Areas for further research include: development of dynamic market‐risk models that incorporate asset‐market psychology, liquidity, market size, frequency of trading, knowledge differences among market participants, and trading rules in each market; and further development of concepts in belief systems.

Decision making and risk assessment are multi‐criteria processes that typically require some processing of information, and thus cannot be defined accurately by rigid quantitative models. Existing market‐risk models are inaccurate – many international banks, central banks, government agencies, and financial institutions use these models for risk management, capital allocation, portfolio management, and investments, and thus the international financial system may be compromised.

The critiques, ideas, and new theories in the article were all developed by the author. The issues discussed in the article are relevant to a multiplicity of situations and people in any case that requires decision making and risk assessment.

This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market.

To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries.

The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events.

It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.

Este estudio analiza la volatilidad del mercado de renta fija de once países (Brasil, Rusia, India, China, Sudáfrica, Argentina, Chile, México, Estados Unidos, Alemania y Japón) de enero de 2000 a diciembre de 2011, mediante el examen de las tasas de interés interbancarias de cada mercado.

Para la volatilidad de los retornos de las tasas de interés, se utilizaron modelos de heteroscedasticidad condicional autorregresiva: ARCH, GARCH, EGARCH, TGARCH y PGARCH, y una combinación de estos con modelos ARIMA, comprobando cuáles de los procesos eran más eficientes para capturar la volatilidad de interés de cada uno de los países de la muestra.

Los resultados sugieren que para la mayoría de los mercados estudiados la volatilidad es mejor modelada por procesos GARCH asimétricos —en este caso el EGARCH— demostrando que las malas noticias conducen a un mayor incremento en la volatilidad de estos mercados que las buenas noticias. Además, las causas de una mayor volatilidad parecen estar más asociadas a eventos que ocurren internamente en cada país, como cambios en las políticas macroeconómicas, que los eventos externos generales.

Se espera que este estudio contribuya a un mejor entendimiento de la volatilidad de las tasas de interés y de los principales factores que afectan a este mercado.

Ingreso fijo, Volatilidad, Países emergentes, Modelos ARCH-GARCH

Artículo de investigación

A new multivariate heavy-tailed distribution is proposed as an extension of the univariate distribution of Politis (2004). The properties of the new distribution are discussed, as well as its effectiveness in modeling ARCH/GARCH residuals. A practical procedure for multi-parameter numerical maximum likelihood is also given, and a real data example is worked out.

The purpose of this paper is to empirically analyze volatility properties of the house price returns of Turkey and Istanbul, Ankara and Izmir provinces over the period of July 2007-June 2014.

The paper uses conditional variance models, namely, ARCH, GARCH and E-GARCH. As the supportive approach for the discussions, we also use correlation analysis and qualitative inputs.

Empirical findings suggest several points. First, city/country-level house price return volatility series display volatility clustering pattern and therefore volatilities in house price returns are time varying. Second, it seems that there were high (excess) and stable volatility periods during observation term. Third, a significant economic event may change country/city-level volatilities. In this context, the biggest and relatively persistent shock was the lagged negative shocks of global financial crisis. More importantly, short-lived political/economic shocks have not significant impacts on house price return volatilities in Turkey, Istanbul, Ankara and Izmir. Fourth, however, house price return volatilities differ across geographic areas, volatility series may show some co-movement pattern. Fifth, volatility comparison across cities reveal that Izmir shows more excess volatility cases, Ankara recorded the highest volatility point and Istanbul and national series show lower and insignificant volatilities.

The study uses maximum available data and focuses on some house price return volatility patterns. The first implication of the findings is that micro/macro dimensions of house price return volatilities should be carefully analyzed to forecast upside/downside risks of house price returns. Second, defined volatility clustering pattern implies that rate of return of housing investment may show specific patterns in some periods and volatile periods may result in some large losses in the returns. Third, model results generally suggest that however data constraint is a major problem, market participants should analyze regional idiosyncrasies during their decision-making in housing portfolio management. Fourth, because house prices are not sensitive to relatively less structural shocks, housing may represent long-term investment instrument if it provides satisfactory hedging from inflation.

The evidences and implications would be useful for housing market participants aiming to manage/use externalities of housing price movements. From a practical contribution perspective, the study provides a tool that will allow measuring first time of the return volatility patterns of house prices in Turkey and her three biggest provinces. Local level analysis for Istanbul, Ankara and Izmir provinces, as the globally fastest growing cities, would be found specifically interesting by international researchers and practitioner.

Over the past many years ago, lot of work has been completed by the researchers trying to understand the relationship between different factors and stock exchange prices. The author has tried to explain different factors that affect share prices. The purpose of this paper is to know about the impact of size, dividend, profitability, asset growth of 15 Pakistani banks on share price on the basis of previous behavior of all the variables with each other.

A sample of 15 banks has been selected from Karachi stock exchange for the period of 2008-2011, Arch-Garch and unit root cannot be applied to check the stationarity and volatility due to small sample size. The analysis utilized fixed effect regression model, the test includes regressing the dependent variable SP (share price) and independent variables size, DY (dividend yield), ROA (return on asset), and AG (asset growth).

Results show that “size” has a positive significant relationship with the share price while the other variables have insignificant relationship.

This paper helps in determination of the factors that affect share price fluctuations in banking sector of Pakistan. The similar affects can be observed in financial sector in other countries.

In recent years, stock exchanges have been increasingly integrating and merging their activities at a national and international scale. While consolidation is often driven by technological, legal and competitive changes, whether merger activities are efficient in terms of market microstructure remains unknown. Academic research to date has analyzed the causes behind these mergers primarily from the technological, legal and competitive perspective, whereas relatively little literature considers their impact on the exchange itself. The paper aims to consider the case of the Euronext merger to explain this topic by studying this merger and its effect on Euronext's market risk (measured by volatility).

The paper uses a standard General Auto‐regressive Conditional Heteroskedasticity (GARCH (1,1)) process to study the volatility of the underlying markets and use break methodology to highlight the merger effects. It also adds control samples to account for any change in volatility that could be caused by factors other than the merger event.

The results suggest that the Euronext merger did not affect the market risk. In particular, the paper finds no evidence that the integration onto the same platforms for trading and clearing had a significant effect on the volatility of the merging markets.

This study contributes to clarify business issues and to guide policy makers on exchange industrial organization.

The present paper further contributes to the ongoing discussion about the drawbacks and merits of horizontal exchange integration.

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:

The paper aims to study the effect of the unemployment rate and its volatility on crime in the USA. It proposes that not only the unemployment rate, but also its volatility affect the crime.

First, the volatility of the unemployment rate is calculated using ARCH models. Next, using the results from the first stage the ARDL approach to cointegration is used to examine the link between the unemployment rate and its volatility on the crime.

The cointegrated or long‐run relationships are found only for burglary and motor‐vehicle theft. The results indicate that the unemployment rate has a significant effect on burglary and motor‐vehicle theft only in the short run and the unemployment volatility has a negative effect on motor‐vehicle theft regardless of time span. However, it has a positive effect on burglary in the short run and no effect in the long run.

The effect of unemployment rate on crime is documented in the literature. However, to the best of our knowledge, this is the first paper that emphasizes the importance of unemployment rate volatility on the crime.

The purpose of this paper is to evaluate selected West African currencies/US dollar exchange rates for the evidence of volatility spillover. Specifically, the paper examines West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD, for any evidence of shock and volatility spillover.

The author employs multivariate GARCH (1,1)–BEKK model which enables the evaluation of the interaction within the volatility of two or more series because of its capability to detect volatility spillover among time series observations, as well as the persistence of volatility within each series.

The major findings of this study are as follows: there is evidence of volatility clustering in West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD. There is evidence of bi-directional shock and volatility spillover between the Nigerian naira and West African CFA franc/USD exchange rates, and uni-directional shock spillover from the Gambian dalasi to the West African CFA franc/USD exchange rates. There is, however, no evidence of exchange rate shock and volatility spillover between Nigerian naira and Gambian dalasi.

Although considerable literature exists on the volatility of exchange rate in West Africa and comparative analysis of exchange rates volatility in few countries of West Africa, there is absence of empirical studies on exchange rate volatility spillover among countries in the region. Since containing exchange rate volatility is one of the major objectives of monetary policy, understanding the nature and direction of exchange rate volatility spillover would propel formulation exchange rate policies that would minimise exchange rate uncertainty and entrench sustainable development. In addition, the nature of exchange rate volatility spillover between West African countries would provide basis for international traders and foreign portfolio investors to develop effective strategies for hedging against exchange rate shocks that are propagated across countries by designing appropriate risk management techniques.