Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

On Modelling Speculative Prices: The Empirical Literature

Traditionally, financial theory and in particular asset pricing models have assumed (implicitly or explicitly) a certain probabilistic structure for speculative prices. The probabilistic structure is usually defined in terms of specific statistical models and relates to the dependence, heterogeneity and the distribution of such prices. The primary objective of this paper is to trace the development of various statistical models proposed since Bachelier (1900), in an attempt to assess how well these models capture the empirical regularities exhibited by data on speculative prices.     

A Socioeconomic Perspective

By combining an economic perspective on financial instability with a sociological analysis of collective behavior, this paper lays out a conceptual foundation that is suggestive of a fuller understanding of speculative manias and financial panics in highly liquid financial assets. The paper argues that a collective enthusiasm develops in an environment of "optimistic uncertainty" created by the gradual diffusion of a revolutionary innovation of the type examined by Rosenberg. An adaptation of Simmel's theory of fashion in the emerging norm framework of Turner and Killian explains the swell of speculative excitement. Once started, the enthusiasm continues in a Schumpeterian manner until the objective potential of the innovation becomes apparent and estimable. If the contemporaneous degree of optimism proves unfounded and prices too high to sustain, a reversal of fortune ushers in distress that may inspire panic. A panic, contrary to implications of extant economic theory, does not necessarily follow a period of extreme speculative enthusiasm. This paper suggests instead, in light of Lang and Lang's analysis of collective dynamics, that certain objective market conditions create a panic in a distressed environment.     

A new look at the relationship between time-series and structural econometric models

This paper examines the relationship between dynamic structural econometric models (SEM) and time series (TS) models. It extends the work of others by suggesting a reconciliation of SEM and TS models based on classical linear parameter restrictions in regression models rather than on time series methods. The paper demonstrates that in a number of common economic contexts there exist sets of plausible restrictions on the stochastic properties of the disturbances and on the dynamic adjustment processes in a SEM such that familiar structural models take on the form of univariate TS models. Consequently, it is argued that TS models should not be arbitrarily dismissed as being devoid of economic content.     

Stock Market Response to the Financial Regulations in the Mortgage‐Lending Market in Korea

This study investigates the economic impact of the financial regulations that aimed to control the housing market in Korea during the reign of late President Ro's Administration, which had diligently fought against the then speculative bubble in the Korean real‐estate market. We test for the validity of the general prediction that the financial regulations in the form of the loan‐to‐value (LTV) and debt‐to‐income (DTI) restrictions would have adverse impacts on the value of the firms operating in the mortgage‐lending industry. In this event study, we select two critical days as event dates and check whether the stock prices of the financial firms react negatively to the announcements of the regulations. Overall, the initial imposition of the DTI restrictions (i.e., the first event) adversely affects those banks that possess a relatively large number of mortgage loans in their asset portfolio. By contrast, banks that hold a small number of mortgage loans appear to benefit from the risk‐reducing effect of the DTI regulation. Subsequently, the reinforcement of the LTV and DTI rules (i.e., the second event) has negative impacts on the banks with large mortgage loans. The degree of this adverse effect is greater in the second event than in the first event (i.e., the DTI restrictions). The reinforced regulations also unfavorably affect the savings banks with large mortgage loans but to a lesser degree compared with their counterparts in the banks. Meanwhile, the reinforcement of the financial regulations has negligible impacts on the banks and the savings banks with smaller mortgage loans.     

An assessment of another decade of capital controls in Colombia: 1998–2008

We explore the effectiveness of capital controls in Colombia. We analyze the impact of administrative restrictions to capital flows on aggregate capital flows, the composition of capital flows, the real exchange rate, and economic activity using restricted versions of vector error correction models (VEC) that control for exogenous global financial conditions. The models are estimated using monthly data ranging from August of 1998 to May of 2008. In addition we estimate GARCH models to identify if capital controls have had relevant impacts on the volatility of the nominal exchange rate and of other relevant asset prices. These models are estimated using weekly data covering the same time period. Results suggest that the capital controls used since 1998 have been ineffective in reducing capital flows and the trend of the Colombian peso to appreciate. In addition there is no evidence suggesting a change in the composition of capital flows induced by capital controls. We find some evidence in favor of capital controls reducing nominal exchange rate volatility at high frequencies.     

User costs and bubbles in land markets

In asset markets with speculative behavior, the long-run equilibrium relationship between asset prices and the discounted flow of future rents may become invalid. We distinguish short-term user costs and longer-term user costs with variables that reflect fundamentals. We show how to work around the empirical problem of measuring speculative expectations about asset price changes and derive a simple user cost formula where the asset price change in the short-term relationship comes out as the long-run change in the overall price level.     

GMM Estimation with Non‐causal Instruments*

This note provides a warning against careless use of the generalized method of moments (GMM) with time series data. We show that if time series follow non‐causal autoregressive processes, their lags are not valid instruments, and the GMM estimator is inconsistent. Moreover, endogeneity of the instruments may not be revealed by the J‐test of overidentifying restrictions that may be inconsistent and has, in general, low finite‐sample power. Our explicit results pertain to a simple linear regression, but they can easily be generalized. Our empirical results indicate that non‐causality is quite common among economic variables, making these problems highly relevant.     

Price-wage dynamics in a transition economy: The case of Poland

In this paper we analyse the wage-price relationship of an economy in transition characterized by important structural changes. It is known (see Perron, 1989) that structural breaks in stationary time series can induce apparent unit roots. The stationarity analysis of the series employed in the present model is conducted jointly with the assumption that the breakpoint location is unknown. We follow a testing procedure recently proposed by Zivot and Andrews (1992). Cointegration analysis of wages and prices in the presence of structural breaks finds empirical evidence in favour of two cointegrating vectors involving prices and wages. Our analysis focuses on the different structural behaviour of the price-wage dynamic relationship in the short and long term; we also demonstrate the relative importance of import prices as a source of wage-price fluctuations.     

Applications of the Likelihood Theory in Finance: Modelling and Pricing

This paper discusses the connection between mathematical finance and statistical modelling which turns out to be more than a formal mathematical correspondence. We like to figure out how common results and notions in statistics and their meaning can be translated to the world of mathematical finance and vice versa. A lot of similarities can be expressed in terms of LeCam’s theory for statistical experiments which is the theory of the behaviour of likelihood processes. For positive prices the arbitrage free financial assets fit into statistical experiments. It is shown that they are given by filtered likelihood ratio processes. From the statistical point of view, martingale measures, completeness, and pricing formulas are revisited. The pricing formulas for various options are connected with the power functions of tests. For instance the Black–Scholes price of a European option is related to Neyman–Pearson tests and it has an interpretation as Bayes risk. Under contiguity the convergence of financial experiments and option prices are obtained. In particular, the approximation of Itô type price processes by discrete models and the convergence of associated option prices is studied. The result relies on the central limit theorem for statistical experiments, which is well known in statistics in connection with local asymptotic normal (LAN) families. As application certain continuous time option prices can be approximated by related discrete time pricing formulas.     

An options pricing approach to ramping rate restrictions at hydro power plants

This paper investigates the impact of ramping rate restrictions imposed on hydro operations to protect aquatic ecosystems. The optimal ramping decision is specified as an optimal control problem which results in a Hamilton Jacobi Bellman (HJB) equation. Electricity prices are modelled as a regime switching stochastic process. The optimal control is determined by solving the HJB equation numerically using a fully implicit finite difference approach with semi-Lagrangian time stepping. The paper focuses on the effect of ramping restrictions on a hydro plant׳s value and optimal operations, and provides an analysis of which factors cause ramping restrictions to have a greater or lesser impact on profitability. It is shown that hydro plant value is negatively affected by ramping restrictions, but the extent of the impact depends on key parameters which determine the desirability of frequent changes in water release rates. Interestingly for the case considered, value is not sensitive to ramping restrictions over a large range of restrictions. The results point to the importance of accurately modelling electricity prices in gauging the trade offs involved in imposing restrictions on hydro operators which may hinder their ability to respond to volatile electricity prices and meet peak demands.     

STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG‐RUN RESTRICTIONS VIA HETEROSKEDASTICITY

Long‐run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just‐identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity models. Using changes in volatility for checking long‐run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.     

Detecting speculative bubbles in an IT-intensive stock market

Using a battery of unit root test procedures and cointegration analysis with alternative null hypotheses we find some evidence of speculative bubbles in the Finnish stock market for monthly data on industry portfolio stock prices and returns from the 1990s. When analyzing the time series behavior of stock market prices and returns against the development of certain macroeconomic fundamentals, the bubbles seem to be present especially in the information technology (IT) prices and only during the latter half of the decade. (JEL C22, G12)     

The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models vs. Simple Linear Econometric Models*

In this paper we construct output gap and inflation predictions using a variety of dynamic stochastic general equilibrium (DSGE) sticky price models. Predictive density accuracy tests related to the test discussed in Corradi and Swanson [Journal of Econometrics (2005a), forthcoming] as well as predictive accuracy tests due to Diebold and Mariano [Journal of Business and Economic Statistics (1995) , Vol. 13, pp. 253–263]; and West [Econometrica (1996) , Vol. 64, pp. 1067–1084] are used to compare the alternative models. A number of simple time‐series prediction models (such as autoregressive and vector autoregressive (VAR) models) are additionally used as strawman models. Given that DSGE model restrictions are routinely nested within VAR models, the addition of our strawman models allows us to indirectly assess the usefulness of imposing theoretical restrictions implied by DSGE models on unrestricted econometric models. With respect to predictive density evaluation, our results suggest that the standard sticky price model discussed in Calvo [Journal of Monetary Economics (1983), Vol. XII, pp. 383–398] is not outperformed by the same model augmented either with information or indexation, when used to predict the output gap. On the other hand, there are clear gains to using the more recent models when predicting inflation. Results based on mean square forecast error analysis are less clear‐cut, although the standard sticky price model fares best at our longest forecast horizon of 3 years, it performs relatively poorly at shorter horizons. When the strawman time‐series models are added to the picture, we find that the DSGE models still fare very well, often outperforming our forecast competitions, suggesting that theoretical macroeconomic restrictions yield useful additional information for forming macroeconomic forecasts.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Causality and the independence phenomenon: The case of the demand for money

We examine the demand for money using causality results with data from two alternative policy regimes. For Spanish series of money and prices we obtain the same result of independence that Feige and others found with U.S. data. The result of the test for the German hyperinflation period reveals bidirectional causality. It is shown that the somehow striking results of widespread independence among economic time series do not disprove but rather confirm the existence of a true underlying causal relationship. Causality results, and independence in particular, give us testable restriction for the structural form. In the case of models for expectations in the rate of inflation, these restrictions allow us to revalidate the stability of the demand for money as postulated by the Quantity Theory.     

Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness

This study assesses systemic risk inherent in credit default swap (CDS) indices using empirical and statistical analyses. We define systemic risk in two perspectives: the possibilities of simultaneous and contagious defaults, and then quantify them separately across benchmark models. To do so, we employ a Marshall-Olkin copula model to measure simultaneous default risk, and an interacting intensity-based model to capture contagious default risk. For an empirical test, we collect daily data for the iTraxx Europe CDS index and its tranche prices in the period from 2005 to 2014, and calibrate model parameters varying across time. In addition, we select forecasting models that have minimal prediction errors for the calibrated time series. Finally, we identify significant changes in each dynamic of systemic risk indicator before and after default and downgrade-related episodes that have occurred in the global financial crisis and European sovereign debt crisis.     

Declining prices and optimality when costs follow an experience curve

There is a widely held notion that as production costs decline with experience, prices should follow more or less in parallel. Although this notion has been an important factor in estimating experience curves and in formulating corporate strategies, we show that it is generally inconsistent with published multiperiod optimal pricing models. The paper then goes on to demonstrate that this apparent conflict can be reconciled by allowing the demand process to exhibit increasing price elasticity over time. Since this demand characteristic is very plausible for innovative products, we suggest it be incorporated into optimal pricing models.     

Information from successive changes in gold and silver prices during phases of the business cycles

This paper concerns the behavior of gold and silver prices on a daily, weekly and monthly time span during January 1970 to December 1989. The methodology consists of extracting the predictive power of time series of changes in past prices for obtaining optimal forecasts for next-period changes in prices. Optimizations are made in the context of information theory via minimizing the degree of diversity between the actual and predicted changes in prices. This methodology has merit in that it does not rest on, generally speaking, unacceptable assumptions regarding the shape of the distribution, stationarity of variance or its existence. The behavior of gold and silver prices are studied during peak to trough and trough to peak of the business cycles over 1970–1989. It is generally shown that information contained in past prices of gold and silver does not allow one to predict next-period changes in prices in the short run. However, longer-term predictions are possible. This study further reveals that as the length of the time interval expands, gold prices exhibit a higher degree of dependency on past prices than silver.     

Testing multivariate distributions in GARCH models

In this paper, we consider testing distributional assumptions in multivariate GARCH models based on empirical processes. Using the fact that joint distribution carries the same amount of information as the marginal together with conditional distributions, we first transform the multivariate data into univariate independent data based on the marginal and conditional cumulative distribution functions. We then apply the Khmaladze's martingale transformation (K-transformation) to the empirical process in the presence of estimated parameters. The K-transformation eliminates the effect of parameter estimation, allowing a distribution-free test statistic to be constructed. We show that the K-transformation takes a very simple form for testing multivariate normal and multivariate t-distributions. The procedure is applied to a multivariate financial time series data set.