A hybrid multivariate time series m odel for forecasting m eteorological data in peninsular malaysia

An extreme rainfall event, high temperature, haze, glacier melting, rises of sea level, and droughts are as a result of climate change. The impact of climate change may result to the devastation of the earth and life. For early preparations to face the challenges of climate change, a model that can forecast future weather variables is needed. There exist several weather models that forecast the future atmospheric data; however, the existing models which are not station-based models, hence will have an incomplete understanding of climate system of a particular case study area. To improve on the climatic modelling, this study developed a new model where the model used data collected from Alor Setar weather stations in Peninsular M alaysia by taking into consideration all the identified dynamic features of the variables. The model is an extension of multivariate time series method, namely vector autoregressive (VAR) model. Dynamic conditional correlation (DCC) model from generalised autoregressive conditional heteroscedasticity (GARCH) model was applied in this study since weather variable has high volatility and DCC model is able to capture the volatility of the model. However, because of the high persistence in the volatility, DCC model alone is not able to capture the structural changes in the volatility. To improve on the model, a joint model with hidden Markov model (HMM) is proposed whereby HM M method will consider the structural changes in the volatility that experienced high, moderate and low volatility. The findings presented that, due to neglected of structural change in volatility, the VAR multivariate time series with the hybrid of DCC model was not able to capture closely the volatility of the weather data. Nevertheless, the proposed joint model that uses the HM M to consider the structural changes in the volatility was able to capture the degree of persistence in the weather data. The out-sample forecasting accuracy gives less than ten percent of the mean absolute percentage error (MAPE) for the proposed joint model. Simulation study proves that the VAR-HMM- DCC proposed model has better result as compare to the hybrid of the conventional VAR-DCC model. The newly joint VAR-HMM-DCC model is the contribution that provides strategies for the future forecasting weather data

The nature and causes of the Norwegian interbank offered rate

The importance of interbank rates for unsecured funding has increased vastly the last decades with the expansion of nancial instruments. Today's interbank rates are arguably the most in uential benchmarks in pricing of assets and an important indicator on the state an economy. In the aftermath of the nancial crisis, the awareness of weaknesses of interbank rates surfaced. The awareness has led to a tightening of the regulations regarding the Norwegian Interbank O ered Rate (NIBOR). The purpose of this paper is to identify the nature of NIBOR in both a domestic and international context, and expand on NIBOR's ability to accurately re ect the lending cost between Norwegian prime banks. The rst part of the paper uses the Nelson-Siegel and Vasicek models to compare o ered rates against observable nancing cost using unsecured corporate bonds. NIBOR has historically been quoted higher than both STIBOR and EURIBOR, and we nd that Norwegian banks contributing to NIBOR and STIBOR face the same nancing costs as European banks contributing to EURIBOR. This implies that the di erences between interbank rates cannot be justi ed by higher nancing costs. When comparing the interbank rates to domestic nancing costs, we are unable to determine if banks contributing to NIBOR are more or less accurate in the Norwegian interbank market compared to other interbank markets where these banks are present. In the second part of the paper, we compare individual interest rate quotes to credit default swaps, and observe an inconsistent relationship between panel banks' quotes and their market priced risk over time. By applying a hidden markov model, we examine individual short term behavioral dynamics during the opening of the day, and preceding the xing. Our results indicate that interpretation of information varies across participants, which is a possible weakness of the governance structure.nhhma