Forecasting Credit Spread Volatility: Evidence from the Japanese Eurobond Market

Different approaches to forecasting the volatility associated with the credit spreads on Yen Eurobonds are investigated. The actual volatility, historical volatility and estimated conditional volatility on spreads derived from a regression-based model with a GARCH and ARMA specification are compared within an adaptation of Black’s (J. Finance, 31, 1976, 361–367) option-pricing model. Surprisingly, the regression forecast over a medium forecasting horizon suggests that historic volatility provides the better forecast. The implications of these results for volatility forecasting and credit spread modelling are also discussed.JEL Classification: C32; G15     

Options Trading Based on the Forecasting of Volatility Direction with the Incorporation of Investor Sentiment

Using options price data on the Taiwanese stock market, we propose an options trading strategy based on the forecasting of volatility direction. The forecasting models are constructed with the incorporation of absolute returns, heterogeneous autoregressive-realized volatility (HAR-RV), and proxy of investor sentiment. After we take into consideration the margin-based transaction costs, the results of our simulated trading indicate that a straddle trading strategy that considers the forecasting of volatility direction with the incorporation of market turnover achieves the best Sharpe ratios. Our trading algorithm bridges the gap between options trading, market volatility, and the information content of investor overreaction.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.     

Forecasting Changes in Copper Futures Volatility with GARCH Models Using an Iterated Algorithm

There is a gap in the literature regarding the out-of-sample forecasting ability of GARCH-type models applied to derivatives. A practitioner-oriented method (iterated cumulative sum of squares) is applied to detecting breakpoints in the variance of two copper futures series. Short-, intermediate-, and long-term out-of-sample forecasts of copper future series are compared to forecasts from a benchmark random walk model for each series. Not only do the GARCH-type models dominate the random walk model, but the relative improvement is fairly consistent across series, forecast horizon, and GARCH-type model. The evidence makes clear that, with few exceptions, the forecast improvement of the GARCH-type models over the RW model lies somewhere between 20–30%. It is particularly true that for the long-term close to close forecasts, there is great coherence among the forecasts. These all fall within a fairly narrow range.     

Discussion of “The Role of Volatility in Forecasting”

Review of Accounting Studies -     

The Predictive Ability of Direct Method Cash Flow Information

This research examines the predictive ability of direct method cash flow information for firms that use the direct method in their cash flow statements. We use cross sectional and pooled time series regressions to predict operating cash flow data and assess relative predictive ability. Principal findings are: (1) past period direct method cash flow data predict future operating cash flow better than indirect method cash flow data; (2) past period direct method gross operating cash flows predict future net operating cash flow better than past period net operating cash flow; (3) measurement error exists in estimates of direct method operating cash flows from other financial statement data; (4) past operating cash flows predict future operating cash flows better than earnings and accruals.     

Estimation and Prediction of a Non-Constant Volatility

In this paper we study volatility functions. Our main assumption is that the volatility is a function of time and is either deterministic, or stochastic but driven by a Brownian motion independent of the stock. Our approach is based on estimation of an unknown function when it is observed in the presence of additive noise. The set up is that the prices are observed over a time interval [0, t], with no observations over (t, T), however there is a value for volatility at T. This value is may be inferred from options, or provided by an expert opinion. We propose a forecasting/interpolating method for such a situation. One of the main technical assumptions is that the volatility is a continuous function, with derivative satisfying some smoothness conditions. Depending on the degree of smoothness there are two estimates, called filters, the first one tracks the unknown volatility function and the second one tracks the volatility function and its derivative. Further, in the proposed model the price of option is given by the Black–Scholes formula with the averaged future volatility. This enables us to compare the implied volatility with the averaged estimated historical volatility. This comparison is done for three companies and has shown that the two estimates of volatility have a weak statistical relation.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

The Dynamics of the S&P 500 Implied Volatility Surface

This empirical study is motivated by the literature on “smile-consistent” arbitrage pricing with stochastic volatility. We investigate the number and shape of shocks that move implied volatility smiles and surfaces by applying Principal Components Analysis. Two components are identified under a variety of criteria. Subsequently, we develop a “Procrustes” type rotation in order to interpret the retained components. The results have implications for both option pricing and hedging and for the economics of option pricing. This revised version was published online in June 2006 with corrections to the Cover Date.     

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.     

Futures Trading Activity and Commodity Cash Price Volatility

Abstract:   This paper examines the lead‐lag relationship between futures trading activity (volume and open interest) and cash price volatility for major agricultural commodities. Granger causality tests and generalized forecast error variance decompositions show that an unexpected increase in futures trading volume unidirectionally causes an increase in cash price volatility for most commodities. Likewise, there is a weak causal feedback between open interest and cash price volatility. These findings are generally consistent with the destabilizing effect of futures trading on agricultural commodity markets.     

Model-free versus Model-based Volatility Prediction

The well-known ARCH/GARCH models for financial time series havebeen criticized of late for their poor performance in volatilityprediction, that is, prediction of squared returns.¹ Focusingon three representative data series, namely a foreign exchangeseries (Yen vs. Dollar), a stock index series (the S&P500index), and a stock price series (IBM), the case is made thatfinancial returns may not possess a finite fourth moment. Takingthis into account, we show how and why ARCH/GARCH models—whenproperly applied and evaluated—actually do have nontrivialpredictive validity for volatility. Furthermore, we show howa simple model-free variation on the ARCH theme can performeven better in that respect. The model-free approach is basedon a novel normalizing and variance–stabilizing transformation(NoVaS, for short) that can be seen as an alternative to parametricmodeling. Properties of this transformation are discussed, andpractical algorithms for optimizing it are given.     

人民币汇率波动与我国通胀、产出波动：一个扩展泰勒曲线的研究

发挥市场供求对汇率的调节作用,增强人民币汇率双向浮动弹性,是当前我国汇率体制改革的主要方向。本文在泰勒曲线的框架下考察人民币汇率波动对我国宏观经济波动和货币政策实施的影响。通过实证研究发现,1994—2006年通货膨胀波动对人民币汇率波动是不敏感的,人民币汇率传递效应不显著,人民币汇率波动对宏观经济波动没有显著的影响;2007年以后人民币汇率波动推动泰勒曲线向内移动,因此更大的人民币汇率弹性对货币政策传导和货币政策有效性是有利的,逐步扩大的人民币汇率弹性区间对我国宏观经济运行是适宜和可接受的。另外人民币汇率波动也使得泰勒曲线更加陡峭,稳定通货膨胀所导致的产出缺口波动减小了,因而更有利于货币政策当局追求一个低而稳定的通胀目标。     

基于人工智能的区域现金需求预测模型研究——以南阳市为例

当前,我国非现金支付对现金支付的替代作用日益显著,但现金支付在居民日常支付中仍占据重要地位。在此背景下,如何精确地预测区域现金需求,保证满足区域现金流通需要已成为一项重要课题。通过借鉴国内外机器学习、神经网络等人工智能技术,基于地区现金投放历史数据,使用岭回归、MLP和LSTM三种模型,构建RR-MLP-LSTM加权组合模型实现了区域现金需求预测,对于统筹货币发行工作、积极防范和化解货币供应风险具有较为重要的借鉴意义。     

Modelling the Implied Volatility of Options on Long Gilt Futures

This paper investigates the properties of implied volatility series calculated from options on Treasury bond futures, traded on LIFFE. We demonstrate that the use of near-maturity at the money options to calculate implied volatilities causes less mis-pricing and is therefore superior to, a weighted average measure encompassing all relevant options. We demonstrate that, whilst a set of macroeconomic variables has some predictive power for implied volatilities, we are not able to earn excess returns by trading on the basis of these predictions once we allow for typical investor transactions costs.     

The Effect of Futures Market Volume on Spot Market Volatility

There has been considerable interest, both academic and regulatory, in the hypothesis that the higher is the volume in the futures market, the greater is the destabilizing effect on the stock market. We show that conventional approaches, such as adding exogenous variables to GARCH models, may lead to false inferences in tests of this question. Using a stochastic volatility model, we show that, contrary to regulatory concern and the results of other papers, contemporaneous informationless futures market trading has no significant effect on spot market volatility.     

Testing for Volatility Jumps in the Stochastic Volatility Process

This paper considers the stochastic volatility process with contemporaneous and correlated jumps in returns and volatility, which was proposed by Eraker, B., Johannes, M. and Poison, N. G. (Journal of Finance 53, 2003, 1269--1300) and proposes the Lagrange multiplier test for the presence of jumps in volatility. The test statistic is derived by regarding the degenerate density of volatility jumps with zero variance under the null as Dirac's delta function. The correlation parameter between jumps, which is a nuisance parameter unidentified under the null, is cancelled out in this test statistic and hence the test is free from the Davies problem (Davies, R. B., Biometrika 64, 1977, 247–254).     

Foreign Exchange Market Volatility in Southeast Asia

The time-varying volatility and volatility transmission in Asian foreign exchange markets are investigated in this paper. It has been found that the time-varying volatility and volatility transmission are all prominent in these markets. Moreover, variance simulation is carried out and the structure of covariance matrices examined, revealing the characteristics of Asian foreign exchange markets and offering explanations to the findings.     

How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes

We propose a discrete-time stochastic volatility model in whichregime switching serves three purposes. First, changes in regimescapture low-frequency variations. Second, they specify intermediate-frequencydynamics usually assigned to smooth autoregressive transitions.Finally, high-frequency switches generate substantial outliers.Thus a single mechanism captures three features that are typicallyviewed as distinct in the literature. Maximum-likelihood estimationis developed and performs well in finite samples. Using exchangerates, we estimate a version of the process with four parametersand more than a thousand states. The multifractal outperformsGARCH, MS-GARCH, and FIGARCH in- and out-of-sample. Considerablegains in forecasting accuracy are obtained at horizons of 10to 50 days.     

Leverage and Volatility Feedback Effects in High-Frequency Data

We examine the relationship between volatility and past andfuture returns using high-frequency aggregate equity index data.Consistent with a prolonged "leverage" effect, we find the correlationsbetween absolute high-frequency returns and current and pasthigh-frequency returns to be significantly negative for severaldays, whereas the reverse cross-correlations are generally negligible.We also find that high-frequency data may be used in more accuratelyassessing volatility asymmetries over longer daily return horizons.Furthermore, our analysis of several popular continuous-timestochastic volatility models clearly points to the importanceof allowing for multiple latent volatility factors for satisfactorilydescribing the observed volatility asymmetries.