Search results

Extensive studies have investigated the relation between risk and return in the stock and major asset markets, whereas little studies have been done for housing, particularly the Australian housing market. This study aims to determine the relationship between housing risk and housing return in Australia.

The analysis of this study involves two stages. The first stage is to estimate the presence of volatility clustering effects. Thereafter, the relation between risk and return in the Australian housing market is assessed by using a component generalised autoregressive conditional heteroscedasticity-in-mean (C-CARCH-M) model.

The empirical results show that there is a strong positive risk-return relationship in all Australian housing markets. Specifically, comparable results are also evident in all housing markets in various Australian capital cities, reflecting that Australian home buyers, in general, are risk reverse and require a premium for higher risk level. This could be attributed the unique characteristics of the Australian housing market. In addition, there is evidence to suggest that a stronger volatility clustering effect than previously documented in the daily case.

The findings enable more informed and practical investment decision-making regarding the relation between housing return and housing risk.

This paper is the first study to offer empirical evidence of the risk-return relationship in the Australian housing market. Besides, this is the first housing price volatility study that utilizes daily data.

We investigate whether or not the effects of the subprime financial crisis on 12 Asian economies are similar to those of the Asian financial crisis by examining volatility spillovers and time-varying correlation between the US and Asian stock markets. After pretesting volatility causality and constancy of correlation, we estimate an appropriate smooth-transition correlation VAR-GARCH model for each Asian stock market. First, the empirical evidence indicates stark differences in stock market linkages between the two crises. The volatility causality comes from the crises-originating country. Volatility in Asian stock markets Granger-caused volatility in the US market during the Asian crisis, whereas volatility in the US stock market Granger-caused volatility in Asian stock markets during the subprime crisis. Second, decreased correlations during the period of financial turmoil were observed, especially during the Asian financial crisis. Third, the estimated points of transition in the correlation are indicative of market participants’ awareness of the ensuing stock market crashes in July 1997 and in September 2008.

The purpose of this paper is to consider a monetary-jump model to measure the contribution of jumps to the total volatility of interest rates in the Brazilian interbank market and to assess the extent to which the central bank’s unanticipated monetary policy decisions are driving these jumps.

The authors use a sample of swap rates contracts with different maturities to estimate a mixture GARCH-jump model that disentangles two components of interest rate volatility: a GARCH-type specification that models conditional heteroskedasticity to account for the volatility during “normal” times and a Poisson process that models the occurrence of abrupt changes in interest rates.

The contribution of jumps to the total volatility is substantial, and monetary policy decisions partly explain the occurrence of those jumps. In particular, the authors find that the likelihood of a jump occurring during a meeting day of the Brazilian central bank’s monetary policy committee (COPOM) is higher in comparison to that of a non-meeting day.

The occurrence of jumps in the term structure of interest rates raises the question of the transmission mechanism of the monetary policy through the asset price channel as well as the relation between jumps and economic fundamentals.

Communication between the central bank and the market will affect expectations and asset values. If the central bank’s decisions generate fewer jumps, then the variance of the interest rate-linked asset values will also be reduced.

The paper employs a new approach to assess monetary policy surprises to a set of Brazilian interest rate data and relates the occurrence of jumps to the macroeconomic environment.

The purpose of this paper is to examine if the enactment of Sarbanes-Oxley (SOX) resulted in lower risk premium and return volatility in the US stock markets. The paper examines the two components of excess return (total risk premium) separately: the amount of volatility (risk) and the unit price of risk (risk premium).

The authors use a Component Generalized Autoregressive Conditional Heteroskedasticity approach to estimate the permanent and transitory component of share price volatility. The authors then use the predicted volatility to measure the unit price of risk and its changes due to the enactment of the SOX Act.

The results regarding excess returns indicate that the implementation of SOX had a positive effect on the market. A positive effect means a steady decrease in required excess rates of returns due to the implementation of SOX. The years leading up to the implementation of SOX are characterized by significant sources of uncertainty. Around the implementation of SOX, the authors observe a long-term reduction in return volatility (risk), and a temporary reduction in the unit price of risk. Subsequent to the implementation, investors gained confidence in the effectiveness of internal controls over the financial reporting process, which helped in reducing the information risk and, therefore, the risk premium.

The authors find that total risk premium decreased over extended periods. The authors conclude that the enactment of SOX helped in reducing the uncertainty in the US capital market resulting in a reduction of total risk premiums and hence the cost of capital.

The results have implications for policy makers, investors and researchers in general and those in the US markets in particular. The results are important because it allows policy makers and regulators to improve on how they design and implement accounting, market and finance regulations and reforms.

The study shows how financial markets react to regulations and the authors also provide information on investors’ reaction as firms adjust to changing regulations. The results of the study allows regulators to potentially use a more refined or targeted approach when introducing new regulations. It also allows investors to make informed investment decisions as they relate to risk premium requirements, which in turn may allow investors to allocate capital more efficiently.

There are many studies concerning the enactment of SOX but few, if any, existing studies examine the original intent of SOX: to calm the US equity markets and restore market confidence from a return volatility perspective. The results have implications for policy makers, investors and researchers in general and those in the US markets in particular. The results are important because it allows policy makers and regulators to improve on how they design and implement accounting, market and finance regulations and reforms.

The purpose of this paper is to assess the informational efficiency of S&P CNX Nifty index options in Indian securities market. The S&P CNX Nifty index is a leading stock index of India, consists of 50 most frequently traded securities listed on NSE. For the purpose, the study covers a period of six years from 4 June 2001 (the starting date for index options in India) to 30 June 2007.

The informational efficiency of implied volatilities (IVs) has been tested vis‐à‐vis select conditional volatilities models, namely, GARCH(1,1) and EGARCH(1,1). The tests have been carried out for “in‐the‐sample” as well as “out‐of‐the‐sample” forecast efficiency of implied volatilities.

The results of the study reveal that implied volatilities do not impound all the information available in the past returns; therefore, these are indicative of the violation of efficient market hypothesis in the case of S&P CNX Nifty index options market in India.

The finance managers, in Indian context, should rely on conditional volatility models (especially the EGARCH(1,1) model) compared to IV‐based forecasts to predict volatility for the horizon of one week. The stock exchanges and market regulator (SEBI) need to take certain initiatives in terms of extending the short‐selling facility and start trading of volatility index (VIX) to enhance the accuracy of IV‐based forecasts.

The paper addresses an issue which is still unexplored in the context of Indian securities market and in that sense makes an important contribution to literature on microstructure studies.

Apart from the well-known, high persistence of daily financial volatility data, there is also a short correlation structure that reverts to the mean in less than a month. We find this short correlation time scale in six different daily financial time series and use it to improve the short-term forecasts from generalized auto-regressive conditional heteroskedasticity (GARCH) models. We study different generalizations of GARCH that allow for several time scales. On our holding sample, none of the considered models can fully exploit the information contained in the short scale. Wavelet analysis shows a correlation between fluctuations on long and on short scales. Models accounting for this correlation as well as long-memory models for absolute returns appear to be promising.

Analyzing and forecasting reliability is increasingly important for enterprises. An accurate product reliability forecasting model cannot only learn and track a product's reliability and operational performance, but can also offer useful information that allows managers to take follow‐up actions to improve the product's quality and cost. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) model is already extensively used to analyze and forecast time series data. However, the GARCH model has not been used to analyze and forecast the failure data of repairable systems. Based on these concerns, this study proposes the GARCH model to analyze and forecast the field failure data of repairable systems.

This paper proposes the GARCH model to analyze and forecast the field failure data of repairable systems. Empirical results from electronic systems designed and manufactured by suppliers of the Chrysler Corporation are presented and discussed.

The proposed method can analyze and forecast failure data for repairable systems. Not only can this method analyze failure data volatility, it can also forecast the future failure data of repairable systems.

Advanced progress in the field of reliability prediction estimation can benefit engineers or management authorities by providing important decision support tools in which the prediction accuracy suggests financial and business outcomes as well as other outcome application results.

Guar Seed crop is ruling the Indian International business mainly due to its application as a drilling fluid in shale energy industry concentrated in the USA. One of the allegations against futures market is its possible role in increasing the volatility of underlying physical market prices. Suspension of guar seed futures contract in 2012 at National Commodity Derivatives Exchange of India (NCDEX)-India, has reignited the controversy and raised an alarm bell to peek into obscure world of Indian commodity derivatives market. Against the backdrop of fiasco in guar futures trading, the purpose of this paper is to investigate whether sudden surge in futures trading volume leads to increase in the volatility of spot market prices.

Guar seed spot returns volatility is modeled as a GARCH (1, 1) process. Futures trading volume and open interest are segregated into expected and unexpected components. The data are analyzed from 2004 to 2011 using Augmented GARCH model to study the contemporaneous relationship between spot volatility and unexpected futures trading activity and Granger Causality test for examining the dynamic relationship between them and ascertaining causality.

Augmented GARCH model reports positive relationship between unexpected futures trading volume (UTV) and spot returns volatility, and, Granger Causality flows from UTV to spot volatility. Therefore, when the level of futures trading volume increases unexpectedly, the volatility of spot prices increases pointing toward the destabilizing impact of futures trading. However, hedger’s activity, represented by open interest is not seen to have any causal/destabilizing impact on spot price volatility of guar seed.

The study provides empirical evidence to support the concern of regulators, genuine hedgers and other traders about the presence of excessive speculation and market manipulations perpetrated through futures market that is disturbing the underlying physical market instead of strengthening it by aiding in price discovery and risk mitigation.

There are very few studies which have empirically investigated the temporal relation between volume and volatility in Indian agricultural commodity markets. With guar seed as a special case the present study investigates statistically the impact of futures trading on spot price volatility. In light of the findings of the study, the curb imposed on guar seed futures trading in 2012 was justified.

This paper investigates the dependence structure and market risk of the currency exchange rate portfolio from the Malaysian ringgit perspective.

The marginal return of the five major exchange rates series, i.e. United States dollar (USD), Japanese yen (JPY), Singapore dollar (SGD), Thai baht (THB) and Chinese Yuan Renminbi (CNY) are modelled by the Bayesian generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model with Student's t innovations. In addition, five different copulas, such as Gumbel, Clayton, Frank, Gaussian and Student's t, are applied for modelling the joint distribution for examining the dependence structure of the five currencies. Moreover, the portfolio risk is measured by Value at Risk (VaR) that considers the extreme events through the extreme value theory (EVT).

The finding shows that Gumbel and Student's t are the best-fitted Archimedean and elliptical copulas, for the five currencies. The dependence structure is asymmetric and heavy tailed.

The findings of this paper have important implications for diversification decision and hedging problems for investors who involving in foreign currencies. The authors found that the portfolio is diversified with the consideration of extreme events. Therefore, investors who are holding an individual currency with VaR higher than the portfolio may consider adding other currencies used in this paper for hedging.

This is the first paper estimating VaR of a currency exchange rate portfolio using a combination of Bayesian GARCH model, EVT and copula theory. Moreover, the VaR of the currency exchange rate portfolio can be used as a benchmark of the currency exchange market risk.