Forecasting stock price with the residual income model

The main purpose of this paper is to demonstrate a method to forecast stock price using analyst earnings forecasts as essential signals of firm valuation. The demonstrated method is based on the residual income model (RIM), with adjustment for autocorrelation. Over the past decade, the RIM has been widely accepted as a theoretical framework for equity valuation based on fundamental information from financial reports. This paper shows how to implement the RIM for forecasting and how to address autocorrelation to improve forecast accuracy. Overall, this paper provides a method to forecast stock price that blends fundamental data with mechanical analyses of past time series.     

Forecasting time-varying covariance with a range-based dynamic conditional correlation model

This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances. As to the empirical study, we use the S&P 500 stock index and the 10-year treasury bond futures to examine both in-sample and out-of-sample results for six models, including MA100, EWMA_, CCC_, BEKK, return-based DCC, and range-based DCC. Of all the models considered, the range-based DCC model is largely supported in estimating and forecasting the covariance matrices.     

The price of gold and the exchange rate

This paper examines the theoretical relationship between the major exchange rates and the prices of internationally-traded commodities. In the empirical section, the case of gold is analyzed using forecast error data. Among other things, it is found that, since the dissolution of the Bretton Woods International monetary system, floating exchange rates among the major currencies have been a major source of price instability in the world gold market and, as the world gold market is dominated by the European currency bloc, appreciations or depreciations of European currencies have strong effects on the price of gold in other currencies.     

Forecasting financial crises and contagion in Asia using dynamic factor analysis

In this paper we use principal components analysis to obtain vulnerability indicators able to predict financial turmoil. Probit modelling through principal components and also stochastic simulation of a Dynamic Factor model are used to produce the corresponding probability forecasts regarding the currency crisis events affecting a number of East Asian countries during the 1997–1998 period. The principal components model improves upon a number of competing models, in terms of out-of-sample forecasting performance.     

Forecasting Value-at-Risk using high frequency data: The realized range model

Current studies on financial market risk measures usually use daily returns based on GARCH type models. This paper models realized range using intraday high frequency data based on CARR framework and apply it to VaR forecasting. Kupiec LR test and dynamic quantile test are used to compare the performance of VaR forecasting of realized range model with another intraday realized volatility model and daily GARCH type models. Empirical results of Chinese Stock Indices show that realized range model performs the same with realized volatility model, which performs much better than daily models.     

我国黄金储备动态管理研究

按照传统的黄金储备管理方法,黄金储备是不流动的,如要动用黄金必须经有关方面许可。这种固定的、封闭的管理方法,使央行承担了黄金储备的机会成本和金价下跌的风险。为了改变这种管理方式,我们必须创建新型的黄金储备管理模式,实施黄金储备流动性管理和保值增值管理,提高黄金储备的收益率。保障黄金储备在外汇储备中的合理规模,增强国家的金融安全。这些建议不仅具有可行性和可操作性,而且可以提供给有关部门参考。     

The sensitivity of Australian industry equity returns to a gold price factor

This paper investigates the extra-market sensitivity of Australian industry equity returns to a gold price factor over the period 1975 to 1994. We find, over the full sample period, that there has been a widespread sensitivity of Australian industry returns to gold price returns, over and above market returns. The sensitivity is found to be of positive sign for resource and mining sector industries, whereas it is of negative sign for the industrials sector. Further, there appears to be a change in importance of the gold price factor over time, as reflected by a comparison of subperiod gold price sensitivity estimates     

Forecasting the return distribution using high-frequency volatility measures

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1, 1) model when the 30-year Treasury bond futures return is considered.     

Asset price prediction using seasonal decomposition

We propose a new forecasting procedure for asset prices using seasonal decomposition methods (SD hereafter), e.g., SABL and X-11. Such SD's are based on moving average methods, and they are thus easy to use and are capable of computing the seasonal pattern that changes over time. A SD typically decomposes a series intoT (trend),S (seasonal component), andR (residual or sometimes referred to as the irregular component). We use an ARIMA model onR to obtain its forecast. TheS component is forecasted by an extrapolation taking into account its changing pattern within the sample period. We propose to set up some scenarios on theT component by examining its possibly nonlinear and nonstationary behavior, and in the paper we suggest one possible way for this. Suppose that the forecasting horizon is relatively short compared toT's several cycles just before the end of the sample. Then we may safely extrapolateT linearly into the forecasting period. LinearizingT in such a case, makes sense. As to the slope of the linear line, we suggest the average rate of change of the most recent upward phase of a cycle to be used if we needed an optimistic scenario. Obviously, that of the downward phase may be used for constructing a pessimistic scenario, and that of one entire cycle is suitable for ‘average’ scenario. Once the forecasted values of the three components are obtained, we may put them back to make predictions on the original series based upon various different scenarios. In addition to proposing a new prediction method, we looked into the following issues, among others, in the paper: (1) on what sort of asset prices would our forecasting method work well? (2) Any significant differences if we used X-11 instead of SABL?     

A model of the classical gold standard with depletion

The operation and properties of the classical gold standard are well recognized. However, one aspect that has not been dealt with is that gold has the characteristics of a durable, but depletable resource. In this paper, we compare the simple classical model of the gold standard with a model of the gold standard that incorporates the durable, depletable nature of gold. Using numerical simulation techniques, we demonstrate an inescapable tendency to long-run deflation when account is taken of the resource constraint. These results are consistent, with and without technological progress and variable real rates of return.     

A liquidity-based model for asset price bubbles

We provide a new liquidity-based model for financial asset price bubbles that explains bubble formation and bubble bursting. The martingale approach to modeling price bubbles assumes that the asset's market price process is exogenous and the fundamental price, the expected future cash flows under a martingale measure, is endogenous. In contrast, we define the asset's fundamental price process exogenously and asset price bubbles are endogenously determined by market trading activity. This enables us to generate a model that explains both bubble formation and bubble bursting. In our model, the quantity impact of trading activity on the fundamental price process—liquidity risk—is what generates price bubbles. We study the conditions under which asset price bubbles are consistent with no arbitrage opportunities and we relate our definition of the fundamental price process to the classical definition.     

GARCH-stable as a model of futures price movements

A GARCH-stable process is tested as a model of the distribution of daily futures prices. The GARCH-stable process cannot be rejected as a model of 12 of the 37 price series considered. The evidence regarding stable distributions as a model of futures prices is not as unfavorable as suggested by some past research. The remaining rejections of the GARCH-stable model could be due to the inappropriateness of the stable distribution assumption or to other factors such as ignoring day-of-the-week effects and price limits.     

Forecasting variance using stochastic volatility and GARCH

This paper estimates the conditional variance of daily Swedish OMX-index returns with stochastic volatility (SV) models and GARCH models and evaluates the in-sample performance as well as the out-of-sample forecasting ability of the models. Asymmetric as well as weekend/holiday effects are allowed for in the variance, and the assumption that errors are Gaussian is released. Evidence is found of a leverage effect and of higher variance during weekends. In both in-sample and out-of-sample comparisons SV models outperform GARCH models. However, while asymmetry, weekend/holiday effects and non-Gaussian errors are important for the in-sample fit, it is found that these factors do not contribute to enhancing the forecasting ability of the SV models.     

Forecasting foreign exchange rates using idiosyncratic volatility

Average idiosyncratic stock volatility forecasts the bilateral exchange rates of the US dollar against major foreign currencies in and out of sample. The US dollar tends to appreciate after an increase in US idiosyncratic volatility. Similarly, ceteris paribus, German and Japanese idiosyncratic volatilities positively and significantly correlate with future US dollar prices of the Deutsche mark and the Japanese yen, respectively. Our results suggest that exchange rates are predictable.     

Hawkes model for price and trades high-frequency dynamics

We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by four kernels associated with, respectively, the trade arrival self-excitation, the price changes mean reversion, the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market price microstructure (including random time arrival of price moves, discrete price grid, high-frequency mean reversion, correlation functions behaviour at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be empirically estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

Forecasting realised volatility using ARFIMA and HAR models

Recent literature provides mixed empirical evidence with respect to the forecasting performance of ARFIMA and HAR models. This paper compares the forecasting performance of both models using high frequency data of 100 stocks representing 10 business sectors for the period 2000-2010. We allow for different sectors, changing market conditions, variation in the sampling frequency and forecasting horizons. For the overall sample and using the 300 sec sampling frequency, the forecasting performance of both models is indistinguishable. However, differences arise under different market regimes, forecasting horizons and sampling frequencies. ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons. Variations in forecasting performance could also be explained using differences in the levels of persistence underlying each model.     

Investigation of the costly-arbitrage model of price formation around the ex-dividend day in Norway

We estimate the costly-arbitrage model of Boyd and Jagannathan [Boyd, John, and Jagannathan, Ravi, 1994, Ex-Dividend Price Behavior of Common Stocks, Review of Financial Studies 7, 711–741.] using Norwegian stock market data. Taxable distributions take place at two separate dates, one that entails the distribution of an imputation-tax credit and another the distribution of the cash dividend. We find that the costly-arbitrage model is consistent with observed stock returns around the ex-dividend day, but the model cannot explain the return patterns around the distribution of the tax credit. We conclude that uncertainty about the cash flows prevents arbitrage.     

Saddlepoint approximations to option price in a regime-switching model

In this paper we consider the saddlepoint approximation for the valuation of a European-style call option in a Markovian, regime-switching, Black–Scholes–Merton economy, where the price process of an underlying risky asset is assumed to follow a Markov-modulated geometric Brownian motion. The standard option pricing procedure under this model becomes problematic as the occupation time of chains for a given state cannot be evaluated easily. In the case of two-state Markov chains, we present an explicit analytic formula of the cumulant generating function (CGF). When the process has more than two states, an approximate formula of the CGF is provided. We adopt a splitting method to reduce the complexity of computing the matrix exponential function. Then we use these CGFs to develop the use of the saddlepoint approximations. The numerical results show that the saddlepoint approximation is an efficient and reliable approach for option pricing under a multi-state regime-switching model.