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The purpose of this paper is to propose two hybrid forecasting models which integrate available ones. A hybrid contaminated normal distribution (CND) model accurately reflects the non‐normal features of monthly S&P 500 index returns, and a hybrid GARCH model captures a serial correlation with respect to volatility. The hybrid GARCH model potentially enables financial institutions to evaluate long‐term investment risks in the S&P 500 index more accurately than current models.

The probability distribution of an expected investment outcome is generated with a Monte Carlo simulation. A taller peak and fatter tails (kurtosis), which the probability distribution of monthly S&P 500 index returns contains, is produced by integrating a CND model and a bootstrapping model. The serial correlation of volatilities is simulated by applying a GARCH model.

The hybrid CND model can simulate the non‐normality of monthly S&P 500 index returns, while avoiding the influence of discrete observations. The hybrid GARCH model, by contrast, can simulate the serial correlation of S&P 500 index volatilities, while generating fatter tails. Long‐term investment risks in the S&P 500 index are affected by the serial correlation of volatilities, not the non‐normality of returns.

The hybrid models are applied only to the S&P 500 index. Cross‐sectional correlations among different asset groups are not examined.

The proposed hybrid models are unique because they combine available ones with a decision tree algorithm. In addition, the paper clearly explains the strengths and weaknesses of existing forecasting models.

Recognising that different property sectors have distinct risk-return characteristics, this paper assesses whether changes in the level and volatility of short- and long-term interest rates differentially affected excess returns of sector-specific Real Estate Investment Trusts (REITs) in the Pacific Rim region between July 2006 and December 2018. The strategic property risk management implications for sector-specific REITs are also identified.

Daily excess returns between July 2006 and December 2018 are used to analyse the sensitivity in the level and volatility of interest rates for REITs among office, retail, industrial, residential and specialty REITs across the USA, Japan, Australia and Singapore. The generalised autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology is employed to assess the linkage between interest rates and excess returns of sector-specific REITs.

Compared with diversified REITs, sector-specific REITs were less sensitive to short- and long-term interest rate changes across the USA, Japan, Australia and Singapore between July 2006 and December 2018. Of sector-specific REITs, retail and residential REITs were susceptible to interest rate movements over the full study period. On the other hand, office and specialty REITs were generally less sensitive to changes in the level and volatility of short- and long-term interest rate series across all markets in the Pacific Rim region. However, the interest rate sensitivity of industrial REITs was somewhat mixed. This sector was sensitive to interest rate movements, but no comparable evidence was found since the onset of GFC.

The insignificant exposure to interest rate risk of sector-specific REITs may imply that they have a stronger interest rate risk aversion and greater hedging benefits than their diversified counterparts, particularly for office and specialty REITs. The results support the existence of REIT specialisation value in the Pacific Rim region from the interest rate risk management perspective. This is particularly valuable to international property investors constructing and managing portfolios with REITs in the region. Property investors are advised to be aware of the disparities in the magnitude and direction of sensitivity to the interest rate level and volatility of REITs across different property sectors and various markets in the Pacific Rim region. This study is expected to enhance property investors' understanding of interest rate risk management for different property types of REITs in local, regional and international investment portfolios.

The study is the first to assess the interest rate sensitivity of REITs across different property sectors and various markets in the Pacific Rim region. More importantly, this is the first paper to offer empirical evidence on the existence of specialisation value in the Pacific Rim REIT markets from the aspect of interest rate sensitivity. This research may enhance property investors' understanding of the varying interest rate sensitivity of different property types of REITs across the USA, Japan, Australia and Singapore.

Using intraday prices for the S&P 500 and Nikkei Stock Average stock indexes and aggregate trading volume for the New York and Tokyo Stock Exchanges, we show how short-run comovements between national stock market returns vary over time in a way related to the trading volume and liquidity in those markets. We frame our analysis in the context of the heterogeneous-agent models of trading developed by Campbell, Grossman and Wang (1993) and Blume, Easley and O’Hara (1994) and Wang (1994) which predict that trading volume acts as a signal of the information content of a given price move. While we find that there exists significant short-run dependence in returns and volatility between Japan and the U.S., we offer new evidence that these return “spillovers” are sensitive to interactions with trading volume in those markets. The cross-market effects with volume are revealed in both close-to-open and open-to-close returns and often exhibit non-linear patterns that are not predicted by theory.

The purpose of this paper is to examine cross-correlation in stock returns of SAARC countries, conditional and unconditional volatility of stock markets and to test efficient market hypothesis (EMH).

Stock indices of India, Bangladesh, Sri Lanka and Pakistan are considered to serve as proxy for stock markets in SAARC countries. Data consist of daily closing price of stock indices from 2000 to 2011. Since preliminary testing indicated presence of serial autocorrelation and volatility clustering, family of GARCH models is selected.

Results indicate presence of serial autocorrelation in stock market returns, implying dependence of current stock prices on stock prices of previous times and leads to rejection of EMH. Significant relationship between stock market returns and unconditional volatility indicates investors’ expectation of extra risk premium for exposing their portfolios to unexpected variations in stock markets. Cross-correlation revealed level of integration of South Asian economies with global market to be high.

Business cycles and other macroeconomic developments affect most companies and lead to unexplained relationships. The paper finds stock markets to exist at different levels of development as economic liberalization started at different points of time in SAARC countries.

Correlation between stock indices of SAARC economies are found to be low which is in line with intra-regional trade being one of lowest as compared to other regional groups. Results point towards greater need for economic cooperation and integration between SAARC countries. Greater financial integration leads to development of markets and institutions, effective price discovery, higher savings and greater economic progress.

The paper focuses on EMH and risk return relation for SAARC nations.

This study uses a two‐step GARCH‐M procedure to observe mean‐return and volatility transmissions between Latin American markets and to Latin America from external markets during the period 1993–2000. The results indicate that mean‐return transmissions are common both within region and from external markets. The volatility transmission results are consistent with contagion theory and indicate that traders use both domestic news events as well as information contained by volatility in other markets in their information set.

The purpose of this paper is to find and describe some stylized facts for foreign exchange and stock market returns, which are explored using statistical methods.

Formal statistics for testing presence of autocorrelation, asymmetry, and other deviations from normality are applied. Dynamic correlations and different kernel estimations and approximations to the empirical distributions are also under scrutiny. Furthermore, dynamic analysis of mean, standard deviation, skewness and kurtosis are also performed to evaluate time‐varying properties in return distributions.

The findings include: different types of non‐normality in both markets, fat tails, excess furtosis, return clustering and unconditional time‐varying moments. Identifiable volatility cycles in both forex and stock markets are associated to common macro financial uncertainty events.

The paper is the first work of this type in Peru.

The biggest enlargement of the European Union (EU) took place in May 2004 when 10 new countries (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) joined the union, increasing the number of member states from 15 to 25. Of these newcomers, eight are former Eastern European countries with transition to Western-type market economies. These emerging markets provide increasingly growing investment opportunities and international diversification options for fund managers and individual investors. Well-known features of emerging equity markets are high returns, high volatility, and low correlation with developed markets. Bekaert and Harvey (2002) find that this correlation is on average increasing, particularly for those emerging markets that have liberalised their financial markets. Mateus (2004) finds similar results with EU access countries for recent years. Additional features of emerging markets are sparse data, low liquidity, and large price changes due to political changes or market crashes (e.g. Hwang & Pedersen, 2004).

The concept of volatility transmission and co-movement has witnessed a resurgence in the international finance literature in recent years after the black swan events which gave evidence of financial market linkages. The purpose of this paper is to examine the dynamic sources of volatility transmission in the foreign exchange market in recent financial market integration in Africa.

A conceptual framework was adapted from the extant literature and was used as the basis of modeling exchange rate volatility transmission. This paper adopts a quantitative research approach and opts for augmented DCC model to empirically unearth the sources of exchange rate volatility transmission.

The key findings of the study are that, the African market is more prone to shock from outside than in the region. Macroeconomic news surprises influence volatility transmission and co-movements. Robust support is found for trade balance, interest rate and gross domestic product. These findings clearly demonstrate the low level of financial development and challenges that sometimes exist in exchange rate-policy implementation by policy makers.

Interested academics and practitioners working in the area might incorporate bilateral investment into the model of exchange rate correlation in future research.

Unilaterally considering exchange rate volatility transmission and subsequent augmentation of the DCC model, this study makes a modest contribution to the examination of exchange rate correlations in Africa. This study makes an important contribution in not only addressing this imbalance, but more importantly improving the relative literature on exchange rate volatility transmission.

The purpose of this paper is to study the variance risk premium in corn and soybean markets, where the variance risk premium is defined as the difference between the historical realized variance and the corresponding risk-neutral expected variance.

The authors compute variance risk premiums using historical derivatives data. The authors use regression analysis and time series econometrics methods, including EGARCH and the Kalman filter, to analyze variance risk premiums.

There are moderate commonalities in variance within the agricultural sector, but fairly weak commonalities between the agricultural and the equity sectors. Corn and soybean variance risk premia in dollar terms are time-varying and correlated with the risk-neutral expected variance. In contrast, agricultural commodity variance risk premia in log return terms are more likely to be constant and less correlated with the log risk-neutral expected variance. Variance and price (return) risk premia in agricultural markets are weakly correlated, and the correlation depends on the sign of the returns in the underlying commodity.

Commodity variance (i.e. volatility) risk cannot be hedged using futures markets. The results have practical implications for US crop insurance programs because the implied volatilities from the relevant options markets are used to estimate the price volatility factors used to generate premia for revenue insurance products such as “Revenue Protection” and “Revenue Protection with Harvest Price Exclusion.” The variance risk premia found implies that revenue insurance premia are overpriced.

The empirical results suggest that the implied volatilities in corn and soybean futures market overestimate true expected volatility by approximately 15 percent. This has implications for derivative products, such as revenue insurance, that use these implied volatilities to calculate fair premia.

Motivated by recent evidence that securitized real estate returns exhibit higher levels of predictability than stock market returns and that feedback trading (FT) can induce returns autocorrelation and market volatility, the purpose of this study is to examine the impact of FT strategies on long-term market volatility of eight international real estate markets (UK, Germany, France, Italy, Sweden, Australia, Japan and Hong Kong).

Assuming that the return autocorrelation may vary over time and the impact of positive feedback trading (PFT) or negative feedback trading (NFT) could be a function of return volatility, the authors use a combination of a FT model and a fractionally integrated Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model.

The results are mixed, revealing that both PFT and NFT strategies persist. Specifically, the authors detect PFT in the real estate markets of France, Hong Kong and Italy as opposed to the real estate markets of Australia, Germany, Japan and Sweden where NFT was present. A noteworthy exception is the UK real estate market, with important and rational FT strategies to sustain. With respect to the long-term volatility persistence, this seems to capture the mean reversion of real estate returns in the UK and Hong Kong markets. In general, the results are not consistent with those reported in previous studies because NFT dominates PFT in the majority of real estate markets under consideration.

The main contribution of this study is the investigation of the link between short-term PFT or NFT and long-term volatility in eight international real estate markets, symmetrically. Particular attention has been given to the link between short-term FT and long-term volatility, by means of a fractionally integrated GARCH approach, a symmetric one. Moreover, investigating the relationship between returns’ volatility and investors’ strategies based on FT entails significant implications because real estate assets offer a good alternative investment for many investors and speculators.