Forecasting category sales and market share for wireless telephone subscribers: a combined approach

The ability to forecast market share remains a challenge for many managers especially in dynamic markets, such as the telecommunications sector. In order to accommodate the unique dynamic characteristics of the telecommunications market, we use a multi-component model, called MSHARE. Our method involves a two-phase process. The first phase consists of three components: a projection method, a ring down survey methodology and a purchase intentions survey. The predictions from these components are combined to forecast category sales for the wireless subscribers market. In the second phase, market shares for the various brands are generated using the forecast of the number of subscribers that are obtained in Phase 1 and the share predictions from the ring down methodology. The proposed methodology produces the minimum Relative Absolute Error for each market as compared to the forecasts from each individual component in the first phase. The value of the proposed model is illustrated by its application to a real world scenario. The managerial implications of the proposed model are also discussed.     

Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method

Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of multiple generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier generations. In the second stage of the forecast methodology, the annual shipment of products of the specified generation will be predicted by deriving annual fuzzy regression lines for each generation, based upon the historical data on the earlier generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict multiple generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

Initial Margin Requirements, Volatility, and the Individual Investor: Insights from Japan

Initial margin requirements represent: (1) a cost impediment to the wealth constrained investor and (2) a potential way of mitigating excessive volatility. However, prior empirical research finds that margins are not an effective tool in reducing volatility. We consider the possibility that margins primarily affect certain stocks and investors. Specifically, we test whether margins affect individuals who, as a group, we believe to be the investors most affected when margin requirements change. Our initial empirical tests, however, do not support this contention.     

Likelihood-based specification analysis of continuous-time models of the short-term interest rate

An extensive collection of continuous-time models of the short-term interest rate is evaluated over data sets that have appeared previously in the literature. The analysis, which uses the simulated maximum likelihood procedure proposed by Durham and Gallant (2002), provides new insights regarding several previously unresolved questions. For single factor models, I find that the volatility, not the drift, is the critical component in model specification. Allowing for additional flexibility beyond a constant term in the drift provides negligible benefit. While constant drift would appear to imply that the short rate is nonstationary, in fact, stationarity is volatility-induced. The simple constant elasticity of volatility model fits weekly observations of the three-month Treasury bill rate remarkably well but is easily rejected when compared with more flexible volatility specifications over daily data. The methodology of Durham and Gallant can also be used to estimate stochastic volatility models. While adding the latent volatility component provides a large improvement in the likelihood for the physical process, it does little to improve bond-pricing performance.     

Forecasting economic time series with unconditional time-varying variance

The classical forecasting theory of stationary time series exploits the second-order structure (variance, autocovariance, and spectral density) of an observed process in order to construct some prediction intervals. However, some economic time series show a time-varying unconditional second-order structure. This article focuses on a simple and meaningful model allowing this nonstationary behaviour. We show that this model satisfactorily explains the nonstationary behaviour of several economic data sets, among which are the U.S. stock returns and exchange rates. The question of how to forecast these processes is addressed and evaluated on the data sets.     

How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?

We investigate how economic policy uncertainty (EPU) and geopolitical risks (GPR) impact Bitcoin volatility with respect to factors related to type and nationality of uncertainty, investigated period, relationship horizon and extreme conditions. Applying ARDL model and quantile regression for monthly data from August 2010 to September 2021, we reveal that June 2014 corresponds to a key date that marks a reversal in the investigated relationship. Furthermore, we show that the relationship between uncertainty and bitcoin volatility changes according to different factors. US uncertainty has short run effects on Bitcoin volatility, while China’s uncertainty has rather long run effects. Moreover, Bitcoin volatility responds in the same manner to US EPU and GPR, while, it responds differently to China's EPU and GPR. In extreme quantiles, we find that Bitcoin hedges against US EPU and GPR. Further, Bitcoin hedges against either individual or joint effects of US uncertainty, but not both.     

我国股权分置改革下权证定价的实证研究

权证在性质上也是一种期权，但并不是一种单纯的期权，定价要比普通期权复杂。我国在近十年后，重新推出了权证。在一系列假设条件下，利用Black—Scholes和二叉树模型，推导出权证定价公式。最后采用我国发行的权证及标的股票的收盘价格资料，计算了公司权益的波动率，从而利用已推导的公式对权证理论价值进行了实证分析。并同其实际市场价进行了比较，为权证的投资决策提供了参考依据。     

基于外汇期权方法的汇率预测研究

文章利用2004年芝加哥商品交易所的美元-欧元期货期权的信息,分析了其隐含偏度和隐含波动率在预测短期汇率中的效力。结果发现,隐含偏度、隐含波动率的偏度与每日汇率变化率有紧密的联系。