Causal relations between inflation and inflation uncertainty—Cross sectional evidence in favour of the Friedman-Ball hypothesis

We test for causality between inflation and its associated uncertainty by means of both in-sample and out-of-sample modelling. Our findings indicate that the impact of inflation on inflation uncertainty is more pronounced than the reverse causal effect.     

Expected inflation, expected stock returns, and money illusion: What can we learn from survey expectations?

We show empirically that survey-based measures of expected inflation are significant and strong predictors of future aggregate stock returns in several industrialized countries both in-sample and out-of-sample. Empirically discriminating between competing sources of this return predictability by virtue of a comprehensive set of expectations data, we find that money illusion seems to be the driving force behind our results. Another popular hypothesis – inflation as a proxy for aggregate risk aversion – is not supported by thedata.     

商品期货有助于预测通货膨胀率吗

本文通过对我国22种商品期货提取隐含的便利收益率时间序列,发现商品期货在样本内和样本外可以显著预测未来通货膨胀率,在控制了利率、货币增速影响后,这一结果仍然成立。进一步,采用南华商品期货综合指数、商品期货各品种指数、商品期货大类指数,以及美国商品研究局商品期货指数对通货膨胀率进行了样本外预测,结果表明这些指数对于预测我国通货膨胀率效果仍然显著。基于商品期货价格的预测模型都优于本文的基准模型和朗润预测指数,这表明商品期货市场包含了与通货膨胀率相关的重要信息,可以作为未来一般物价走势的重要参考。     

Forecasting US real house price returns over 1831–2013: evidence from copula models

Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831–2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1874–2013, based on an in-sample of 1831–1873. Overall, our results provide overwhelming evidence in favour of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t-copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Inflation, price dispersion, and market structure

In this paper, we use a novel data set containing prices from bazaars, convenience stores, and supermarkets in Istanbul to re-examine the relationship between price dispersion and inflation. Although existing evidence is mixed, we find positive and significant relationships between dispersion, on the one hand, and lagged dispersion and unexpected product-specific inflation on the other. We also find evidence that dispersion is initially decreasing in anticipated aggregate inflation but is eventually increasing. Finally, average price duration and dispersion are lowest in the bazaar. This is intuitive, since menu and search costs should be minimal in that market structure.     

Inflation and the Dispersion of Relative Prices: A Case for 4 % Solution

Unlike earlier literature that documented positive association between inflation and the dispersion of relative prices over time, the empirical evidence from this study suggests that the relative price dispersion increases in response to the deviation of inflation from certain threshold/target level in either direction rather than inflation per se. The striking feature of the empirical evidence from United States and Japan is that the inflation rate at which the dispersion of relative prices is minimised turn out to be 4%; hence, supporting the proposal of 4% inflation target for both the countries.     

Detecting and Predicting Forecast Breakdowns

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data-generating process and relate the properties of our forecast breakdown test to those of structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips' curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed.     

On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

A Panel Smooth Transition Approach to Inflation and Relative Price Variability in China

This paper examines the relationship between inflation and relative price variability (RPV) by using provincial level data from China. The data contains three different inflation regimes and evidence of smooth transition is statistically prominent and the province‐specific time‐varying marginal impact of inflation on RPV varies substantially across inflation regimes. The inflation–RPV linkage is stronger when inflation is moderately high, but tends to fade out and might eventually disappear when inflation is steadily low. The policy implication of this result is that inflation targets should be set within a range in which the inflation–RPV link is the weakest (i.e. low‐inflation regime) to minimize undesirable price dispersion induced by inflation.     

Trend inflation estimates for Thailand from disaggregated data

This paper constructs a new trend inflation measure for Thailand based on the multivariate unobserved components model with stochastic volatility and outlier adjustments (MUCSVO) of Stock and Watson (2016). Similar to core inflation, the MUCSVO produces an estimate of trend inflation utilizing information in disaggregated data, but also allows for time-varying weights that depend on the volatility, persistence and comovement of the underlying sectoral inflation series. Based on the empirical results, the majority of sectoral weights show significant time-variation in contrast to their relatively stable expenditure shares. Volatile food and energy sectors that are typically excluded from core inflation measures also turn out to help explain approximately 10 percent of MUCSVO trend inflation rate movements. Compared against other benchmark trend inflation measures, we show that the MUCSVO delivers trend estimates that are smoother, more precise, and are able to forecast average inflation over the 1–3 year horizon more accurately both in-sample and out-of-sample, especially since the year 2000.     

Do jet fuel price movements help forecast airline fares and the demand for air travel?

The paper studies the predictive content of jet fuel prices for the U.S. aviation industry through in-sample and out-of-sample forecasting exercises. Our results suggest the possibility of limited improvements in the predictions of airline fares, and little evidence of predictability from jet fuel prices to measures of air travel demand.     

High-low range in GARCH models of stock return volatility

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a range-GARCH(1,1) model. Our empirical analysis conducted on stocks, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.     

Is Beta-t-EGARCH(1,1) superior to GARCH(1,1)?

Statistical performance, in-sample point forecast precision and out-of-sample density forecast precision of GARCH(1,1) and Beta-t-EGARCH(1,1) models are compared. We study the volatility of nine global industry indices for period from April 2006 to July 2010. Competing models are estimated for periods before, during and after the United States (US) financial crisis of 2008. The results provide evidence of the superior out-of-sample predictive performance of Beta-t-EGARCH compared to GARCH after the US financial crisis.     

Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation

The conduct of inflation targeting is heavily dependent on accurate inflation forecasts. Non-linear models have increasingly featured, along with linear counterparts, in the forecasting literature. In this study, we focus on forecasting South African inflation by means of non-linear models and using a long historical dataset of seasonally adjusted monthly inflation rates spanning from 1921:02 to 2013:01. For an emerging market economy such as South Africa, non-linearities can be a salient feature of such long data, hence the relevance of evaluating non-linear models’ forecast performance. In the same vein, given the fact that 1969:10 marks the beginning of a protracted rising trend in South African inflation data, we estimate the models for an in-sample period of 1921:02–1966:09 and evaluate 1, 4, 12, and 24 step-ahead forecasts over an out-of-sample period of 1966:10–2013:01. In addition, using a weighted loss function specification, we evaluate the forecast performance of different non-linear models across various extreme economic environments and forecast horizons. In general, we find that no competing model consistently and significantly beats the LoLiMoT’s performance in forecasting South African inflation.     

PRICE DISPERSION,INFLATION, AND WELFARE*

We examine the implications of inflation for both price dispersion and welfare in a monetary search economy. In our economy, if the degree of buyers' incomplete information about prices is fixed, both price dispersion and real prices are increasing in inflation. As the inflation rate approaches the Friedman rule, both price dispersion and welfare losses vanish. If households choose the number of prices to observe, then the optimal inflation rate may exceed the Friedman rule as inflation induces search and, up to a point, raises welfare by eroding market power.     

中国粮价与通货膨胀关系(1987-1999)

本研究在收集整理粮食集市价格和消费物价月度数据的基础上,利用均衡修正模型对中国1987-1999年粮价变动与通货膨胀关系进行协整分析.有很强的经验证据,说明通货膨胀与市场粮价存在长期均衡关系.因果关系走向是通货膨胀影响粮价变动,而不是粮价上涨导致通货膨胀.另外,研究还发现,我国长期真实粮价基本不变,说明传统认为粮食相对紧缺度会不断上升的观点缺乏依据.然而,粮价对通货膨胀冲击做出反应时,通常会发生过度或超量调节.     

Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models

Recent empirical evidence suggests that stock market returns are predictable from a variety of financial and macroeconomic variables. However, with two exceptions this predictability is based upon a linear functional form. This paper extends this research by considering whether a nonlinear relationship exists between stock market returns and these conditioning variables, and whether this nonlinearity can be exploited for forecast improvements. General nonlinearities are examined using a nonparametric regression technique, which suggest possible threshold behaviour. This leads to estimation of a smooth-transition threshold type model, with the results indicating an improved in-sample performance and marginally superior out-of-sample forecast results.     

QARMA-Beta-t-EGARCH versus ARMA-GARCH: an application to S&P 500

Statistical performance and out-of-sample forecast precision of ARMA-GARCH and QARMA-Beta-t-EGARCH are compared. We study daily returns on the Standard and Poor’s 500 (S&P 500) index and a random sample of 50 stocks from the S&P 500 for period May 2006 to July 2010. Competing models are estimated for periods before and during the US financial crisis of 2008. Out-of-sample point and density forecasts are performed for periods during and after the US financial crisis. The results provide evidence of the superior in-sample statistical and out-of-sample predictive performance of QARMA-Beta-t-EGARCH.