Asset Price Volatility Research Articles

Climate risk and financial stability in the network of banks and investment funds

We analyze the effects on financial stability of the interplay between climate transition risk and market conditions, such as recovery rate and asset price volatility. To this end, we extend the framework of the climate stress-test of the financial system by including an ex-ante network valuation of financial assets which accounts for asset price volatility as well as for endogenous recovery rate on interbank assets. Moreover, we also consider the dynamics of indirect contagion of banks and investment funds, which are key players in the low carbon transition, via exposures to the same asset classes. We derive some analytical results and we apply the model to a unique supervisory dataset in a range of climate policy scenarios and market conditions. In the event of a disorderly low-carbon transition, stronger market conditions allow to reach more ambitious climate policies at the same level of financial risk.

Valuation of options under a constant elasticity of variance process and stochastic volatility

In this paper, we price European call options under a constant elasticity of variance process for the asset price and stochastic volatility. In particular, we derive an analytic approximation formula in the form of asymptotic expansions, which is valid for European options with short times to expiry. Further, we examine the performance of our formula on a market sample of short-tenor crude oil call options (traded on the International Commodities Exchange) and find that the formula provides an excellent fit to market prices.

Decoupling the Short- and Long-Term Behavior of Stochastic Volatility

AbstractWe introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrelations, including proper long memory, which are two stylized facts often found in volatility data. Our prime model is based on the so-called Brownian semistationary process and we derive a number of theoretical properties of this process, relevant to volatility modeling. Applying the models to realized volatility measures covering a vast panel of assets, we find evidence consistent with the hypothesis that time series of realized measures of volatility are both rough and very persistent. Lastly, we illustrate the utility of the models in an extensive forecasting study; we find that the models proposed in this article outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.

The effects of asset price volatility on market participation: Evidence from the Thai foreign exchange market

Existing models of market participation offer contrasting predictions on the impact of asset price volatility on market participation. Utilising granular trading data from the Thai foreign exchange (FX) market, we test the empirical relevance of these predictions. We find that the volatility of the US dollar–Thai baht exchange rate has a positive effect on market participation measured by trading volume and average transaction size. This finding is consistent with the models illustrating that volatility increases participation as it creates profit-making opportunities. The result is robust to controlling for information flow that may generate a positive but non-causal relationship between volatility and participation. We also find heterogeneity across participant types. In particular, the impact of FX volatility on trading volume is positive for foreign end-customers and interbank players, but negative for local end-customers. This heterogeneity is explained by different purposes of FX transactions: financial returns for the former and real demand for the latter. Finally, we show that the impact of volatility on trading volume turns negative at high levels of volatility and during a period of high regulatory uncertainty.

Divergent risk-attitudes and endogenous collateral constraints

Financial crises are anticipated by leverage build-up and asset price booms and followed by sharp de-leveraging and asset price burst. Leverage pro-cyclicality, debt margins counter-cyclicality and heightened asset price volatility are often hard to reconcile with credit frictions models, with and without occasionally binding constraints. We show that a model in which the anticipatory effects of occasionally binding collateral constraints interact with borrowers' time-varying risk-attitudes (modeled through gain-loss reference dependent utilities) and with borrowers/lenders risk-attitudes heterogeneity can explain those facts. Simulations through global methods show that the model can also match numerous statistics characterizing the asset price and leverage cycles.

Delta Hedging of Derivatives using Deep Reinforcement Learning

Building on previous work of Kolm and Ritter (2019) and Cao et al. (2019), this paper explores the novel application of Deep Reinforcement Learning for Delta Hedging of options in an utility based framework where an agent is faced with a trade-off between hedging error and transaction costs while aiming at maximizing the expected profit and loss and minimizing its variance. In the presence of transaction costs we compare the performance of two state-of-the-art Reinforcement Learning algorithms with two simple benchmark strategies widely used in practice. We perform the analysis on synthetic data for different market characteristics, transaction costs, option maturities and hedging frequencies, and find that the agents deliver a strong performance in markets characterized by stochastic volatility and jumps in asset prices, as well as for high transaction costs, high hedging frequency and for options with long maturities. Furthermore, we apply trained algorithms to similar (but not seen before) options and present a way of improving the robustness of the algorithms to different levels of volatility. Finally, we transfer the hedging strategies learned on simulated data to empirical option data on the S&P500 index, and demonstrate that transfer learning is successful: hedge costs encountered by reinforced learning decrease by as much as 30% compared to the Black- Scholes hedging strategy. Our results indicate that the hedging strategies based on Reinforcement Learning outperform the benchmark strategies and are suitable for traders taking real-life hedging decisions, even when the networks are trained on synthetic (but versatile) data.

Balance Sheet Channel with Information-Trading Frictions in Secondary Markets

AbstractThis article develops a theory of the balance sheet channel that places a central emphasis on the liquidity of secondary markets for macro-contingent claims. We show that the presence of dispersed information and imperfect competition in secondary markets, interacted with financial constraints, results in mispricing and misallocation of aggregate risk, distorts aggregate investment, and exacerbates asset price and output volatility. The magnitude of balance sheet amplification effects becomes endogenously tied to the severity of market frictions, which likely vary over time and across economies. The laissez-faire equilibrium is constrained inefficient due to a novel externality originating from rent-extracting behaviour of agents in secondary markets. Optimal corrective policy boosts secondary market liquidity through subsidies to trade in macro-contingent claims, which enhances aggregate risk-sharing and stabilizes the business cycle.

Temporal Aggregation and Long Memory for Asset Price Volatility

The effects of temporal aggregation and choice of sampling frequency are of great interest in modeling the dynamics of asset price volatility. We show how the squared low-frequency returns can be expressed in terms of the temporal aggregation of a high-frequency series. Based on the theory of temporal aggregation, we provide the link between the spectral density function of the squared low-frequency returns and that of the squared high-frequency returns. Furthermore, we analyze the properties of the spectral density function of realized volatility series, constructed from squared returns with different frequencies under temporal aggregation. Our theoretical results allow us to explain some findings reported recently and uncover new features of volatility in financial market indices. The theoretical findings are illustrated via the analysis of both low-frequency daily Standard and Poor’s 500 (S&P 500) returns from 1928 to 2011 and high-frequency 1-min S&P 500 returns from 1986 to 2007.

ECB'S non‐standard monetary policy and asset price volatility: Evidence from EU‐6 economies

AbstractIn this paper, we provide evidence that the effects of the different waves of asset purchase programmes implemented by the ECB from 2009 onwards have spilled over into a group of six Central and Eastern European economies belonging to the EU but not to the euro area, contributing to shield their financial markets from the negative shocks that hit international investors' degree of risk aversion in recent years. By means of a Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticity Model, and resorting to three different proxies to describe the functioning, and measure the impact, of the ECB's programmes of asset purchase, we show that such non‐standard monetary measures have had a marked role in dampening volatility spikes in EU‐6 financial markets, likely reflecting the working of a “risk taking” and a “liquidity” channel of transmission. Results are in general robust to an extensive series of tests, including changes in the estimation methodology.

Testing risk proxies for financial collateral haircuts: adequacy of capturing tail risk

PurposeThe use of risk proxies in internal models remains a popular modelling solution. However, there is some risk that a proxy may not constitute an adequate representation of the underlying asset in terms of capturing tail risk. Therefore, using empirical examples for the financial collateral haircut model, this paper aims to critically review available statistical tools for measuring the adequacy of capturing tail risk by proxies used in the internal risk models of banks. In doing so, this paper advises on the most appropriate solutions for validating risk proxies.Design/methodology/approachThis paper reviews statistical tools used to validate if the equity index/fund benchmark are proxies that adequately represent tail risk in the returns on an individual asset (equity/fund). The following statistical tools for comparing return distributions of the proxies and the portfolio items are discussed: the two-sample Kolmogorov–Smirnov test, the spillover test and the Harrell’s C test.FindingsUpon the empirical review of the available statistical tools, this paper suggests using the two-sample Kolmogorov–Smirnov test to validate the adequacy of capturing tail risk by the assigned proxy and the Harrell’s C test to capture the discriminatory power of the proxy-based collateral haircuts models. This paper also suggests a tool that compares the reactions of risk proxies to tail events to verify possible underestimation of risk in times of significant stress.Originality/valueThe current regulations require banks to prove that the modelled proxies are representative of the real price observations without underestimation of tail risk and asset price volatility. This paper shows how to validate proxy-based financial collateral haircuts models.

Natural gas price, market fundamentals and hedging effectiveness

How to effectively manage risk is an important issue that the financial and commodity industries face. One of the issues is the estimation of the financial and consumption asset price volatility and estimation of the optimal hedge ratio. Our study examines whether it is important to incorporate fundamental variables in estimating price returns and volatilities by studying the U.S. natural gas market. In doing so, we explain the spot and futures returns and volatilities based on market fundamental variables such as weather, gas underground storage, oil price and macroeconomic news. We find significant impacts of most of these variables on gas price. In addition, we calculate the optimal hedge ratio based on the price and volatility estimations. Our empirical evidence suggests that, as expected, the optimal hedge ratio was not constant but fluctuated significantly during the sample period. Incorporating time-varying hedge ratio has improved hedging effectiveness by a large percentage. In addition, incorporating market fundamental variables further improves the hedging effectiveness significantly. Our empirical results support the proposition that it is important to incorporate fundamental market variables in analyzing commodity price movement and improving hedging effectiveness.

Macroeconomic news and treasury futures return volatility: Do treasury auctions matter?

Various macroeconomic announcements are known to influence asset price volatility. In addition to non-farm payrolls, we highlight the importance of Treasury auctions – a news event that has grown in importance due to ongoing Federal deficits. The occurrence of an auction, which increases supply in the underlying cash market, pushes futures prices lower and volatility higher. Conversely, a higher bid-to-cover ratio, indicates greater demand for Treasury securities, increases Treasury futures prices and lowers volatility. The response is consistent with market participants using futures to manage inventory risk. The results are consistent across a set of volatility estimates, and in an alternate conditional volatility framework.

Asset Price Volatility and Investment Horizons: An Experimental Investigation

We study the effects of the investment horizon on asset price volatility using a Learning to Forecast experiment. We end that, for short investment horizons, participants coordinate on self-fulfilling trend extrapolating predictions. Price deviations are then reinforced and amplified, possibly leading to large bubbles and crashes in asset prices. For longer investment horizons such bubbles do not emerge and price volatility tends to be lower. This is due to the fact that, for longer horizons, there is more dispersion in participants' forecasts, and participants extrapolate trends in past prices to a lesser extent. We also show that, independent of the investment horizon, if the initial history of asset prices is already relatively stable before participants start their prediction task, price volatility remains small, with prices close to their fundamental values for the duration of the experiment.

Is idiosyncratic risk ignored in asset pricing: Sri Lankan evidence?

The present study focused on one of the important South Asian nations—Sri Lanka—to examine the role of idiosyncratic volatility in asset prices. A four-factor model with idiosyncratic volatility was designed for capturing the market, size, value and idiosyncratic risk yields better than Fama and French’s (J Financ Econ 33:3–56, 1993) three-factor model and performance of the model. Fama–MacBeth’s cross-sectional regression, residual graphs and GRS test all confirm the superiority of four-factor model over 2 three-factor models. For all MC- and IVOL-based portfolios, idiosyncratic volatility is negatively related to the expected returns and positively related for all PB-based portfolios. Finally, study findings confirm that there is a high importance for idiosyncratic volatility risk factor while considering investment decision in Colombo stock exchange. Hence, investor should compensate for holding such risk factors in the portfolio.

Coherent-Price Systems and Uncertainty-Neutral Valuation

This paper considers fundamental questions of arbitrage pricing that arises when the uncertainty model incorporates ambiguity about risk. This additional ambiguity motivates a new principle of risk- and ambiguity-neutral valuation as an extension of the paper by Ross (1976) (Ross, Stephen A. 1976. The arbitrage theory of capital asset pricing. Journal of Economic Theory 13: 341–60). In the spirit of Harrison and Kreps (1979) (Harrison, J. Michael, and David M. Kreps. 1979. Martingales and arbitrage in multiperiod securities markets. Journal of Economic Theory 20: 381–408), the paper establishes a micro-economic foundation of viability in which ambiguity-neutrality imposes a fair-pricing principle via symmetric multiple prior martingales. The resulting equivalent symmetric martingale measure set exists if the uncertain volatility in asset prices is driven by an ambiguous Brownian motion.

금융위기, 자산가격 변동성 및 구조적 변화

금융위기, 자산가격 변동성 및 구조적 변화

Watch Your Neighbor: A Volatility Spillover in ASEAN-5 Stock Exchange

In the process of financial markets integration, this research was conducted to investigate the phenomenon of the transmission of stock return volatility among stock market in five ASEAN countries. Those were Indonesia, Singapore, Malaysia, Philippines, and Thailand. The research was important because when the interdependence of financial markets had increased, changes in asset prices in the market were not only influenced by the shock in the market but also by its response to asset price volatility that occurred in other countries. Information about the volatility spillover between markets was important for investors to the portfolio selection process. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) method was used on a daily time series stock return data obtained by accessing www.bloomberg.com. The result indicates that the shock in Singapore, Malaysia, Thailand, and the Philippines stock market will be transmitted to the Indonesia Stock Market with an asymmetric pattern. It has increased intensity after the implementation of the ASEAN Economic Community in December 2015.

The nonlinear characteristics of Chinese stock index futures yield volatility

PurposeThe purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.Design/methodology/approachThis paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.FindingsThis paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.Research limitations/implicationsThis paper has research limitations in variable measurement and data selection.Practical implicationsThis study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.Originality/valueThe existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

Management strategies for a defined contribution pension fund under the hybrid stochastic volatility model

In this paper, we introduce the Heston–Hull–White (the hybrid) model in the pension fund management. The optimal investment and benefit payments policies for the DC pension fund with an income drawdown option are presented explicitly. In this study, the pension fund manager is allowed to invest the fund wealth in riskless and risky assets. The risk asset price dynamics evolve according to the Hybrid Stochastic Volatility (Heston–Hull–White) model. The goal of the pension fund manager is to maintain the standard of lives for the pension fund members after retirement. We derived the HJB equation and established the closed-form solution for the optimal control policies using stochastic dynamic programming principle. To illustrate the economic implications of the model, we provide a numerical example and simulations. The results reveal that the optimal investment and benefit payments strategies convergence successful with time t. The risk asset price volatility and interest rate mean reversion are inversely proportional to investment policy. The interest rate volatility and risk preference are both directly proportional to investment and benefit payments. The pension fund managers are advised to control the investment and the benefit payments policies to achieve the goals of the pension fund members.

Stochastic asset price dynamics and volatility using a symmetric supply and demand price equation

A symmetric supply/demand model of price dynamics is developed and used to understand the relationship between price change and volatility. This differs from the classical approach in which the expected rate of price change and variance are assumed to be independent. The microeconomic and stochastic analysis leads to the conclusion that a particular measure of the marginal volatility has a minimum shortly before the expected log-price has an extremum. The maximum of the volatility occurs when prices are likely to change most rapidly, and the supply/demand imbalance is greatest. The great bubble and collapse of Bitcoin’s price serves as a test of this analysis. The volatility reached a minimum shortly prior to the peak of Bitcoin’s price in December 2018. The model is further developed under the assumption that supply and demand depend on the fundamental value of the asset. Thus the paper is a key step in understanding the issue of whether volatility highs and lows can forecast trading price tops and bottoms. The methodology can be extended beyond log-normal returns and is further compared with an empirical study of 40 sharp market boom/bust events studied by Sornette et. al. (2017).