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 oil price realized volatility using information channels from other asset classes

Motivated from Ross (1989) who maintains that asset volatilities are synonymous to the information flow, we claim that cross-market volatility transmission effects are synonymous to cross-market information flows or “information channels” from one market to another. Based on this assertion we assess whether cross-market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting. We concentrate on realized volatilities derived from the intra-day prices of the Brent crude oil and four different asset classes (Stocks, Forex, Commodities and Macro), which represent the different “information channels” by which oil price volatility is impacted from. We employ a HAR framework and estimate forecasts for 1-day to 66-days ahead. Our findings provide strong evidence that the use of the different “information channels” enhances the predictive accuracy of oil price realized volatility at all forecasting horizons. Numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results.     

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.     

Gold mining companies and the price of gold

This paper studies the exposure of Australian gold mining firms to changes in the gold price. We use a theoretical framework to formulate testable hypotheses regarding the gold exposure of gold mining firms. The empirical analysis based on all gold mining firms in the S&;P/ASX All Ordinaries Gold Index for the period from January 1980 to December 2010 finds that the average gold beta is around one but varies significantly through time. The relatively low average gold beta is attributed to the hedging and diversification of gold mining firms. We further find an asymmetric effect in gold betas, i.e. the gold exposure increases with positive gold price changes and decreases with negative gold price changes consistent with gold mining companies exercising real options on gold.     

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.     

The effects of uncertainty measures on the price of gold

This paper analyzes the determinants of the price of gold with a special focus on four uncertainty measures (namely, the volatility (VIX), skewness (SKEW), global economic policy uncertainty (EPU), and partisan conflict (PC) indexes). The nonlinear Autoregressive-distributed Lag (ARDL) model is used to investigate the asymmetric effect of uncertainty measures on gold prices. The results show that rising economic policy uncertainty contributes to increases in the price of gold. By contrast, gold prices are less likely to fall when economic policy conditions are improved.     

Forecasting gold volatility with geopolitical risk indices

This paper tries to forecast gold volatility with multiple country-specific (GPR) indices and compares the role of combined prediction models and dimension reduction methods regarding the improvement of gold volatility prediction accuracy. For this purpose, GARCH-MIDAS model’s several extensions are used. We find firstly that most country-specific GPR indices have driving effects on gold volatility, and it makes sense to take forecast information from multiple country-specific GPR indices into account when forecasting gold volatility. The out-of-sample empirical results also indicate that the dimension reduction methods yield better predictions compared to the combined prediction models. In addition, dimension reduction technologies have excellent forecasting performance mainly during low gold volatility periods. Finally, our empirical findings are robust after changing the evaluation method, model settings, in-sample length and gold market.     

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。     

金价波动的原因、前景与中国选择

金融危机以来,黄金一路高走。该文从布雷顿森林崩溃后国际货币体系与黄金价格走势的关系分析中,认为国际货币体系的不确定性是黄金狂涨的宏观背景,而实物投资需求增加和开采商去套期保值化、美元汇率走低和通货膨胀预期、发达国家惜售黄金储备而新兴国家增持黄金储备以及投机气氛甚浓等,是支持黄金上涨的具体原因。在此基础上,进一步分析认为黄金价格的总体趋势不会改变,但短期调整仍会继续。相应地,中国应有长期增加黄金储备的战略,但在具体时机的选择上需要谨慎。     

A specie-flow model of the gold standard

An international gold standard model is presented, focusing on the relatively short-run movements of prices, gold stocks, and money. The paper criticizes the common assumption of the gold standard as an ideal, automatically adjusting system, and allows discretionary policy to be a potentially important short-run factor.     

The intrinsic value of gold: An exchange rate-free price index

In this paper, we propose a gold price index that enables market participants to separate the change in the ‘intrinsic’ value of gold from changes in global exchange rates. The index is a geometrically weighted average of the price of gold denominated in different currencies, with weights that are proportional to the market power of each country in the global gold market. Market power is defined as the impact that a change in a country’s exchange rate has on the price of gold expressed in other currencies. We use principal components analysis to reduce the set of global exchange rates to four currency ‘blocs’ representing the U.S. dollar, the euro, the commodity currencies and the Asian currencies, respectively. We estimate the weight of each currency bloc in the index in an error correction framework using a broad set of variables to control for the unobserved intrinsic value. We show that the resulting index is less volatile than the USD price of gold and, in contrast with the USD price of gold, has a strong negative relationship with global equities and a strong positive relationship with the VIX index, both of which underline the role of gold as a safe haven asset.     

A price dynamic equilibrium model with trading volume weights based on a price-volume probability wave differential equation

Guided by a price-volume probability wave differential equation in a new mathematical method, we study intraday market dynamic equilibrium in stock market. We select intraday cumulative trading volume distribution over a price range as individual mental representation and determine a price equilibrium point by the maximum volume utility price. We propose the hypothesis that a stock price can deviate away from the equilibrium point in momentum and restore to it in reversal, and the volume distribution embodies market dynamic equilibrium. Then, we examine it by a set of explicit price dynamic equilibrium models with trading volume weights from the differential equation against a large number of the price-volume distribution using tick-by-tick high frequency data in Chinese stock market in 2019. It holds true. We can infer that the theory is applied for a broader scope because it embraces core mathematical components in expected utility theory, prospect theory, and reflexivity theory.     

How do macroeconomic news surprises affect round-the-clock price discovery of gold?

We examine round-the-clock international price discovery of gold among the major gold markets—New York, London and Shanghai during news-intensive and no-news time zones using one-minute data. Using GMM based parallel price discovery measure, we find global leadership of the US as New York gold futures lead across five time zones with 56% information share. New York/London (Nylon) timezone (51%) is the most informative trading session in sequential price discovery for all markets in 24-h. Our aggregate and disaggregate news analysis reveals that the US news surprises have a substantial and positive impact on its price discovery leadership while Eurozone news surprises have a negative impact and Chinese news have negligible impact. Using least absolute shrinkage and selection operator (LASSO) regression, we find scheduled news with a large surprise index has a significant yet asymmetric impact as negative news triggers a strong reaction. The impact of news surprise is state-dependent and display sign-reversals during extreme uncertainty, adverse macroeconomic conditions and abnormal investor behaviour.     

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.     

Exchange market pressures during the financial crisis: A Bayesian model averaging evidence

In this paper, we examine whether pre-crisis leading indicators help explain pressures on the exchange rate (and its volatility) during the global financial crisis. We use a unique data set that covers 149 countries and 58 indicators, and estimation techniques that are robust to model uncertainty. Our results are threefold: First and foremost, we find that price stability plays a pivotal role as a determinant of exchange rate pressures. More specifically, the currencies of countries that experienced higher inflation prior to the crisis tend to be more affected in times of stress. Second, we investigate potential effects that vary with the level of pre-crisis inflation. In this vein, our results reveal that an increase in domestic savings reduces the severity of pressures in countries that experienced a low-inflation environment prior to the crisis. Finally, we find evidence of the mitigating effects of international reserves on the volatility of exchange rate pressures.